SYSTEM AND METHOD FOR PRODUCTION FORECASTING AND USES THEREOF

Description

FIELD OF THE DISCLOSURE

This disclosure relates generally to hydrocarbon production, and more particularly, to production forecasting.

BACKGROUND OF THE DISCLOSURE

Decline curve analysis (DCA) is a procedure used for analyzing production rates and forecasting future performance of oil and gas wells. Oil and gas production rates decline as a function of time. Fitting a line through a performance history and assuming this same trend will continue in future forms a basis of a DCA concept. Multiphase DCA is an extension of traditional DCA that accounts for a production of multiple fluid phases (such as oil, gas, and water) simultaneously. In many reservoirs, different phases of hydrocarbons are produced together, and respective production rates may decline differently over time. Similar to traditional DCA, historical production data for all fluid phases (oil, gas, water) are collected and analyzed. Multiphase decline models are used to characterize the production behavior of each fluid phase over time. These models can incorporate factors such as phase interactions, fluid properties, and reservoir dynamics. Multiphase decline models are used to simulate future production rates and reserves for each fluid phase. This allows operators to forecast the performance of the well under various scenarios and optimize production strategies accordingly.

SUMMARY OF THE DISCLOSURE

Various details of the present disclosure are hereinafter summarized to provide a basic understanding. This summary is not an extensive overview of the disclosure and is neither intended to identify certain elements of the disclosure nor to delicate the scope thereof. Rather, the primary purpose of this summary is to present some concepts of the disclosure in a simplified form prior to the more detailed description that is presented hereinafter.

According to an embodiment, a method can include receiving multiphase data comprising production data and pressure data, upscaling the multiphase data to produce upscaled multiphase data, segmenting upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data, and predicting a future production of the one or more wells based on a decline curve analysis (DCA) and the segmented production datasets.

According to another embodiment, a system can include one or more computing platforms configured to: receive multiphase data comprising production data and pressure data, upscale the multiphase data to produce upscaled multiphase data, segment upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data, and predict a future production of the one or more wells based on a DCA and the segmented production datasets.

In yet another embodiment, a system can include a tool that includes a data upscaler to receive multiphase data comprising production data and pressure data, upscale the multiphase data to produce upscaled multiphase data, a production data segmenter to segment upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data; and a production forecasting engine to predict a future production of the one or more wells based on a DCA and the segmented production datasets.

Any combinations of the various embodiments and implementations disclosed herein can be used in a further embodiment, consistent with the disclosure. These and other aspects and features can be appreciated from the following description of certain embodiments presented herein in accordance with the disclosure and the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example of a block diagram of a tool for production forecasting and estimating reserves.

FIG. 2 is an example of a graphical user interface (GUI) that can be rendered during production data analysis.

FIG. 3 is an example of a method for production forecasting and estimating reserves.

FIG. 4 is an example of a computing environment that can be used to perform one or more methods according to an aspect of the present disclosure.

FIG. 5 is an example of a cloud computing environment that can be used to perform one or more methods according to an aspect of the present disclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure will now be described in detail with reference to the accompanying Figures. Like elements in the various figures may be denoted by like reference numerals for consistency. Further, in the following detailed description of embodiments of the present disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the claimed subject matter. However, it will be apparent to one of ordinary skill in the art that the embodiments disclosed herein may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. Additionally, it will be apparent to one of ordinary skill in the art that the scale of the elements presented in the accompanying Figures may vary without departing from the scope of the present disclosure.

Examples are disclosed herein relating to multiphase decline curve analysis (DCA). Multiphase DCA is a challenging task as it requires individual analysis of a well's production history (e.g., across several production phases) for production forecasting and estimating reserves. This is because when several hundred wells are involved analysis needs to be carried out for each well, which can take substantial time especially for multiphase production histories with frequent irregular production that can be caused by various factors, for example, reservoir heterogeneity, operational changes, equipment malfunctions, and/or unexpected reservoir behavior.

Examples are disclosed for automating DCA across multiple production phases for forecasting production (e.g., estimating production in a future) and oil and gas reserves based on irregular production histories. According to the examples, a tool (e.g., implemented as machine readable instructions) can be configured to segment production history that does not follow traditional production declines (across multiple phases). Existing techniques required that a user (e.g., a reservoir engineer) locate production declines in hydrocarbon well's production history, which is prone to human error. By using the tool, production declines can be identified more accurately. The tool can be configured to analyze previous well head pressure or an equivalent production rate (across multiple phases) over a specified windowed time. In some examples, the tool can be used to detect production anomalies caused by changes in pressure or sudden changes in a reservoir system. The production anomalies can include, for example, reservoir fluid phase changes in the reservoir system, frack hits, and/or other subsurface phenomenon that can result in a significant response in well head pressure (e.g., the well head pressure exceeding a define or determined threshold). Traditional DCA is limiting to incorporating production volumes at daily, monthly, quarterly, or yearly intervals. However, by using the tool, engineers can implement decline curve analysis on multiphase high frequency data sets that includes less than a day sampling frequency in some instances.

FIG. 1 is an example of a block diagram of a tool 100 for production forecasting and estimating reserves of one or more wells 146. The tool 100 can be implemented using one or more modules, shown in block form in the drawings. The one or more modules can be in software or hardware form, or a combination thereof. In some examples, the tool 100 can be implemented as machine readable instructions for execution on one or more computing platforms 102 (referred to as a computing platform herein), as shown in FIG. 1. The computing platform 102 can include one or more computing devices selected from, for example, a desktop computer, a server, a controller, a blade, a mobile phone, a tablet, a laptop, a personal digital assistant (PDA), and the like.

The computing platform 102 can include a processor 104 and a memory 106. By way of example, the memory 106 can be implemented, for example, as a non-transitory computer storage medium, such as volatile memory (e.g., random access memory), non-volatile memory (e.g., a hard disk drive, a solid-state drive, a flash memory, or the like), or a combination thereof. The processor 104 can be implemented, for example, as one or more processor cores. The memory 106 can store machine-readable instructions that can be retrieved and executed by the processor 104 to implement the tool 100. Each of the processor 104 and the memory 106 can be implemented on a similar or a different computing platform. The computing platform 102 can be implemented in a cloud computing environment (for example, as disclosed herein) and thus on a cloud infrastructure. In such a situation, features of the computing platform 102 can be representative of a single instance of hardware or multiple instances of hardware executing across the multiple of instances (e.g., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). Alternatively, the computing platform 102 can be implemented on a single dedicated server or workstation.

The tool 100 includes a data upscaler 108. The data upscaler 108 can receive (or retrieve from the memory 106, or different memory) multiphase data 110. The multiphase data 110 can include production data 112 and pressure data 114. The production data 112 can indicate an amount of production produced over a period of time, such as an hour, a day, a month, or a year, or a volume amount per time for each well, and can be referred to as a production volume. In some examples, the production data 112 can indicate a production volume at which each fluid phase (e.g., oil, gas and water) is produced by each well over the period of time, or as a mount of volume per time. The production volume can be expressed in units, such as barrels (bp) for oil, cubic feet (cf) for gas, and barrels of water (bw) for water. The production volume of each fluid phase can be sampled. For example flow meters, gauges, sensors, or other measurement devices installed on a wellhead or at a production facility can be used to capture fluid flow rates and calculate corresponding production volumes. A fluid flow rate refers to a rate at which fluids (such as oil, gas, and/or water) are flowing through a well and/or a pipeline. The sampled production volumes can represent actual measurements of a fluid production at (or over) discrete time intervals, and can be provided as part of the production data 112. The production data 112 can be represented as a table, wherein rows of the table indicate a production volume of a well over a fixed interval of time, such as every 15 minutes. The pressure data 114 can include pressure measurements (or recordings), for example, wellhead pressure measurements. The pressure data 114 can be represented as a table, wherein rows of the table indicate a wellhead pressure over time (e.g., measured at fixed interval of time).

The data upscaler 108 can upscale the multiphase data 110 to provide upscaled data 116 based on a scaling parameter 118. Upscaling can be used by the data upscaler to transform higher frequency data, such as, production volume measured every minute, into equivalent lower frequency measure, such as hourly volume. The upscaled data 116 can include upscaled production data 120 and upscaled pressure data 122. The scaling parameter 118 can be user-defined in some instances. Thus, the user can control an upscaling process and specify a desired interval for data upscaling. The production data 112 and pressure data 114 for each well can be collected at a high frequency (e.g., frequently), such as every 15 or 30 minutes. High frequency data is more prone to noise or fluctuations due various factors, such as measurement errors, transient events, or operational variations. By upscaling, the data upscaler 108 aggregates and sums high-frequency data points, such as minute-by-minute production volumes, to create lower-frequency, smoother data that minimizes the effects of noise. For example, the data upscaler 108 can take production volumes recorded every minute (the high-frequency multiphase data 110) and sum these volumes to produce an aggregated measure, such as the total volume per hour (the upscaled data 116). Thus, the data upscaler 108 uses an upscaling process to reduce an impact of random fluctuations and measurement errors present in the original data.

The tool 100 includes a production data segmenter 124. The production data segmenter 124 can segment the upscaled production data 120 based on the segmentation criteria 148, which can be provided based on the upscaled pressure data 122. Pressure governs fluid flow within a reservoir. Pressure can influence a movement of fluids from reservoir rock into a wellbore and to a surface. Changes in pressure can occur due to various factors, for example, fluid depletion, reservoir compaction, fluid injection, and/or external influences, such as nearby production activities. In an isothermal reservoir production system, where temperature variations are ignored, changes in pressure can be related to changes in fluid saturation, reservoir drive mechanisms, and/or reservoir performance indicators. Production rate changes across oil, gas, and water phases are influenced by a well head pressure. Changes in the well head pressure can indicate when segmentation is initiated for large changes in production rate that cause deviations from traditional production declines. By segmenting the upscaled production data 120 based on pressure, distinct production regimes or behaviors within a reservoir can be analyzed. Different pressure regimes can correspond to different stages of reservoir depletion, fluid influx, and/or operational changes.

The segmentation criteria 148 can identify a deviation from a flagged pressure or equivalent production rate q_e change in either a maximum or minimum central tendency. The segmentation criteria 148 can be provided by a segmentation criteria calculator 130, as shown in FIG. 1. For example, the equivalent production rate q_e in either the maximum or minimum central tendency can be used, such as in instances in which pressure data is not available for one or more wells 146. The production data segmenter 124 can compute the equivalent production rate q_e according to the following expression:

q e = q boe + q w , ( 1 )

wherein q_e is the equivalent production rate, q_boe is an energy equivalent of combined oil and gas production phases, and q_w is a water production rate.

The energy equivalent parameter in equation (1) can be calculated by adding an oil production rate in barrels to an energy equivalent in gas. By way of example, if an oil rate, q_o, is in bbls/day and gas, q_g, is in MCF/day then q_boe is computed by adding q_o to a product of ⅙ and q_g (e.g., q_boe=q_o+⅙q_g). Thus, the energy equivalent combination of oil and gas production phases term q_boe in equation (1) represents a combined production of oil and gas that has been converted into a common energy unit. For example, to calculate q_boe, the production data segmenter 124 can convert gas production into oil equivalent using a conversion factor, such as a ratio of gas volume to oil volume based on energy content (e.g., cubic feet of gas per barrel of oil equivalent). Then, the production data segmenter 124 can sum up an oil production with the converted gas production to get q_boe. The water production rate q_w term represents a rate at which water is produced from the reservoir. The production data segmenter 124 can compute the equivalent production rate q_e by summing up the q_boe and q_w, which provides a comprehensive measure of a total fluid production of each well, accounting for both hydrocarbons (oil and gas) and water. Thus, the production data segmenter 124 can provide equivalent production rate data 132. The equivalent production rate data 132 can be represented as a table, wherein rows of the table indicate equivalent production rates.

For example, the production data segmenter 124 can use a running window 126 to compute a maximum and minimum central tendency. The running window 126 can define a data point selection window, such as over a well production history (the upscaled production data 120). The production data segmenter 124 can define a size of the running window 126 (referred to as a window length) by specifying a number of rows of information (from the upscaled production data 120) that the running window 126 can contain. In an example, if the production data 112 is upscaled to 12-hour intervals (reflected by the upscaled production data 120) and a running window of 4 rows is chosen, each window can encompass 4 consecutive 12-hour intervals, totaling 2 days of production data (e.g., (4 rows*12 hours per row=48 hours=2 days)). Once the window length is set, the production data segmenter 124 can apply the running window 126 to the upscaled production data 120 by moving the running window sequentially down the rows of the upscaled production data 120. At each step, the running window advances by one row, overlaying a new subset of data points. From there the central tendency (e.g., mean or medium) is calculated by the production data segmenter 124 across the running window 126 for an entire production history (the upscaled production data 120). This results in a central tendency being calculated for each available row of the production history. From there the maximum and minimum of the central tendency can be calculated by the production data segmenter 124 across the running window 126. This results in a maximum and minimum central tendency for each available row of the production history (the upscaled production data 124).

The tool 100 further includes the segmentation criteria calculator 130, as shown in FIG. 1. The calculator 130 can receive or retrieve from the memory 106 the upscaled pressure data 122. The calculator 130 can calculate a central tendency of the upscaled pressure data 122 or the equivalent production rate data 132. The central tendency can be a median, mean, or mode. The calculator 130 can calculate a maximum of the central tendency (P_m,max) and a minimum of the central tendency (P_m,min) based on the upscaled pressure data 122. The maximum of the central tendency (P_m,max) and the minimum of the central tendency (P_m,min) refer to maximum and minimum values among the central tendency values calculated (e.g., maximum and minimum among the median, mean, or mode values). These values provide information about a range of central tendency values observed in the upscaled pressure data 122. The calculator 130 can calculate a percent change in a maximum central tendency ΔP_m,max). The calculator 130 can further calculate a percent change in a minimum central tendency (ΔP_m,min). The calculator 130 can then calculate a percent change in the upscaled pressure data 122 or the equivalent production rate data 132 (ΔP_%).

The calculator 130 calculates a mean and standard deviation of a maximum percent change of the central tendency (ΔP_m,max and σ_ΔPm,max respectively). The calculator 130 further calculates a mean and standard deviation of a minimum percent change of the central tendency (ΔP_m,min and σ_ΔPm,min respectively). The calculator 130 calculates a max flagged pressure or equivalent production rate (ΔP_flag,max) pressure change using the following expression:

Δ ⁢ P flag , max = Δ ⁢ P m , max + u stdv , max × σ Δ ⁢ P m , max , ( 2 )

wherein u_stdv,max is a maximum unit standard deviation indicative of an outlier.

For the equation (2), the outlier can be defined as data points that are a given standard deviation from a mean (e.g., 3.5 standard deviations from the mean, as an example). The outliner can be indicative of a segmentation change and thus are used in equation (2).

The calculator 130 can calculate a minimum flagged pressure or equivalent production rate pressure change (ΔP_flag,min) using the following expression:

Δ ⁢ P flag , min = Δ ⁢ P m , min + u stdv , min × σ Δ ⁢ P m , min , ( 3 )

wherein u_stdv,min is a minimum unit standard deviation indicative of an outlier.

The calculator 130 can provide the segmentation criteria 148 indicating the max and minimum flagged pressure or equivalent production rate pressure changes. The production data segmenter 124 can segment the upscaled production data 120 based on the segmentation criteria 148 to provide segmented production datasets 128. For example, the production data segmenter 124 can segment the upscaled production data 120 whether either the change in the maximum central tendency is larger than the maximum flagged pressure or the equivalent production rate change, or the change in the minimum central tendency is larger than the minimum flagged pressure or equivalent production rate change. The production data segmenter 124 can provide a series of segmented datasets of upscaled production data 120 referred to as the segmented production datasets 128 herein, each representing a distinct time interval defined by the running window 126 based on the segmentation criteria 148. Thus, the production data segmenter 124 can implement a segmentation algorithm (or method), as disclosed herein, that can be applied to well head pressure, which serves as a means to detect production anomalies. The tool 100 can detect sudden changes in a reservoir system (e.g., well). In regard to sudden changes in the reservoir system, this refers to changes that occur during production at the well that cause the production to deviate from a production decline (e.g., a baseline or traditional production decline). These changes can be attributed to changing a choke size of the well, nearby production wells that are siphoning production away (parent child relationships or frack hits) and other operational phenomena.

The tool 100 further includes a production forecasting engine 134 to forecasting production and estimate reserves of each well in a future to provide an estimated ultimate recovery (EUR) for each well. The production forecasting engine 134 can perform a DCA based on the segmented production datasets 128. Each segmented production dataset includes upscaled production volume data points. The production forecasting engine 134 can represent a multiphase production system as an empirical model. For example, the production forecasting engine 134 can perform a best fit for each segmented production dataset of the segmented production datasets 128 to provide empirical models (e.g., fitted empirical equations). An empirical model is a mathematical function that describes the relationship between variables based on observed data. In the context of the multiphase production system, the empirical model represents a relationship between the equivalent production rate data 132 and a set of parameters (β₀, β₁, β₂, . . . , β_n). The empirical model can be denoted by f(β₀, β₁, β₂, . . . , β_n), where β₀ to β_n are the controlling variables that influence a production behavior of the multiphase production system. The empirical model f (β₀, β₁, β₂, . . . , β_n) can take various forms depending on a specific characteristics of the multiphase production system. The empirical model could be a simple linear equation, a polynomial equation, or a more complex nonlinear function.

For example, the production forecasting engine 134 can generate a production prediction model 136 representing fitted empirical models provided based on the segmented production datasets 128. For the production prediction model 136, the production forecasting engine 134 can be configured to find best parameters (β₀, β₁, β₂, . . . , β_n) that minimize a square error between the equivalent production rate data 132 (for a given segmented production dataset of the segmented production datasets) and an empirical model f(β₀, β₁, β₂, . . . , β_n) provided based on the given segmented production dataset. The production forecasting engine 134 can iteratively adjusting the parameters to reduce the difference between observed and modeled production rates. The square error can be calculated as a squared difference between the equivalent production rate data 132) and modeled equivalent production rate f (β₀, β₁, β₂, . . . , β_n) for each production segment. By minimizing the square error, the production forecasting engine 134 can find the set of parameters that best represent observed production behavior. Accordingly, the production forecasting engine 134 can find the best parameters (β₀, β₁, β₂, . . . , β_n) that reduces the square error between the equivalent production rate data 132 and the empirical model represented by f(β₀, β₁, β₂, . . . , β_n) for each production segment (e.g., minimize [q−f(β₀, β₁, β₂, . . . , β_n)] 2, wherein q represents the given segmented production dataset and f(β₀, β₁, β₂, . . . , β_n) represents the empirical model, by changing β₀, β₁, β₂, . . . , β_n.

The production forecasting engine 134 can forecast (predict) a primary production phase starting from a last production segment until a terminal decline rate (e.g., a rate at which a hyperbolic decline switches from a hyperbolic to exponential decline) using the empirical model. The production prediction model 136 can forecast other production phases using fitted multiphase parameters up to the terminal decline rate. The production forecasting engine 134 can use the production prediction model 136 to forecast until economic limit. Thus, forecasting can be accomplished by the production forecasting engine 134 using the fitted empirical equation to extrapolate from the last available production data point. This is accomplished up to a terminal decline rate which outlines a critical production decline rate. From there an exponential model can be utilized up to the economic limit, which can represent a minimum production rate that makes the well profitable. The terminal decline rate and economic limit can be defined by the user, such as by using an input device, as disclosed herein.

For each production phase, the production forecasting engine 134 can sum up the volumes produced from each segmented production dataset, from forecasting using the multiphase system of empirical equations (the production prediction model 136), and an exponential model to the economic limit. The total production volume for the primary phase can be the estimated ultimate recovery (EUR). The total production volume for the secondary phase is the estimated ultimate recovery for that phase. The total production volume for the water phase is the estimated water that will be produced as consequence of producing the EURs from the primary and secondary phases. The primary phase can refer to a phase of focus that operations is most focused on producing. A secondary phase can refer to a phase that is dependent on a production of the primary phase. The water phase is an aqueous phase that is a production consequence from trying to produce the primary and secondary phases. Thus, the production prediction model 136 can sum up the production volumes produced from each of the segmented production datasets 128, from forecasting using the multiphase system of empirical equations, and the exponential model to the economic limit to compute a total production volume for the primary phase and the secondary phase, and the water phase for each well. For example, for a black oil reservoir, the primary phase is oil, and the secondary phase is gas. Thus, the total production volume for the primary and secondary phase can be oil and gas. In another example, if it is a gas/gas condensate reservoir, the primary phase can be gas and the secondary phase can be condensate (oil). Thus, the total production volume for the primary and secondar phase would be gas and condensate in the other example.

The production prediction model 136 (or the production forecasting engine 134) can output predicted production data 138. The predicted production data 138 can characterize the total production volume from each of the primary phase and the secondary phase. The predicted production data 138 can be rendered on an output device 150, as shown in FIG. 1. The output device 150 can correspond to an output device, as disclosed herein. In some examples, the tool 100 can generate a graphical user interface (GUI) based on the predicted production data 138 for rendering on the output device 150.

In some examples, the tool 100 includes a command generator 140 for optimizing a production at the one or more wells 146 based on the predicted production data 138. For example, the command generator 140 can be used to control a flow of hydrocarbons from a reservoir to a wellbore. The command generator 140 can generate a command for adjusting one or more parameters at the one or more wells. The command generator 140 can communicate the command over a network 142 to one or more control systems 144 at a corresponding well of the one or more wells 146. The network 142 can include wired and/or wireless networks. The command can cause cause the control systems 144 to adjust one or more operational parameters to optimize the production of the one or more wells 146. The one or more operational parameters include a flow rate, a pressure, and a temperature. For example, the control systems 144 can adjust a valve to optimize a flow rate at the corresponding well of the one or more wells 146 based on the command.

In some examples, the production prediction model 136 can be used as a part of a feedback control system. For example, if the well 146 is producing and the production prediction model 136 was created for the well 146, a production alarm could be generated by the command generator 140 to create an alert if production deviates from the production model 136. The alarm could alert the engineer and then they can investigate the issue that is causing the alarm, or initiate the control system 144 to adjust a production of the well 146.

By way of further example, in a given example, a wet gas system is producing with intermittent production changes corresponding to interrupted production declines. The series of production equation describing this multiphase production of gas (primary phase), condensate (secondary phase), and water can be represented by the following equations:

Gas Production Rate Equations:

q g ( t ) = QINIT , T < TMOS ; ( 4 ) q g ( t ) = QINIT ⁢ ( 1 + b × Di × T ) - 1 b ) ; ( 5 )

Condensate Production Rate Equations:

q g ( t ) = MIN ⁢ ( MAX ⁢ ( BMMINIT + SLOPE × 
 CUMGAS ) , BMMFINAL ) , BMMAX ) ; ( 6 )

• • and

Water Production Rate Equations:

q w = q g ( t ) GWR ⁢ or ⁢ q w = WOR × q 0 ( t ) , ( 7 )

wherein QINIT is an initial rate, TMOS is a time at constant gas rate, Di is a nominal initial decline rate, b is a hyperbolic exponent, >0, ≠1, BMMINIT is a y-axis intercept of straight-line relationship B/MM=BMMINIT+slope*Cumulative gas (MMCF), slope is a slope of straight line, BMMMAX is a maximum value for BMM, generally <BMMINIT, BMMFINAL is a lowest permitted value of B/MM, ≥0, GWR is a ratio of gas production to water production, and WOR is a ratio of water production to oil production.

The production forecasting engine 134 can reduce Σ(model-data)² using an empirical optimization algorithm such as genetic algorithm. The empirical model provides an equation based model for the primary, secondary, and water phases. When the empirical model is matched by reducing Σ(model-data)² this results in the equation model fitting the production data across the three phases.

FIG. 2 is an example of a graphical user interface (GUI) 200 that can be rendered during production data analysis. The GUI 200 can be provided by the tool 100, as shown in FIG. 1. Thus, reference can be made to one or more examples of FIG. 1 in the example of FIG. 2. The GUI 200 relates to an example of a multiphase DCA that can be implemented by the tool 100, as applied to gas condensate data where there is no water production. As shown in FIG. 2, production declines across multiple phases are captured and corresponding parameters per segment can be extracted by the tool 100 according to one or more examples, as disclosed herein.

In view of the foregoing structural and functional features described above, example methods will be better appreciated with reference to FIG. 3. While, for purposes of simplicity of explanation, the example method of FIG. 3 is shown and described as executing serially, it is to be understood and appreciated that the present example is not limited by the illustrated order, as some actions could in other examples occur in different orders, multiple times and/or concurrently from that shown and disclosed herein. Moreover, it is not necessary that all described actions be performed to implement the method.

FIG. 3 is an example of a method 300 for predicting production volume for one or more wells, such as the one or more wells 146, as shown in FIG. 1. The method 300 can be implemented by the tool 100, as shown in FIG. 1. Thus, reference can be made to one or more examples of FIGS. 1-2 in the example of FIG. 3. The method 300 can begin at 302 by receiving multiphase data (e.g., the multiphase data 110) comprising production data (e.g., the production data 112) and pressure data (e.g., the pressure data 114). At 304, the multiphase data is upscaled to produce upscaled multiphase data (e.g., the upscaled data 116). At 306, upscaled production data (e.g., the upscaled production data 120) of the upscaled multiphase data can be segmented to provide segmented production datasets (e.g., the segmented production datasets 128) based on segmentation criteria (e.g., the segmentation criteria 148). The segmentation criteria can be provided based on upscaled pressure data (e.g., the upscaled pressure data 122) of the upscaled multiphase data. At 308, a future production (e.g., the predicted production data 138) of the one or more wells can be predicted based on a DCA and the segmented production datasets.

While the disclosure has described several exemplary embodiments, it will be understood by those skilled in the art that various changes can be made, and equivalents can be substituted for elements thereof, without departing from the spirit and scope of the invention. In addition, many modifications will be appreciated by those skilled in the art to adapt a particular instrument, situation, or material to embodiments of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, or to the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, or component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative.

In view of the foregoing structural and functional description, those skilled in the art will appreciate that portions of the embodiments may be embodied as a method, data processing system, or computer program product. Accordingly, these portions of the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware, such as shown and described with respect to the computer system of FIG. 4. Thus, reference can be made to one or more examples of FIGS. 1-3 in the example of FIG. 4.

In this regard, FIG. 4 illustrates one example of a computer system 400 that can be employed to execute one or more embodiments of the present disclosure. Computer system 400 can be implemented on one or more general purpose networked computer systems, embedded computer systems, routers, switches, server devices, client devices, various intermediate devices/nodes or standalone computer systems. Additionally, computer system 400 can be implemented on various mobile clients such as, for example, a personal digital assistant (PDA), laptop computer, pager, and the like, provided it includes sufficient processing capabilities.

Computer system 400 includes processing unit 402, system memory 404, and system bus 406 that couples various system components, including the system memory 404, to processing unit 402. Dual microprocessors and other multi-processor architectures also can be used as processing unit 402. System bus 406 may be any of several types of bus structure including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. System memory 404 includes read only memory (ROM) 410 and random access memory (RAM) 412. A basic input/output system (BIOS) 414 can reside in ROM 412 containing the basic routines that help to transfer information among elements within computer system 400.

Computer system 400 can include a hard disk drive 416, magnetic disk drive 418, e.g., to read from or write to removable disk 420, and an optical disk drive 422, e.g., for reading CD-ROM disk 424 or to read from or write to other optical media. Hard disk drive 416, magnetic disk drive 418, and optical disk drive 422 are connected to system bus 406 by a hard disk drive interface 426, a magnetic disk drive interface 428, and an optical drive interface 430, respectively. The drives and associated computer-readable media provide nonvolatile storage of data, data structures, and computer-executable instructions for computer system 400. Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, other types of media that are readable by a computer, such as magnetic cassettes, flash memory cards, digital video disks and the like, in a variety of forms, may also be used in the operating environment; further, any such media may contain computer-executable instructions for implementing one or more parts of embodiments shown and disclosed herein. A number of program modules may be stored in drives and RAM 410, including operating system 432, one or more application programs 434, other program modules 436, and program data 438. In some examples, the application programs 434 can include one or more modules (or block diagrams), or systems, as shown and disclosed herein. Thus, in some examples, the application programs 434 can include the tool 100, as shown in FIG. 1.

A user may enter commands and information into computer system 400 through one or more input devices 440, such as a pointing device (e.g., a mouse, touch screen), keyboard, microphone, joystick, game pad, scanner, and the like. These and other input devices are often connected to processing unit 402 through a corresponding port interface 442 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, serial port, or universal serial bus (USB). One or more output devices 444 (e.g., display, a monitor, printer, projector, or other type of displaying device) is also connected to system bus 406 via interface 446, such as a video adapter.

Computer system 400 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer 448. Remote computer 448 may be a workstation, computer system, router, peer device, or other common network node, and typically includes many or all the elements described relative to computer system 400. The logical connections, schematically indicated at 450, can include a local area network (LAN) and a wide area network (WAN). When used in a LAN networking environment, computer system 400 can be connected to the local network through a network interface or adapter 452. When used in a WAN networking environment, computer system 400 can include a modem, or can be connected to a communications server on the LAN. The modem, which may be internal or external, can be connected to system bus 406 via an appropriate port interface. In a networked environment, application programs 434 or program data 438 depicted relative to computer system 400, or portions thereof, may be stored in a remote memory storage device 454.

Although this disclosure includes a detailed description on a computing platform and/or computer, implementation of the teachings recited herein are not limited to only such computing platforms. Rather, embodiments of the present disclosure are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models (e.g., software as a service (SaaS, platform as a service (PaaS), and/or infrastructure as a service (IaaS)) and at least four deployment models (e.g., private cloud, community cloud, public cloud, and/or hybrid cloud). A cloud computing environment can be service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability.

FIG. 5 is an example of a cloud computing environment 500 that can be used for implementing one or more modules and/or systems in accordance with one or more examples, as disclosed herein. Thus, reference can be made to one or more examples of FIGS. 1-7 in the example of FIG. 5. As shown, cloud computing environment 500 can include one or more cloud computing nodes 502 with which local computing devices used by cloud consumers (or users), such as, for example, personal digital assistant (PDA), cellular, or portable device 504, a desktop computer 506, and/or a laptop computer 508, may communicate. The computing nodes 502 can communicate with one another. In some examples, the computing nodes 502 can be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds, or a combination thereof. This allows the cloud computing environment 500 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. The devices 504-508, as shown in FIG. 5, are intended to be illustrative and that computing nodes 502 and cloud computing environment 500 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). In some examples, the one or more computing nodes 502 are used for implementing one or more examples disclosed herein relating to root-source identification. Thus, in some examples, the one or more computing nodes can be used to implement modules, platforms, and/or systems, as disclosed herein.

In some examples, the cloud computing environment 500 can provide one or more functional abstraction layers. It is to be understood that the cloud computing environment 500 need not provide all of the one or more functional abstraction layers (and corresponding functions and/or components), as disclosed herein. For example, the cloud computing environment 500 can provide a hardware and software layer that can include hardware and software components. Examples of hardware components include: mainframes; RISC (Reduced Instruction Set Computer) architecture based servers; servers; blade servers; storage devices; and networks and networking components. In some embodiments, software components include network application server software and database software.

In some examples, the cloud computing environment 500 can provide a virtualization layer that provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers; virtual storage; virtual networks, including virtual private networks; virtual applications and operating systems; and virtual clients. In some examples, the cloud computing environment 500 can provide a management layer that can provide the functions described below. For example, the management layer can provide resource provisioning that can provide dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. The management layer can also provide metering and pricing to provide cost tracking as resources are utilized within the cloud computing environment 500, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. The management layer can also provide a user portal that provides access to the cloud computing environment 500 for consumers and system administrators. The management layer can also provide service level management, which can provide cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment can also be provided to provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

In some examples, the cloud computing environment 500 can provide a workloads layer that provides examples of functionality for which the cloud computing environment 500 may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation; software development and lifecycle management; virtual classroom education delivery; data analytics processing; and transaction processing. Various embodiments of the present disclosure can utilize the cloud computing environment 500.

The present disclosure is also directed to the following exemplary embodiments, which can be practiced in any combination thereof:

A. A method comprising:

• • receiving multiphase data comprising production data and pressure data;
  • upscaling the multiphase data to produce upscaled multiphase data;
  • segmenting upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data; and
  • predicting a future production of the one or more wells based on a decline curve analysis (DCA) and the segmented production datasets.

B. A system comprising:

• • one or more computing platforms configured to:
    • receive multiphase data comprising production data and pressure data;
    • upscale the multiphase data to produce upscaled multiphase data;
    • segment upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data; and
      predict a future production of the one or more wells based on a decline curve analysis (DCA) and the segmented production datasets.

C. [Nth Independent Claim]

C. A system comprising:

• • a tool comprising:
    • a data upscaler to:
      • receive multiphase data comprising production data and pressure data;
      • upscale the multiphase data to produce upscaled multiphase data;
    • a production data segmenter to segment upscaled production data of the upscaled multiphase data to provide segmented production datasets based on segmentation criteria, the segmentation criteria being provided based on upscaled pressure data of the upscaled multiphase data; and
    • a production forecasting engine to predict a future production of the one or more wells based on a decline curve analysis (DCA) and the segmented production datasets.

Each of embodiments A through C may have one or more of the following additional elements in any combination: Element 1: optimizing a production of the one or more wells based on the predicted future production. Element 2: the optimization comprising controlling a flow of hydrocarbons from a reservoir to a wellbore. Element 3: the optimization comprises generating a command for adjusting one or more parameters at the one or more wells, and communicating the command to a control system to cause the control system to adjust one or more operational parameters to optimize the production of the one or more wells. Element 4: the one or more operational parameters include a flow rate, a pressure, and a temperature. Element 5: the segmenting of the upscaled production data is further based on a running window. Element 6: the production data characterizes a production volume of one or more wells, and the pressure data characterizes a wellhead pressure at the one or mor wells. Element 7: the upscaling is based on a scaling parameter. Element 8: the segmentation criteria characterizes maximum and minimum flagged pressure or equivalent production rate pressure changes.

The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a standalone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, for example, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “contains”, “containing”, “includes”, “including,” “comprises”, and/or “comprising,” and variations thereof, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. In addition, the use of ordinal numbers (e.g., first, second, third, etc.) is for distinction and not counting. For example, the use of “third” does not imply there must be a corresponding “first” or “second.” Also, as used herein, the terms “coupled” or “coupled to” or “connected” or “connected to” or “attached” or “attached to” may indicate establishing either a direct or indirect connection, and is not limited to either unless expressly referenced as such. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. The term “based on” means “based at least in part on.” The terms “about” and “approximately” can be used to include any numerical value that can vary without changing the basic function of that value. When used with a range, “about” and “approximately” also disclose the range defined by the absolute values of the two endpoints, e.g. “about 2 to about 4” also discloses the range “from 2 to 4.” Generally, the terms “about” and “approximately” may refer to plus or minus 5-10% of the indicated number.

What has been described above include mere examples of systems, computer program products and computer-implemented methods. It is, of course, not possible to describe every conceivable combination of components, products and/or computer-implemented methods for purposes of describing this disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of this disclosure are possible. The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.