OPTIMIZED METHOD OF SAMPLING THE ZONES

Description

TECHNICAL FIELD

The disclosure generally relates to the field of well testing, and more specifically, to the optimization of well sampling operations.

BACKGROUND

Well control measures and running tough logging condition (TLC) systems on deep gas wells may prove challenging and potentially costly for sampling operations. Lost rig time may accumulate over hundreds of thousands of dollars in daily losses for a company/operator. In high pressure, high temperature (HPHT) wells, TLC systems may not be an option, leaving wireline tools as one of the only technically-feasible and cost effective options for well testing. A drill stem test (DST) may be technically feasible, but a DST may cost millions of dollars to perform.

In some geographic areas, such as the Red Sea, deep gas wells may be drilled into formations exceeding 400° F., which may further complicate the sampling techniques to be used. Fluid samples may be required for booking reserves, and several zones of the well may be sampled.

Zones are typically sampled via wireline in a sequence-either from top to bottom, or bottom to top. However, if a risk is encountered during sampling, the wireline tool may be pulled from the well, and a second run may be carried out.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of the disclosure may be better understood by referencing the accompanying drawings.

FIG. 1 is an illustration depicting an example wireline system, according to some implementations.

FIG. 2 is an illustration depicting an example computer, according to some implementations.

FIG. 3 is an example plot depicting pit gain vs. time during a sampling operation, according to some implementations.

FIG. 4 is an illustration depicting example sampling orders, according to some implementations.

FIG. 5 is a first flowchart depicting example operations for determining an optimized sampling sequence, according to some implementations.

FIG. 6 is a second flowchart depicting example operations for determining an optimized sampling sequence, according to some implementations.

FIG. 7 is a third flowchart depicting example operations for determining an optimized sampling sequence, according to some implementations.

FIG. 8 is a plot depicting pressure history vs. time of one or more bubbles in the wellbore, according to some implementations.

FIG. 9 is a plot depicting time vs. depth of one or more bubbles in the wellbore, according to some implementations.

FIG. 10 is a flowchart depicting an example method of operations, according to some implementations.

FIGS. 1-10 and the operations described herein are examples meant to aid in understanding example implementations and should not be used to limit the potential implementations or limit the scope of the claims. None of the implementations described herein may be performed exclusively in the human mind nor exclusively using pencil and paper. None of the implementations described herein may be performed without computerized components such as those described herein. Some implementations may perform additional operations, fewer operations, operations in parallel or in a different order, and some operations differently.

The description that follows includes example systems, methods, techniques, and program flows that embody implementations of the disclosure. However, it is understood that this disclosure may be practiced without these specific details. In other instances, well-known instruction instances, protocols, structures, and techniques have not been shown in detail in order not to obfuscate the description.

Implementations of the machine-readable media, systems, and/or methods as described herein provide one or more improvements in the existing technology in the fields of well control and well logging. For example, the various implementations as described herein provide improvement(s) to existing technological processes by maximizing the number of zones which may be sampled during a single trip of a wireline or similar logging tool during a sampling operation by altering an order in which the zones are sampled. Traditional sampling operations may sample formations in a top-down or bottom-up approach, and well control issues that arise may cause each sampling operation to take place over multiple trips of a logging tool. Various implementations as described herein also provide an improvement to the functioning of a computer by providing an algorithm in which, when implemented by the computer, may allow the computer to select an optimized sampling sequence from a plurality of simulated sampling sequences.

Overview

Rather than sampling a plurality of subsurface zones with a wireline tool via a simple top-down or bottom-up approach, a technique for sampling zones in a well (particularly deep gas wells) may utilize an objective function to maximize the number of zones sampled on an initial run of the wireline tool without jeopardizing the integrity of the well. A sampling optimizer, for example, may include an objective function which may be constrained by the wellbore pressure at the zones and a maximum trip speed of the wireline tool. The objective function may output an optimum fluid influx into the wellbore at each zone, a number of zones to be sampled, and the order in which the zones are to be sampled. The objective function may allow for the ability to take samples from formation zones out of order when compared to traditional approaches. This optimized technique may result in reduced downtime and cost savings on an order of millions of dollars across multiple assets.

The sampling optimizer may aid in maintaining safe operation during sampling of one or more subsurface formation zones. The sampling optimizer may, for example, alter a sampling order of the zones to avoid an underbalanced well scenario. The sampling optimizer may also prioritize the ability to swiftly pull the wireline at an accelerated rate and run pipe quickly to the bottom of the well. This proactive approach may facilitate effective well control procedures, mitigating any potential challenges or complications that may arise.

Example Wireline System

FIG. 1 is an illustration depicting an example wireline system, according to some implementations. A wireline system 100 may be used in an illustrative logging environment with a drill string removed, in accordance with some implementations of the present disclosure. A computer system 150 may include a sampling optimizer 210 (described with additional detail in FIG. 2).

Subterranean operations may be conducted using the wireline system 100 once a drill string has been removed from a wellbore 110, though, at times, some or all of the drill string may remain in the wellbore 110 during logging with the wireline system 100. The wireline system 100 may include one or more logging tools 108 that may be suspended in the wellbore 110 by a conveyance apparatus 106 (e.g., a cable, slickline, or coiled tubing). The conveyance apparatus 106 may include any hardware suitable to lower the logging tool 108 to a target depth. The logging tool 108 may be communicatively coupled to the conveyance apparatus 106. The conveyance apparatus 106 may include conductors for transporting power to the wireline system 100 and telemetry from the logging tool 108 to a logging facility 101. The logging facility 101 may include the computer system 150, the computer system 150 capable of optimizing a sampling sequence to sample a plurality of formation zones including zones 112, 114, and 116 as described herein (e.g., with respect to FIGS. 2-7). Alternatively, the conveyance apparatus 106 may lack a conductor, as is often the case using slickline or coiled tubing, and the wireline system 100 may contain a control unit 102 that contains memory, one or more batteries, and/or one or more processors for performing operations and storing measurements.

In some implementations, the control unit 102 may be positioned at the surface, in the wellbore (e.g., in the conveyance apparatus 106 and/or as part of the logging tool 108) or both (e.g., a portion of the processing may occur downhole, and a portion may occur at the surface). The control unit 102 may include a control system or a control algorithm. In some implementations, a control system, an algorithm, or a set of machine-readable instructions may cause the control unit 102 to generate and provide an input signal to one or more elements of the logging tool 108, such as the sensors along the logging tool 108. The input signal may cause the sensors to be active or to output signals indicative of sensed properties. The logging facility 101, while depicted as a vehicle/mobile configuration in FIG. 1, may include any other suitable structure. The logging facility 101 may collect measurements from the logging tool 108, and may include computing facilities for controlling, processing, or storing the measurements gathered by the logging tool 108. The computing facilities may be communicatively coupled to the logging tool 108 by way of the conveyance apparatus 106 and may operate similarly to the control unit 102.

While depicted as an onshore well, the wireline system 100 may also be implemented in a wellbore 110 in an offshore, subsurface, or subsea well. This well configuration may include a riser comprising a conduit extending from the seafloor to an offshore rig from which the logging tool 108 extends from. The riser may therefore extend the surface of the well (located at the seafloor) to the offshore rig, platform, etc.

Sampling operations of the zones 112-116 may implement a meticulous approach to ensure safe operation and prevent any adverse incidents. Specifically, a sampling procedure of at least the zones 112-116 may be executed in a manner that avoids sudden influxes in a riser condition of the riser. The riser condition may induce rapid expansion of gases as they approach the surface of the well. This may lead to the well becoming unbalanced within a short time frame. Such a scenario may pose a dual risk, as it could result in the well entering a kick condition, and ultimately, a blowout. The sampling optimizer 210 may be configured to optimize the sampling process of the zones 112-116 to avoid such an incident.

Example Computer

FIG. 2 is an illustration depicting an example computer 200, according to some implementations. The computer 200 may include a processor 201 (possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.). The computer system may include memory 207. The memory 207 may be system memory or any one or more of the above already described possible realizations of machine-readable media. The computer system may also include a bus 203 and a network interface 205. The system may communicate via transmissions to and/or from remote devices via the network interface 205 in accordance with a network protocol corresponding to the type of network interface, whether wired or wireless and depending upon the carrying medium. In addition, a communication or transmission may involve other layers of a communication protocol and or communication protocol suites (e.g., transmission control protocol, Internet Protocol, user datagram protocol, virtual private network protocols, etc.).

The system may implement a sampling optimizer 210 in hardware, software, and/or other logic configured to perform the operations described herein. In some implementations, the sampling optimizer 210 may be implemented as instructions executable on the processor 201. The sampling optimizer 210 may include computerized functionality configured to optimize an order of a wellbore sampling operation. For example, a wellbore sampling operation may include any operation to sample a formation fluid, formation rock, formation attribute, etc. The wellbore sampling operation may be completed via wireline (such as the wireline system 100 of FIG. 1), although other implementations may be possible. The sampling optimizer 210 may include one or more objective functions that may enable an operator/user of the wireline system 100 to maximize the number of zones sampled on an initial run of the logging tool 108 without jeopardizing the integrity of the well. The one or more objective functions may be constrained by the wellbore pressure at the zones and a maximum trip speed of the logging tool 108. Using the one or more objective functions, the sampling optimizer 210 may output an optimum fluid influx into the wellbore at each zone, a number of zones to be sampled, and the order in which the zones are to be sampled. Additionally, the sampling optimizer 210 may be configured to perform the operations of FIGS. 5-7.

Any one of the previously described functionalities may be partially (or entirely) implemented in hardware and/or on the processor 201. For example, the functionality may be implemented with an application specific integrated circuit, in logic implemented in the processor 201, in a co-processor on a peripheral device or card, etc. Further, realizations may include fewer or additional components not illustrated in FIG. 2 (e.g., video cards, audio cards, additional network interfaces, peripheral devices, etc.). The processor 201 and the network interface 205 are coupled to the bus 203. Although illustrated as being coupled to the bus 203, the memory 207 may be coupled to the processor 201.

Example Illustrations

FIG. 3 is an example plot depicting pit gain vs. time during a sampling operation, according to some implementations. A plot 300 may include an X-axis 302 depicting time in seconds and a Y-axis 304 depicting a pit gain in meters cubed (m³). A pit gain trend 306 may show how pit gain of one or more mud pits at the surface changes over time. The pit gain may spike during fluid influx events within a wellbore, causing fluid in the well to flow into the retention pits at the surface. These fluid influx events, which may be referred to as kicks, may result from one or more fluid bubbles reaching the surface of the wellbore. These bubbles may enter the wellbore 110 as a result of sampling a formation within the well. In some implementations, the spikes in pit gain may result from an underbalanced well scenario in which a hydrostatic pressure of the column of fluid in the well is less than the pore pressure of the one or more reservoirs (or formation) in the wellbore. Excess fluid ingress may occur when the well is underbalanced. Uncontrolled kicks may result in a blowout in which gas and/or liquid may be expelled from the surface of the well at high speed.

The fluid influx events in the wellbore may be the result of gas influx. Spikes in pit gain over time may correspond to gas influx events from one or more zones within the wellbore. The hydrostatic pressure at the depth of the zones 112-116, for example, may change based on various factors. For example, tripping the logging tool 108 in and out of the wellbore 110 may induce increases and decreases in bottomhole pressure, respectively. Controlling the pressure in a well and ensuring the well is properly overbalanced during sampling may become especially challenging due to the presence of wireline equipment such as the logging tool 108 within the well. Therefore, maintaining stringent control measures while conducting sampling operations is essential.

FIG. 4 is an illustration depicting example sampling orders, according to some implementations. A diagram 400 includes a plurality of sampling sequences 402-212. Sampling sequences 402 and 404 are traditional sampling sequences in which a logging tool may sample zones linearly, either from top-to-bottom or bottom-to-top. Several zones may be sampled in traditional sampling in sequence. If a risk of a kick is encountered when using wireline, the logging tool 108 may be pulled from the wellbore 110, and a second run may be carried out. However, sampling sequences 406-412 are non-traditional sampling sequences. At least one of the non-traditional sampling sequences may be an optimized sampling sequence used to maximize the number of samples taken in a single trip without jeopardizing the well.

The sampling optimizer 210 of FIG. 2 may generate the non-traditional sampling sequences 406-412. As referenced above, the sampling optimizer 210 may utilize one or more objective functions to determine the non-traditional sampling sequences 406-412. The objective function(s) may include one or more algorithms, computerized functionalities, etc. with a goal of maximizing the number of zones sampled on a single trip of the logging tool 108 in the wellbore 110. The one or more objective functions may be constrained by a wellbore pressure at each of the zones, a maximum trip speed of the logging tool 108, etc. The sampling optimizer 210 may output values determined via the one or more objective functions such as an optimum fluid influx into the wellbore at each zone, a number of zones which may be sampled during the trip, and the order (sequence) of the sampling of the zones. The optimum fluid influx of each zone may refer to a fluid (especially gas) inflow rate and volume from each zone that, when accounting for the depth of each zone, time of each sampling procedure at each respective zone, the sampling sequence for all the zones, gas expansion, etc., may avoid producing bubbles that exceed safe operating parameters. For example, zone 116 and zone 112 may produce an equal rate/volume of fluid inflow into the wellbore 110. However, because the zone 116 is deeper than the zone 112, gases from the zone 116 may expand further than those sourced from the zone 112 as they approach the surface. The sampling optimizer 210 may factor in how much inflow from each zone is permissible to allow for sampling of as many zones as possible within a single trip of the logging tool 108. In some implementations, the optimum fluid influx of each zone may be determined based on the pit gain observed at the surface.

The sampling optimizer 210 may use various methods to generate the non-traditional sampling sequences 406-412, at least one of which may be an optimized sampling sequence. For example, the sampling optimizer 210 may use any one of a brute force technique, a penalty technique, etc. However, any other technique or optimization approach may be used. The brute force technique may, for example, refer to a straightforward computational approach to compute every possible combination of sampling sequences.

The penalty technique may refer to an optimization approach used to solve a constrained optimization problem. The penalty method may include a penalty function incorporated with an objective function that penalizes violations of constraints. For example, the sampling optimizer may use cumulative fluid influx estimates from all zones as a constraint. Other constraints may include the wellbore pressure at the zones, maximum trip speed of the logging tool 108, etc. The penalty technique may be expanded to include calculations for 10 or more zones without the penalty of time.

The objective function(s) used by the sampling optimizer 210 may include Equation 1:

F ⁡ ( X ) = f ⁡ ( X ) + a ⁢ ∑ i = 1 m [ G j ( X ) ] 2 + b ⁢ ∑ k = 1 p [ H k ( X ) ] 2 ( Eq . 1 )

where f(x₁, . . . , x_p) is to be maximized, as this is the number of zones sampled with constraints g_i(x₁, . . . , x_n) 1eq/eq 0 (i=1, . . . , m). In some implementations, the constraints may be flow constraints.

In some implementations, a for-else loop may be used. For example, Equation 2 may be used in conjunction with the for-else loop of Equations 3-4:

f ′ ( x 1 , … , x n ) = f ⁡ ( x 1 , … , x n ) + ∑ i = 1 , m w i · h i ( x 1 , … , x n ) ( Eq . 2 ) with : For ⁢ g i ( x 1 , … , x n ) = 0 : h i ( x 1 , … , x n ) : = - g i ( x 1 , … , x n ) ( Eq . 3 ) ( Eq . 4 ) For ⁢ g i ( x 1 , … , x n ) < = 0 : h i ( x 1 , … , x n ) : = IF ⁢ g i ( x 1 , … , x n ) < 0 , THEN ⁢ 0 ELSE = - g i ( x 1 , … , x n )

Equations 1˜4 may be used by the sampling optimizer 210 to perform at least a portion of the operations of FIGS. 5-7. In some implementations, Equations 3-4 may represent various constraints of the sampling operation.

Example Flowcharts

FIGS. 5-7 include multiple flowcharts depicting example techniques for determining properties of gas bubbles from multiple zones in a well during a sampling operation. The properties may be used to determine an optimal sampling procedure for the zones within the wellbore. The flowcharts may include a simulation of a formation sampling procedure in a wellbore. The simulation may be performed using at least one of the object functions described with reference to FIG. 4. At least some of the operations described in FIGS. 5-7 may be part of loops that are completed for a plurality of bubbles in the wellbore. At least a portion of the operations described in FIGS. 5-7 may not be simulated and may instead respond to data from a live well operation.

FIG. 5 is a first flowchart 500 depicting example operations for determining an optimized sampling sequence, according to some implementations. Operations of the flowchart 500 may be described with reference to FIGS. 1-4. The operations of the flowchart 500 may be performed by any combination of hardware/software, etc. Operations of the flowchart 500 begin at block 502.

At block 502, the sampling optimizer 210 may begin a simulation of a sampling operation in a well bore. The simulation may be used to determine properties of gas bubbles entering the wellbore during sampling. For example, the logging tool 108 may include a sampling tool. The sampling tool may be used to probe the wellbore 110 to sample a formation fluid of one or more subsurface formations at formation zones 112-116 in the wellbore 110. Formation fluid may be released into the wellbore and/or borehole during sampling. The released formation fluid may form bubbles that expand as they move up the wellbore towards the surface. As the bubbles expand and move towards the surface, a bottomhole pressure of the well may decrease further.

If the hydrostatic pressure from a fluid column in the wellbore 110 decreases lower than the pore pressure any of the zones 112-116, additional fluid influx into the well may occur. This may induce a kick and other well control issues during sampling. The simulation initiated at block 502 may simulate the entire sampling process and may determine an optimal order and/or sequence to safely sample the plurality of formation zones. Flow progresses to block 504.

At block 504 the sampling optimizer 210 may import (via one or more sensors, one or more tools in the wellbore, etc.) a given value of surface pressure. For example, the sampling optimizer 210 begin with simulation of block 502 with a known value of surface pressure in atmospheres prior to sampling. In some implementations, a given surface pressure in pascals may also be provided, although other units may be possible. This known surface pressure may also be set by a user/personnel prior to simulation. Flow progresses to block 506 which is contained within a loop 550.

At block 506, the sampling optimizer 210 may determine a gas bubble location and depth during the simulation. For example, the sample optimizer 210 may use one or more sensors in the wellbore 110, a known geometry of the wellbore 110 (e.g., the diameter of the wellbore), etc. to determine the location and depth of one or more gas bubbles in the well released during the sampling of a zone. The depth of each bubble may refer to the depth at the bottom of the bubble. Flow progressives to block 508.

At block 508, the sampling optimizer 210 may update a density of a fluid and gas within the wellbore 110. For example, the sampling optimizer 210 may update the density of the fluid (which may include liquid and gas) from block 506 based on the determined bubble location and depth. Flow progresses to block 510.

At block 510, the sampling optimizer 210 may update a wellbore pressure profile based on the determined fluid densities of block 508 and the gas bubble location and depth of block 506. The calculations to update the wellbore pressure profile may be updated step-by-step based on the location depth and length of each bubble in the well. The updated wellbore pressure profile may also be updated based on a mud interval between each of the identified bubbles in the wellbore, where the mud interval may refer to a depth range within the wellbore

Blocks 506 through 510 may be contained within a loop 550. The loop 550 may include additional calculations in addition to those described in blocks 506-510. The loop 550 may also be used to update the wellbore pressure profile of block 510 for each individual bubble in the well, where each loop of the loop 550 may be completed for each bubble one-by-one. The loop may be repeated a plurality of times for each bubble at each time step. Other additional calculations may be expanded upon in FIG. 6. For example, the processes described in FIG. 6 may be used to determine gas bubble attributes in the well, and these results may be fed into the loop 550 via transition point B. After updating the wellbore pressure, flow progresses to block 512.

At block 512 the sampling optimizer 210 may use the updated wellbore pressure profile of block 510 to obtain an overall pressure profile of the well. For example, the given surface pressure of block 502 and the updated wellbore pressure profile of block 510 may be used to obtain the overall pressure profile. Flow progresses to block 514.

At block 514, the sampling optimizer 210 may record middle data. For example, the middle data may include pressure history, bubble locations, and other data recorded at time steps after initiating the simulation. The middle data may be recorded at certain depths within the (simulated) wellbore 110, such as depths proximate to the sampled zones and the casing shoe depth.

The fluid sampling operation may be simulated during this time, in which a sampling tool samples a plurality of zones in a specified order within the simulated wellbore. Fluid influx may occur during the sampling, and the fluid may form bubbles that progress to the surface. Flow progresses to block 516.

At block 516, the sampling optimizer 210 may determine whether the deepest kick gas bubble front-edge reaches the surface. The bubble front-edge of the deepest kick gas bubble may refer to the upper portion of the final bubble traveling through the wellbore to the surface. The bubble front of the deepest bubble may be determined as having reached the surface based on one or more sensors in the wellbore, a ceasing of fluid influx (also referred to as pit gain) into one or more pits at the surface, etc.

In some implementations, the above calculations may be repeated for each bubble in the well until the final bubble's front edge reaches the surface. The final kick gas bubble may be from the deepest sampled formation, from the most recently sampled formation, etc. If the final kick gas bubble front edge has not reached the surface, flow returns to block 504 in which the calculations of blocks 504-514 are repeated for any number of bubbles in the well. If the final kick gas bubble front edge has reached the surface, then flow progresses to block 518.

At block 518, the sampling optimizer 210 may record time and results after ending the simulation. The time and results may include a total time of all the bubbles migrate to the surface within the simulated wellbore, the time to underbalanced condition at any depth of interest (such as sampling depth, casing shoe, etc.) within the simulated wellbore, a pressure profile of the well, etc. The sampling optimizer 210 may end the simulation when the front edge of the final bubble in the wellbore reaches the surface. The sampling optimizer 210 may determine, based on the recorded results, whether the sampling sequence of the zones requires optimization. Flow progresses to block 520.

At block 520, the sampling optimizer 210 determines whether to optimize a sampling order of the subsurface zones based on the results of block 518. For example, the sampling optimizer 210 may initiate sampling order optimization to optimize an order in which the zones of the one or more subsurface formations in the wellbore are sampled to avoid a kick at the surface. If the sampling optimizer 210 determines that optimization is required, flow progresses to block 522. If the sampling optimizer 210 determines that a sampling order optimization is not needed, flow progresses to block 524 at which results of the simulation are output.

At block 522, the sampling optimizer 210 may save the simulation results with the sampling sequence described in the flowchart 500. Multiple simulations with differing sampling orders may be performed, and the sampling optimizer 210 may be configured to save each one. From block 522, flow progresses to transition point A which continues at FIG. 7.

At block 524, the sampling optimizer 210 outputs the results of the simulation with the original sampling order. In some implementations, the results may be output to a user interface. Output from transition point D may also feed into block 524; transition point D is depicted in FIG. 7. Flow progresses to block 526.

At block 526, the sampling optimizer 210 may perform further processing on the output results of block 524. For example, the sampling optimizer 210 may perform qualitative post-processing, generate one or more plots based on the output results, perform quantitative post-processing, extract numerical values from the results of block 524, etc. Flow of the flowchart 500 ceases.

FIG. 6 is a second flowchart 600 depicting example operations for determining an optimized sampling sequence, according to some implementations. Operations of the flowchart 600 may be described with reference to FIGS. 1-5. The operations of the flowchart 600 may be performed by any combination of hardware/software, etc. The operations of the flowchart 600 may be performed alongside or in parallel with at least some of the operations of flowchart 500. Operations of the flowchart 600 begin at block 602.

At block 602, the sampling optimizer 210 may begin calculations with a given influx volume of one or more bubbles at their respective in-situ pressure and temperature. For example, the sampling optimizer 210 may obtain the gas bubble location depth and length of block 506 based on the given volume of each bubble. The depth of the bubble corresponds to its bottom, and a length of the bubble is also known. Given a known diameter of the wellbore, the sampling optimizer 210 may determine the volume of each bubble (so as the depth and length of the bubbles) in the wellbore at their respective in-situ pressure and temperatures as they move towards the surface. These values, and all subsequent calculations, may be repeated at each time step to account for the movement of the bubbles. Flow progresses to block 604.

At block 604, a quality check may be performed on the simulation. For example, this may be performed by personnel or a computerized functionality. The quality check may include checking the volume of all bubbles within the simulated wellbore. If any one bubble exceeds a length limit and is too large, the simulation may be halted. For example, a bubble that is too large may be a bubble that overlaps the bubble above it. This may produce invalid inputs for the optimization function described in FIG. 4. Assuming the simulation passes the quality check, flow progresses to block 606.

At block 606, the sampling optimizer 210 may calculate an in-situ density of kick gas in the wellbore with the known in-situ pressure and temperature conditions. For example, the sampling optimizer 210 may determine an in-situ density of a kick gas bubble based on Boyle's law, a pit gain observed at the surface (i.e., a volume of wellbore fluid displaced), and a known density of the displaced wellbore fluid. Other techniques to determine the density of the kick gas may also be used. Flow progresses to block 608.

At block 608, the sampling optimizer 210 may determine a total influx gas mass. For example, the sampling optimizer 210 may determine a mass of the gas influx into the wellbore based on the density of block 606. Flow progresses to block 610.

At block 610, a temperature model may be coupled with the sampling optimizer 210. For example, a temperature model may be provided and trained to model temperatures across the simulated wellbore. In addition to pressure, temperature may also effect the size of each bubble. The temperature model may be selected by a user or personnel. The temperature model may be configured for various well types and scenarios. For example, a geothermal temperature model may be used to model the temperature profile expected in a geothermal well. Other temperature models may also be possible. Flow progresses to block 612.

At block 612, the sampling optimizer 210 may determine a local temperature and pressure of each gas bubble. For example, the sampling optimizer 210 may use the temperature model of block 610 to determine the local temperature of each gas bubble. The local pressure at each gas bubble may be determined based on the hydrostatic pressure of the wellbore fluid at each depth added to the given surface pressure of block 502. Flow progresses to block 613.

At block 613, the sampling optimizer calculates a kick-gas gas/liquid phase change bubble pressure (Pb). Flow progresses to block 614.

At block 614, the sampling optimizer determines whether the local wellbore pressure at each gas bubble is greater than the kick gas/liquid phase change pressure of each bubble. If the wellbore pressure is greater, then flow progresses to block 616. If the local wellbore pressure is below the kick gas/liquid phase change pressure of a bubble, then flow progresses to block 618.

At block 616, the sampling optimizer 210 determines that a bubble in the well is comprised of a single liquid phase with no free gas present. This means that the local wellbore pressure is above the bubble point of the gas, and the kick is not multiphase. Therefore, the gas remains in solution. As the bubble moves up the wellbore and pressure decreases, gas may emerge from solution. Calculations at future time steps may recalculate for this emerging gas at an updated depth. From block 616, flow progresses to block 640.

Assuming free gas is present, flow progresses from block 614 to block 618.

At block 618, the sampling optimizer 210 may determine whether the mud in well comprises a water-based formation fluid (also referred to as water-based mud or WBM) or an oil-based formation fluid (oil-based mud, also referred to as OBM) or synthetic based mud (also referred to as SBM). If the mud is comprised of OBM, flow progresses to block 620. If the bubble is comprised of non-OBM formation fluid, flow progresses to block 626.

At block 620, the sampling optimizer 210 may determine a solubility of the free gas within the OBM and/or synthetic-based mud (SBM). The solubility may be used in future calculations. Flow progresses to block 622.

At block 622, the sampling optimizer 210 may determine a mass of the gas dissolved-into and/or to be released from the mud. Flow progresses to block 624.

At block 624, the sampling optimizer 210 may determine whether any free gas remains in the wellbore. If yes. Then flow progresses to block 626, where calculations are initiated to solve for a kick with a gas phase. If no free gas remains, then flow returns to block 616 in which a single-phase liquid kick is expected.

At block 626, the sampling optimizer 210 may begin calculations of a kick having a gaseous phase. These kicks move faster than single-phase liquid kicks in the wellbore and may prove more challenging to mitigate. Flow progresses to block 628.

At block 628, the sampling optimizer 210 may determine a kick gas Z-factor at the local temperature and pressure of the bubble including the gas phase. The Z-factor, also referred to as the compressibility factor, may be used in additional calculations. Flow progresses to block 630.

At block 630 the sampling optimizer 210 may determine a gas volume expansion with local pressure and temperature. This may quantify an extent to which the gas has expanded at the local pressure and temperature. This volume expansion may increase as the kick approaches the surface. Flow progresses to block 632.

At block 632, the sampling optimizer 210 may determine a gas migration velocity of the bubble. This may be repeated for each bubble in the wellbore. The gas migration velocity may be determined via one or more supplementary models. For example, at block 634, a gas migration velocity model may be coupled with the sampling optimizer 210 to determine the gas migration of velocity of the bubble in the wellbore. Flow progresses to block 636.

At block 636, the sampling optimizer 210 may determine a time effect of each of the bubbles. For example, the sampling optimizer 210 may determine how far up the wellbore the bubble has progresses since the previous time step. In some implementations, the time step may be adjusted at block 638 if necessary. For example, the time step may be adjusted if/when the bubble passes a critical depth, such as a formation boundary, downhole sensor, etc. Flow progresses to block 640.

At block 640, the sampling optimizer 210 may calculate each gas bubble location depth and length. These results may be input into block 506 for each bubble. Flow progresses to block 642.

At block 642, the sampling optimizer 210 may determine an expansion-displaced mud volume above the current bubble. In some implementations, the expansion-displaced mud volume above the current bubble may be determined via a pit gain observed at the surface as a result of gas expansion as the bubble approaches the surface. Flow progresses to block 644.

At block 644, the sampling optimizer 210 determines whether there are more bubbles in the wellbore. For example, more bubbles may indicate additional free gas in the wellbore which may indicate the potential for more kicks. If there are no more bubbles, flow progresses to the transition point B. From transition point B, flow returns to the loop 550 of FIG. 5, where the gas bubble location depth and length of block 640 are input into the flow of FIG. 5. In some implementations, the sampling optimizer 210 be deployed to a wellsite for real-time calculations. In some implementations, if no more bubbles are present in this scenario, the sampling optimizer 210 may remain on standby to determine a gas bubble location depth and length of a newly emerging bubble(s). If the sampling optimizer 210 detects that more bubbles are still present in the current kick, then flow progresses to block 646.

At block 646, the sampling optimizer 210 may determine a location of the above bubble (bubble above the current bubble). The location of the above bubble may be determined via one or more pressure sensors in the well, although other techniques to determine the above bubble location may also be used. This above bubble location may also be based on the outputs of block 640. Once the location of the above bubble has been determined, flow then returns to block 612, where the operations of blocks 612-644 may occur for this subsequent bubble and any additional bubbles within the well. Flow of the flowchart 600 ceases.

FIG. 7 is a third flowchart 700 depicting example operations for determining an optimized sampling sequence, according to some implementations. Operations of the flowchart 700 may be described with reference to FIGS. 1-6. The operations of the flowchart 700 may be performed by any combination of hardware/software, etc. The operations of the flowchart 700 may be performed alongside or in parallel with at least some of the operations of flowcharts 500 and 600. From transition point A, operations of the flowchart 700 begin at block 702.

At block 702 the sampling optimizer 210 may determine whether the optimization procedure is complete. One or more conditions may be examined to determine whether the optimization procedure is complete (i.e., whether any optimization is required). The sampling optimizer 210 may analyze the pit gain, surface conditions, a riser condition (including riser pressure and temperature for offshore operations), shoe conditions, wellbore pressure, etc. If the optimization process is determined to be complete, flow progresses to block 704. If the sampling optimizer 210 determines that the optimization procedure is not complete, flow progresses to block 706.

At block 704, the sampling optimizer 210 may compare all simulations and find the best optimized sampling sequence. This sampling sequence may include a certain order/sequence in which to sample the various zones within the wellbore. The best approach will include the maximum number of zones sampled within the allotted constraints of the objective function. The best optimized sampling sequence may also be output with the number of zones sampled, the order of the zones, and the optimum fluid influx into the wellbore at each zone. Other outputs may include the well pressure profile, pressure history, location of the one or more bubbles within the well, bubble locations through time, total fluid influx volume, a time until at least certain depths of the well become underbalanced, etc. The best optimized sampling sequence may be selected as the ordering that minimizes the flow-in pressure in the well, minimizes a reduction in shut-in pressure of the well after sampling has concluded, maximizes the number of zones sampled, minimizes the time to run in hole, etc. These results may flow to transition point D. From transition point D, flow may return to block 524 in which results of the simulation are output. In some implementations, the results may be compared to data from other logging runs, both real-world and simulated. The outputs from these other logging runs may be used in retraining the sampling optimizer 210 for more accurate results.

Assuming the optimization procedure is incomplete, flow progresses to block 706.

At block 706, the sampling optimizer 210 may select an optimization technique for the sampling order. In some implementations, the optimization technique may be selected by personnel (a user) via a user interface. Any optimization technique may be used. For example, the sampling optimizer 210 and/or a user may choose to optimize the sampling sequence of the zone using a brute force approach, a penalty approach, a super-position approach, etc. Flow progresses to block 708.

At block 708, the sampling optimizer 210 may rearrange the sampling sequence of the zones. Flow progresses to block 710.

At block 710, the sampling optimizer 210 may rerun the workflow of the flowcharts 500 and 600 with the rearranged sampling sequence. From block 710, flow progresses to transition point C and transition point E. Both transition point C and transition point E lead into the operations of flowchart 500 and flowchart 600 respectively-transition point C returns to block 504, and transition point E returns to block 602. Flowcharts 500 and 600 may be re-run for a plurality of simulations to test a variety of sampling orders. find the best optimized sampling sequence for sampling the zones. An optimized sampling approach may then be determined from the variety of sampling orders. Flowcharts 500 and 600 may be run in parallel to determine bubble/kick properties, wellbore data, etc. Flow the flowchart 700 ceases.

In some implementations, at least some portions of the operations of FIGS. 5-7 may be used in training the sampling optimizer 210, re-training the sampling optimizer 210, etc. The sampling optimizer 210, once trained, may be deployed to determine a real-world optimal sampling sequence for the zones 112-116 of the wellbore 110. The sampling modeling and optimization calculations may be performed in real-time during a fluid sampling operation, before a fluid sampling operation, or at any other point in time. Other implementations may be possible.

Example Results

FIGS. 8-9 include plots which may include outputs from the simulation of FIGS. 5-7. FIG. 8 is a plot depicting pressure history vs. time of one or more bubbles in the wellbore, according to some implementations. A plot 800 includes an X-axis 802 of time in seconds and a Y-axis 804 of pressure in pascals. In some implementations, the bubbles may be comprised of a single-phase liquid including dissolved gas. However, the bubbles may also be comprised of multiphase fluid. A plurality of sensors positioned at various depths may be configured to collect pressure data over time in the wellbore. Pressure history data 806 may be collected via a pressure sensor positioned at a depth of 5,000 m in a wellbore such as the wellbore 110. Pressure history data 808 may be collected via a pressure sensor positioned at a depth of 4,000 m. Pressure history data 810 may be collected via a pressure sensor positioned at a depth of 3,000 m. Pressure history data 812 may be collected via a pressure sensor positioned at a depth of 2,500 m. Pressure history data 814 may be collected via a pressure sensor positioned at a depth of 2,000 m. Across all pressure history data trends 806-814, five stark drops in wellbore pressure are observed. These pressure drops may occur as a bubble reaches the surface of the wellbore.

FIG. 9 is a plot depicting time vs. depth of one or more bubbles in the wellbore, according to some implementations. A plot 900 includes an X-axis 902 of time in seconds and a Y-axis 904 of depth in feet. The plot 900 depicts the migration and expansion of a plurality of bubbles as they approach the surface with time. Each of the bubbles may emerge from zones at different depths. The simulation of FIGS. 5-7 may conclude as the front edge of a final bubble 906 reaches the surface of the wellbore.

Example Method of Operations

FIG. 10 is a flowchart depicting an example method of operations, according to some implementations. Operations of a method 1000 may be performed by software, firmware, hardware, or a combination thereof. Such operations are described with reference to FIGS. 1-9. However, such operations may be performed by other systems or components. The operations of the method 1000 begin at block 1002.

At block 1002, the method includes determining an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within a wellbore, the wellbore formed in one or more subsurface formations. For example, the sampling optimizer 210 may be configured to determine an optimized sampling sequence that samples a maximum number of zones within a single trip of the logging tool 108. The sampling optimizer may utilize the operations of the flowcharts 5-7 to determine one or more properties of at least one bubble traveling to the surface of the wellbore 110.

One or more wellbore attributes and one or more operational attributes may be determined based on the one or more properties of the bubble(s). For example, at least the operations of blocks 506-510 and the flowchart 600 may be used to determine bubble properties including the volume, depth, density, location, time to reach a critical depth, and velocity of one or more gas bubbles in the wellbore 110. The sampling optimizer 210 may determine one or more wellbore attributes such as an overall pressure profile of the well, optimum fluid influx from each zone, a wellbore pressure at each of the zones, pressure history, a time in which at least some portions of the well become underbalanced, a shut-in pressure of the well, etc. Some implementations of the sampling optimizer 210 may determine a maximum time to an underbalance condition at one or more depths in the well. This may also be referred to as a maximum time to be underbalanced in the wellbore. The one or more wellbore attributes may refer to any property related to the wellbore and/or subsurface formations and reservoirs before, during, or after the sampling operation. The one or more operational attributes may refer to any property related to an operation within the wellbore 110—specifically, operation of the logging tool 108. For instance, the operational attributes may include the number of zones sampled within the logging operation, the ordering of the zones to be sampled, the run-in time into the wellbore, etc. Flow progresses to block 1004.

At block 1004, the method 1000 includes performing a sampling operation with the optimized sampling sequence. An optimized sampling sequence may be selected as a sampling sequence that satisfies the majority of the following: maximizes the number of zones sampled in a single trip of the logging tool 108, minimizes a reduction in shut-in pressure at the end of the sampling operation, maximizes a time to underbalance at one or more locations within the wellbore, and minimizes a time to run in-hole. The sampling optimizer 210 may select the optimized sampling sequence from one or more sampling sequences. A sampling operation may be performed according to the optimized sequence selected by the sampling optimizer 210. Flow of the method 1000 ceases.

In some implementations, a downhole operation or attribute in the wellbore may be modified or updated based on the determined bubble properties in the wellbore. For example, an operation (at the surface or downhole) may be performed and/directed to be performed to change a downhole operation or attribute. For example, a sampling order of a sampling operation may be rearranged based on the influx of fluid at each of the zones, a time in which a bubble front-edge reaches the surface, etc. For instance, if a current order to sample the zones is determined to not be optimal, any one of the wellbore attributes and operational attributes may be updated to increase the number of zones sampled within a single run of the logging tool 108 without inducing hazardous conditions (i.e., a kick leading to a blowout) at the surface.

While the aspects of the disclosure are described with reference to various implementations and exploitations, it will be understood that these aspects are illustrative and that the scope of the claims is not limited to them. In general, techniques for formation property simulation using a quantile-trained learning machine as described herein may be implemented with facilities consistent with any hardware system or hardware systems. Many variations, modifications, additions, and improvements may be possible.

Plural instances may be provided for components, operations or structures described herein as a single instance. Finally, boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of the disclosure. In general, structures and functionality presented as separate components in the example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure.

Use of the phrase “at least one of” preceding a list with the conjunction “and” should not be treated as an exclusive list and should not be construed as a list of categories with one item from each category, unless specifically stated otherwise. A clause that recites “at least one of A, B, and C” may be infringed with only one of the listed items, multiple of the listed items, and one or more of the items in the list and another item not listed. As an example, “at least one of: a, b, or c” is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c.

The various illustrative logics, logical blocks, modules, circuits, and algorithm processes described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. The interchangeability of hardware and software has been described generally, in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits and processes described throughout. Whether such functionality is implemented in hardware or software depends upon the particular application and design constraints imposed on the overall system.

The hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules and circuits described in connection with the implementations disclosed herein may be implemented or performed with a general purpose single- or multi-chip processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor or any conventional processor, controller, microcontroller, or state machine. A processor also may be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some implementations, particular processes and methods may be performed by circuitry that is specific to a given function.

In one or more implementations, the functions described may be implemented in hardware, digital electronic circuitry, computer software, firmware, including the structures disclosed in this specification and their structural equivalents thereof, or in any combination thereof. Implementations of the subject matter described in this specification also may be implemented as one or more computer programs, e.g., one or more modules of computer program instructions stored on a computer storage media for execution by, or to control the operation of, a computing device.

If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable instructions which may reside on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another. Storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-Ray™ disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations also may be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer-readable medium, which may be incorporated into a computer program product.

Various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

Certain features that are described in this specification in the context of separate implementations also may be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also may be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

While operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one more example process in the form of a flow diagram. However, some operations may be omitted and/or other operations that are not depicted may be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations may be performed before, after, simultaneously, or between any of the illustrated operations. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described should not be understood as requiring such separation in all implementations, and the described program components and systems may generally be integrated together in a single software product or packaged into multiple software products. Additionally, other implementations are within the scope of the following claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve desirable results.

Unless otherwise specified, use of the terms “up,” “upper,” “upward,” “uphole,” “upstream,” or other like terms shall be construed as generally away from the bottom, terminal end of a well; likewise, use of the terms “down,” “lower,” “downward,” “downhole,” or other like terms shall be construed as generally toward the bottom, terminal end of the well, regardless of the wellbore orientation. Use of any one or more of the foregoing terms shall not be construed as denoting positions along a perfectly vertical axis. In some instances, a part near the end of the well may be horizontal or even slightly directed upwards. Unless otherwise specified, use of the terms “subsurface formation” or “subterranean formation” shall be construed as encompassing both areas below exposed earth and areas below earth covered by water such as ocean or fresh water.

Example Implementations

Implementation #1: A method comprising: determining an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within a wellbore, the wellbore formed in one or more subsurface formations.

Implementation #2: The method of Implementation 1, wherein determining the optimized sampling sequence comprises selecting the optimized sampling sequence from a plurality of sampling sequences, and wherein each sampling sequence of the plurality includes an order in which to sample the plurality of zones.

Implementation #3: The method of any one or more of Implementations 1-2, further comprising: determining, via one or more processors, a plurality of properties of one or more bubbles within the wellbore.

Implementation #4: The method of any one or more of Implementations 1-3, further comprising: determining at least one of a wellbore attribute and an operational attribute during the sampling operation based on the plurality of properties of the one or more bubbles, wherein the optimized sampling sequence is determined based, at least in part, on at least one of the wellbore attribute and the operational attribute.

Implementation #5: The method of any one or more of Implementations 1-4, further comprising: determining, via one or more processors, a number of zones to be sampled within the wellbore during the sampling operation; and determining, via the one or more processors, an optimum fluid influx into the wellbore from each zone of the plurality of zones.

Implementation #6: The method of any one or more of Implementations 1-5, further comprising: determining, via one or more processors, the optimized sampling sequence with respect to at least one of a maximum trip speed of a logging tool in the wellbore, a wellbore pressure at each zone of the plurality of zones, and a maximum time to be underbalanced in the wellbore.

Implementation #7: The method of any one or more of Implementations 1-6, further comprising: directing an operation to modify the sampling operation based, at least in part, on the optimized sampling sequence.

Implementation #8: The method of any one or more of Implementations 1-7, further comprising: modifying the sampling operation based, at least in part, on the optimized sampling sequence, wherein the sampling operation is performed via a logging tool; and performing the modified sampling operation at the plurality of zones within the wellbore.

Implementation #9: A system comprising: a logging tool configured to sample one or more zones within a wellbore formed in one or more subsurface formations; one or more processors; and a computer-readable medium having instructions executable by the processor, the instructions including: instructions to determine an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within the wellbore.

Implementation #10: The system of Implementation 9, wherein the instructions to determine the optimized sampling sequence comprise instructions to select the optimized sampling sequence from a plurality of sampling sequences, and wherein each sampling sequence of the plurality includes an order in which to sample the plurality of zones.

Implementation #11: The system of any one or more of Implementations 9-10, further comprising: instructions to determine, via the one or more processors, a plurality of properties of one or more bubbles within the wellbore; and instructions to determine at least one of a wellbore attribute and an operational attribute during the sampling operation based on the plurality of properties of the one or more bubbles, wherein the optimized sampling sequence is determined based, at least in part, on at least one of the wellbore attribute and the operational attribute.

Implementation #12: The system of any one or more of Implementations 9-11, further comprising: instructions to determine, via the one or more processors, a number of zones to be sampled within the wellbore during the sampling operation; instructions to determine, via the one or more processors, an optimum fluid influx into the wellbore from each zone of the plurality of zones; and instructions to determine, via the one or more processors, the optimized sampling sequence with respect to at least one of a maximum trip speed of the logging tool in the wellbore, a maximum time to be underbalanced in the wellbore, and a wellbore pressure at each zone of the plurality of zones.

Implementation #13: The system of any one or more of Implementations 9-12, further comprising: instructions to direct an operation to modify the sampling operation based, at least in part, on the optimized sampling sequence.

Implementation #14: The system of any one or more of Implementations 9-13, further comprising: instructions to modify the sampling operation based, at least in part, on the optimized sampling sequence, wherein the sampling operation is performed via the logging tool; and instructions to perform the modified sampling operation at the plurality of zones within the wellbore.

Implementation #15: One or more non-transitory machine-readable media including instructions executable by one or more processors, the instructions comprising: instructions to determine an optimized sampling sequence that maximizes a number of zones to be sampled during a sampling operation of a plurality of zones within a wellbore, the wellbore formed in one or more subsurface formations.

Implementation #16: The machine-readable media of Implementation 15, wherein the instructions to determine the optimized sampling sequence comprise instructions to select the optimized sampling sequence from a plurality of sampling sequences, and wherein each sampling sequence of the plurality includes an order in which to sample the plurality of zones.

Implementation #17: The machine-readable media of any one or more of Implementations 15-16, further comprising: instructions to determine, via the one or more processors, a plurality of properties of one or more bubbles within the wellbore; and instructions to determine at least one of a wellbore attribute and an operational attribute during the sampling operation based on the plurality of properties of the one or more bubbles, wherein the optimized sampling sequence is determined based, at least in part, on at least one of the wellbore attribute and the operational attribute.

Implementation #18: The machine-readable media of any one or more of Implementations 15-17, further comprising: instructions to determine, via the one or more processors, a number of zones to be sampled within the wellbore during the sampling operation; instructions to determine, via the one or more processors, an optimum fluid influx into the wellbore from each zone of the plurality of zones; and instructions to determine, via the one or more processors, the optimized sampling sequence with respect to at least one of a maximum trip speed of a logging tool in the wellbore, a maximum time to be underbalanced in the wellbore, and a wellbore pressure at each zone of the plurality of zones.

Implementation #19: The machine-readable media of any one or more of Implementations 15-18, further comprising: instructions to direct an operation to modify the sampling operation based, at least in part, on the optimized sampling sequence.

Implementation #20: The machine-readable media of any one or more of Implementations 15-19, further comprising: instructions to modify the sampling operation based, at least in part, on the optimized sampling sequence, wherein the sampling operation is performed via a logging tool; and instructions to perform the modified sampling operation at the plurality of zones within the wellbore.