Patent Application
20240287901
This disclosure relates generally to well metering.
Knowing the accurate production rate from each well in an oilfield is an important step in production management. It is extensively used in many field management-related activities, from defining the size of the process facility to overcoming production challenges. Well production rate and forecast are used in almost all engineering calculations. Yet it is very challenging for engineers to measure and forecast production rate with acceptable error and under reasonable capital expenditure.
According to various embodiments, a method of well metering a target well based on empirical operational parameter values for the target well is presented. The method includes: obtaining a well-test data set, the well-test data set including field measurements of a well-test flow rate and well-test operational parameter values for at least one test well; performing, based on the well-test data set, a meta-heuristic estimation of a plurality of coefficient values in a correlation, where the correlation correlates liquid flow rate with operational parameters; measuring empirical operational parameter values for the target well; determining a liquid flow rate value of the target well based on the empirical operational parameter values for the target well and using the correlation with the plurality of coefficient values; and providing the liquid flow rate value.
Various optional features of the above embodiments include the following. The method may include: determining that the liquid flow rate value of the target well can be improved based on the liquid flow rate value and a past liquid flow rate value of the target well; and improving the liquid flow rate of the target well. The improving may include adjusting at least one of: a choke size of the target well, a pump stroke rate for the target well, a gas injection rate for the target well, or a pump revolution per minute for the target well. The empirical operational parameter values for the target well may include a value for: a choke size for the target well, a wellhead pressure of the target well, and a gas-liquid ratio for the target well. The meta-heuristic estimation may be based on a particle swarm optimization. The particle swarm optimization may include a pre-specified: Gilbert particle, Ros particle, Baxendell particle, or Achong particle. The particle swarm optimization may include a time factor that provides a higher influence on the coefficients by newer well-test data of the well-test data set relative to a lower influence on the coefficients by older well-test data of the well-test data set. The correlation may be of a form:
where q represents the liquid flow rate, Pwh represents a wellhead pressure, Pwh represents a choke size, R represents a gas-liquid ratio, and A1, A2, and A3 represent the coefficients. The test well may include the target well. The method may include identifying the target well as a well-test candidate based on a difference between the liquid flow rate value and the well-test flow rate exceeding a predefined threshold.
According to various embodiments, a method of well metering a target well based on empirical operational parameter values for the target well without contemporaneous use of a wellhead flowmeter at the target well is presented. The method includes: obtaining a well-test data set, where the well-test data set includes field measurements of a well-test flow rate for a test well, where the well-test data set further includes well-test operational parameter values for the test well, where the operational parameter values for the test well include a value for: a choke size for the test well, a wellhead pressure of the test well, and a gas-liquid ratio for the test well; performing, based on the well-test data set, a meta-heuristic estimation of a plurality of coefficient values in a correlation, where the meta-heuristic estimation solves for the plurality of coefficient values in a multidimensional vector space using a swarm of particles in the multidimensional vector space, where a position of a particle in the swarm of particles represents potential values of the coefficients, where the correlation correlates liquid flow rate with operational parameters; measuring empirical operational parameter values for the target well, where the empirical operational parameter values for the target well include a value for: a choke size for the target well, a wellhead pressure of the target well, and a gas-liquid ratio for the target well; determining a liquid flow rate value of the target well based on the empirical operational parameter values for the target well and using the correlation with the plurality of coefficient values; and providing the liquid flow rate value.
Various optional features of the above embodiments include the following. The method may include: determining that the liquid flow rate of the target well can be improved based on the liquid flow rate value and a past liquid flow rate value of the target well; and improving the liquid flow rate of the target well, where the improving includes automatically adjusting at least one of: a choke size of the target well, a pump stroke rate for the target well, a gas injection rate for the target well, or a pump revolution per minute for the target well. The correlation may be of a form:
where q represents the liquid flow rate, Pwh represents a wellhead pressure, Pwh represents a choke size, R represents a gas-liquid ratio, and A1, A2, and A3 represent the coefficients. The performing may occur periodically, on demand, or upon the occurrence of specified events. The test well may include the target well, and the method may further include: identifying the target well as a well-test candidate based on a difference between the liquid flow rate value and the well-test flow rate exceeding a predefined threshold.
According to various embodiments, a method of well metering a target well based on empirical operational parameter values for the target well without contemporaneous use of a wellhead flowmeter at the target well, the method including: obtaining a well-test data set, where the well-test data set includes field measurements of a well-test flow rate for a test well, where the well-test data set further includes well-test operational parameter values for the test well, where the operational parameter values for the test well include a value for: a choke size for the test well, a wellhead pressure of the test well, and a gas-liquid ratio for the test well; performing, periodically, on demand, or upon the occurrence of specified events, and based on the well-test data set, a meta-heuristic estimation of a plurality of coefficient values in a correlation, where the meta-heuristic estimation solves for the plurality of coefficient values in a multidimensional vector space using a swarm of particles in the multidimensional vector space, where a position of a particle in the swarm of particles represents potential values of the coefficients, where the correlation correlates liquid flow rate with operational parameters, and where the correlation is of a form:
where q represents the liquid flow rate, Pwh represents a wellhead pressure, Pwh represents a choke size, R represents a gas-liquid ratio, and A1, A2, and A3 represent the coefficients; measuring empirical operational parameter values for the target well, where the empirical operational parameter values for the target well include a value for: a choke size for the target well, a wellhead pressure of the target well, and a gas-liquid ratio for the target well; determining a liquid flow rate value of the target well based on the empirical operational parameter values for the target well and using the correlation with the plurality of coefficient values; and providing the liquid flow rate value to a production optimization system, where the production optimization system includes hardware that automatically adjusts at least one of: a choke size, a pump stroke rate, gas injection rate, or a pump revolution rate.
Various optional features of the above embodiments include the following. The method may include: determining that the liquid flow rate of the target well can be improved based on the liquid flow rate value and a past liquid flow rate value of the target well; and improving the liquid flow rate of the target well, where the improving includes automatically adjusting, by the production optimization system, at least one of: a choke size of the target well, a pump stroke rate for the target well, a gas injection rate for the target well, or a pump revolution per minute for the target well. The meta-heuristic estimation may include a time factor that provides a higher influence on the coefficients by newer well-test data of the well-test data set relative to a lower influence on the coefficients by older well-test data of the well-test data set. The meta-heuristic estimation may include an iteration, where a step in the iteration includes a respective particle in the swarm of particles moving in a direction defined by a function of both a history of the respective particle and a collective behavior of the swarm of particles. The test well may include the target well, and the method may further include: identifying the target well as a well-test candidate based on a difference between the liquid flow rate value and the well-test flow rate exceeding a predefined threshold.
Combinations, (including multiple dependent combinations) of the above-described elements and those within the specification have been contemplated by the inventors and may be made, except where otherwise indicated or where contradictory.
Various features of the examples can be more fully appreciated, as the same become better understood with reference to the following detailed description of the examples when considered in connection with the accompanying figures, in which:
Reference will now be made in detail to example implementations, illustrated in the accompanying drawings. Wherever convenient, the same reference numbers will be used throughout the drawings to refer to the same or like parts. In the following description, reference is made to the accompanying drawings that form a part thereof, and in which is shown by way of illustration specific exemplary examples in which the invention may be practiced. These examples are described in sufficient detail to enable those skilled in the art to practice the invention and it is to be understood that other examples may be utilized and that changes may be made without departing from the scope of the invention. The following description is, therefore, merely exemplary.
The production rate, e.g., daily production rate, from an oil well can be measured using a flowmeter at wellheads, but because of inherent errors and economic limitations, flowmeters are not installed on every wellhead. Therefore, simulation model-based virtual-flow-metering has become an integral part of almost all digital oil field projects to predict the daily production rates. But it has its limitations, such as the need for substantial data points to create models and high capital expenditure associated with infrastructure setup, periodic system updates, and model management. Because of these limitations, it becomes difficult to execute model-based workflows for many operators around the world.
Thus, attempts have been made to use correlations between production rate and readily measured empirical wellhead parameters, such as choke size and wellhead pressure. The use of empirical choke flow correlations for estimating well rate still is particularly useful in the case of limited simulation infrastructure. The first empirical choke flow correlation was introduced in 1954, and since then numerous similar correlations have been proposed by different pioneer researchers. An objective of deriving such correlations is to define a universal relationship between liquid production rate and readily ascertained operational parameters, such as choke opening, pressure, and gas-liquid ratio. Currently, the most common empirical choke flow correlations used in industry are Gilbert (1954), Ros (1960), Achong (1961), and Baxendell & Thomas (1961). The correlation is usually expressed in a form:
In Equation (1), q represents liquid flow rate (e.g., in barrels/day), D represents choke size (e.g., in 1/64 inches), Pwh represents wellhead pressure (e.g., in absolute psi), R represents gas-liquid ratio (e.g., in standard cubic feet/barrel), and A1, A2, and A3 are static flow coefficients. For known uses of Equation (1) as set forth by Gilbert, Ros, Achong, and Baxendell & Thomas, these coefficients are fixed and specified as defined in Table 1.
All these pioneers have suggested a similar relationship between the operational parameters, but the fixed coefficients they proposed are different. However, a single correlation with a single set of fixed coefficients cannot fit over every single operational condition, therefore multiple correlations, each with a different set of fixed coefficients may be considered, which makes it difficult for engineers to decide the most applicable correlation and corresponding set of fixed coefficients for their particular oilfield or well. Due to enforced generalization by using fixed, static coefficients in the correlation and difficulty in selecting the most applicable correlation and coefficients, the known empirical choke flow correlations are not reliable when there is a need for accurate rate calculation.
Some embodiments overcome the above deficiencies and do not rely on a fixed set of coefficients in a correlation between liquid flow rate and operational parameters, and instead use dynamically adjusted coefficients. Some embodiments utilize recent technological developments in the field of data analytics, such as particle swarm optimization, to update coefficient values periodically, on demand, or upon the occurrence of specified events. Some embodiments provide a dynamic correlation with dynamically updatable coefficients to estimate the production rate of naturally flowing oil wells based on readily available operational parameters, namely, empirical wellhead parameters (choke size and wellhead pressure) and empirical gas-liquid ratio. Some embodiments eliminate the need to preselect static coefficients used in an empirical choke flow correlation, thus making the correlation dynamic, more data driven, and accurate.
These and other features and advantages are shown and described in detail in reference to the figures and tables herein.
Sensors (S), such as pressure gauges, gas and liquid flowmeters, etc. may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, the sensors (S) may be positioned at a wellhead, test header, in production tool 106d or associated equipment, such as Christmas tree 129, gathering network 146, surface facility 142, process facility, and/or the production facility, to measure fluid parameters, such as fluid composition (e.g., gas-liquid ratios), flow rates (e.g., liquid flow rates), pressures (e.g., wellhead pressures), temperatures, and/or other parameters of the production operation. Other sources of data may also be provided from offsite locations.
Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).
Computer facilities may be positioned at various locations about the oilfield 100 (e.g., the surface unit 134) and/or at remote locations. Surface unit 134 may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors (S). Surface unit 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit 134 may also collect data generated during the drilling operation and produce data output 135, which may then be stored or transmitted. Surface unit 134 may further collect and provide data obtained by sensors (S). Surface unit 134 may further provide data regarding a choke size and control a size of an adjustable choke, e.g., present at the wellhead.
Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations. Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100. Surface unit 134 may then send command signals to oilfield 100 in response to data received. Surface unit 134 may receive commands via transceiver 137 or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, injection volumes or rates, choke size, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.
The field configurations of
At the wellhead, when the produced fluid passes through the choke, its velocity increases at the cost of pressure energy. This decrease in pressure may be utilized to control the surface pressure and the production rate. A few standard empirical equations have been used to understand the relationship between fluid rates and wellhead parameters to estimate production rates. But the coefficients in these equations are static, generic, and cannot accurately handle the dynamic flow regime. Therefore, use of these equations usually results in an erroneous liquid flow determination. According to some embodiments, these limitations are overcome by using an adaptation of Particle Swarm Optimization (PSO). The novel technique, presented herein, modifies the coefficients of existing empirical equations to get the best fit on well-test data and thus derives a new empirical correlation by way of new coefficients for each provided well-test dataset. As a result, a unique relationship between operational parameters (namely, the wellhead parameters of pressure and choke size, as well as the gas-liquid ratio) and liquid production rate may be obtained for each well, and the derived relationship may be used to estimate daily flow rates of naturally flowing oil wells.
A reduction to practice has been validated using both simulated data (synthetic) and open-source field data (actual). In the case of PIPESIM simulated data, the reduction to practice predicted the rate closest to the PIPESIM generated rates when compared with the other standard empirical correlations. Whereas, on validating the reduction to practice with the field data of more than 1200 well-test data sets, the reduction to practice improved the correlation accuracy by an average of 25%. The reduction to practice is described herein throughout.
At 202, the method 200 includes obtaining a well-test data set. The well-test data set may include field measurements (e.g., empirical measurements) of a well-test flow rate and other well-test operational parameter values for at least one test well.
The well-test flow rate may be measured using a flowmeter, e.g., a multi-phase flowmeter, for example. In general, flowmeters are only used on a temporary basis and may be portable or fixed infrastructure.
Portable flowmeters may be incorporated into a mobile test unit. Periodically, for example, once per month, a portable flowmeter may be brought to a well and installed temporarily (e.g., for one day) to measure the actual liquid flow rate for the well.
Fixed infrastructure flowmeters, such as a test header, may be located at a facility to which the outputs of a plurality of wells flow by way of a gathering network. To test the actual liquid flow rate of a particular well, the flows from the other wells that feed into the facility may be temporarily (e.g., for one day) stopped or diverted to a different flowline, such that only the well that is being tested feeds into the fixed flowmeter.
The test well may be the same as, or different from, the target well. By way of non-limiting example, if no historical well-test data is available for the target well, the test well may be selected to be different from the target well, e.g., in the same or a different field. If historical well-test data is available for the target well, then the test well may be the target well. If the target well and the test well are the same, the field measurements may represent historical data for the test well measured, e.g., a week ago, a month ago, three months ago, or a year ago. Alternately, or in addition, the test well may be a different well in the same (or different) oil field as the target well, and the well-test data may be from any time period, e.g., as long as test well is a good representation of target well and test-data is valid.
In general, the well-test dataset may include well-test data from one, or a plurality, of wells, which may or may not include well test data from the target well. Further, the well-test dataset may include data that is obtained periodically (e.g., monthly, quarterly, yearly, etc.) and/or on demand.
In addition to the field measurements, the method 200 may accept and utilize other data. Thus, the method 200 may utilize any, or any combination, of the following:
In general, the various operational parameter values may be obtained at different times. The utilized data may undergo pre-processing, in which the data is collected. The collected data may be filtered for null/invalid values as part of the pre-processing.
The gas-liquid ratio may be determined using any of a variety of techniques at any of a number of locations. For example, the gas-liquid ratio may be determined using a separator test, e.g., at a process facility. For a separator test, a separator vessel may be used, the separated gas and liquid volumes may be measured, and the gas-liquid ratio is calculated as the ratio of gas volume to liquid volume. As another example, the gas-liquid ratio may be determined using a pressure/volume/temperature (PVT) analysis, e.g., performed at a laboratory. For a PVT analysis, the pressure, volume, and temperature of a well fluid sample may be measured, and the data used to determine fluid properties, including gas-liquid ratio. As yet another example, the gas-liquid ratio may be determined using a well test, e.g., at a test header. According to this example, liquid and gas flow meters may be used and the gas-liquid ratio calculated.
At 204, the method 200 includes performing, based on the well-test data set, a meta-heuristic estimation of a plurality of coefficients in a correlation, where the correlation correlates liquid flow rate with operational parameters. By way of non-limiting example, the correlation may be in the form of Equation (1), and the coefficients may include any, or any combination, of A1, A2, and/or A3. The meta-heuristic estimation may be a meta-heuristic optimization, such as a particle swarm optimization. As used herein, e.g., in the aforementioned context, the term “optimization” indicates improvement, rather than absolute superiority. For example, a particle swarm optimization may be used to obtain improved coefficient values, but may not obtain objectively or subjectively determined maximally superior values. By way of non-limiting example, 204 is described presently in reference to a novel adaptation of a particle swarm optimization.
The adapted particle swarm optimization provides an intelligence-based meta-heuristic optimization. The adapted particle swarm optimization solves for coefficient values in a multidimensional vector space. It can preserve information about the search space throughout all iterations and has faster convergence and balanced global-local search capacity than other meta-heuristic optimization algorithms, such as hill climbing, simulated annealing, tabu search. For example, the reduction to practice can produce flow rate estimates in about five seconds. Further, the adapted particle swarm optimization may utilize less data than is required by other techniques, e.g., the well-test data may include data that was collected once, or collected every three months for the past three or six months or so. The adapted particle swarm optimization utilizes a swarm (group of particles) based search in 3-D space, where the position of each particle is defined as a potential solution to the optimization problem. With every iteration, these particles move in a direction defined by a function of the particle's own experience and the collective behavior of the group (the swarm). Ultimately, some or all the particles may converge to point representing a global optima.
Optimization of correlation coefficients (e.g., A1, A2, A3 in Equation (1)) using the adapted particle swarm optimization may use well-test datasets, e.g., recent or the latest well-test data sets. The number of latest well-test records to consider, as well as other parameters of the adapted particle swarm optimization, can be configured as hyperparameter. An example set of such hyperparameters is shown below in Table 2, which represents a configuration file from the reduction to practice. Table 2 includes default values of hyperparameters (empirically selected) in bold; such hyperparameters may be adjusted as part of hyperparameter tuning.
The FILE_PATH in Table 2 represents a file path for the test well datasets. The SIMULATION_CYCLES represents the number of optimization runs to perform in each prediction. The TESTS_RANGE represents how many of the most recent well test datasets to use. The CRITIAL_FLOW_CONST represents a physical parameter relating to the condition of the well flow. The GILBERT_CONST, ROS_CONST, BAXENDELL_CONST, and ACHLONG_CONST represents particles with pre-initialized positions. The N_VAR represents the number of variables; here, three variables may be used, for A1, A2, and A3. The VAR_MIN and VAR_MAX represent allowable ranges for the variables, that is, they specify the search space. The MAX_ITERATIONS represents a maximum number of iterations to perform, e.g., if convergence has not occurred. The POP_SIZE represents the number of particles to use. The LOCAL_BEST_CONST and GLOBAL_BEST_CONST represent parameters that control how much a particle's local best known position and the best known position in the entire search space influence its movement in each iteration. The VELOCITY_CONST values represent particle inertia. The DAMPING_CONST parameter slows the particle's movement as the iteration limit is reached. The TOLERANCE_CONST helps to prevent the algorithm from continuing to search for a solution that is very close to the current best solution. It can affect the convergence speed and the quality of the solutions found by the algorithm. Finally, SHOW_GRAPH provides a visualization of particle movement within the search space during an iteration if set to True.
All of the hypermeters, such as population size, number of iterations, range of coefficients, etc., can be configured before the adapted particle swarm optimization is run. To ensure faster convergence, some of the particles may be initialized from known vector positions, such as those set forth in Table 1 (with coefficients A1, A2, and A3 in the x, y, and z axes respectively) instead of random initialization. (The values of A1 in Table 2 differ from those in Table 1 due to unit conversions.) Further, a user may add one or more additional particles that are initialized at specified vector positions, e.g., those from previously determined coefficients. Another adaptation that is done in incorporating the time factor in the optimization process, so that the latest well-test datasets have the highest influence on the optimized coefficients. This may ensure that results are more skewed towards the latest production conditions.
Table 3 contains pseudo code that describes the adapted swarm optimization as used in the reduction to practice.
The pseudocode shown in Table 3 depicts the time factor that weighs more recent well-test data more than less recent well-test data. This is shown in pseudocode bullet 5, “[e]valuate weighted average fitness (of N well-test fitness values) using polynomial function for each particle (highest weightings to latest well-test data).”
The pseudocode shown in Table 3 depicts that some of the particles may be initialized with specified vector positions, e.g., one or more corresponding to the coefficient values set forth in Table 1. Alternatively, or in addition, a user may specify one or more particles with specified initial vector positions. The ability to specify initial particle locations with specified vector positions is shown in the pseudocode bullet 2, “[i]nitialize special swarm of particles with constants (Gilbert, Roz, Baxendell, Achong, optional user-defines) as positions.”
Once the coefficients are estimated, e.g., using the adapted particle swarm optimization, the coefficients may be entered into a correlation, such as the correlation of Equation (1).
At 206, the method 200 includes measuring empirical operational parameter values for the target well. The measured operational parameter values may include a value for at least one of: a choke size for the target well, a wellhead pressure of the target well, and/or a gas-liquid ratio of the target well. The measurements may be conducted using equipment and techniques, e.g., as described herein in reference to 202 and/or
At 208, the method 200 includes determining a liquid flow rate value of the target well based on the empirical operational parameter values for the target well and using the correlation with the plurality of coefficients. For example, once the coefficients of Equation (1) are optimized on well-test datasets (training data) and the coefficients placed into Equation (1) as described in reference to 204, the operation datasets, that is, the measured empirical operational parameter values of 206 (e.g., wellhead pressure, choke size, gas-liquid ratio) may be entered into the resulting correlation to obtain the liquid rate q. The resultant rate may be referred to as the estimated liquid flow rate. Note that the estimated liquid flow rate is dynamic, in the sense that it is based on coefficients that are newly updated based on recent well-test data.
At 210, the method 200 includes providing the predicted liquid flow rate value. The value may be provided by display on a computer monitor, e.g., for asset monitoring, provided to oil well extraction equipment, stored in persistent memory, or sent over a network, by way of non-limiting example. The value may be provided to a DSC (Distributed Control System) to run another workflow, such as well performance monitoring, production optimization, or inventory planning. For example, the production optimization may include hardware that automatically adjusts a choke size, a pump stroke rate, gas injection rate, or a pump revolution rate.
At 212, the method 200 optionally includes validating the results. For example, for validation purposes, the method 200 may also predict the rates with the original coefficients presented in Table 1. The predicted rates (from Gilbert, Ros, Achong, Baxendell, and the adapted particle swarm optimization according to an embodiment) may be compared with actual rates from the field as measured with a flow meter. The actions of 212 may be performed, for example, if estimates according to an embodiment have not previously been performed for a particular oil field. If the results are not valid, then the embodiment may be re-executed before expanding the use of the predicted flow rate to the entire oil field, for example. Prior to re-execution, any of the following may be changed: (1) hyper-parameter tuning may be performed, e.g., by changing the default values shown Table 2, (2) the well-test data may be expanded, and/or (3) the target well may be designated as a well-test candidate to acquire new well-test data.
At 214, the method 200 includes determining that a liquid flow rate of the target well can be improved based on a past determination of the liquid flow rate value of the target well. For example, the determination may include comparing one or more past liquid flow rate for the target well, either empirically measured or determined using the method 200. If such comparison reveals that the current liquid flow rate is less than an expected liquid flow rate, then the determination may include that the liquid flow rate of the target well can be improved.
At 216, the method 200 includes increasing the liquid flow rate of the target well. The improvement may be accomplished by adjusting extraction equipment of the target well. For example, one or more of the following may be manually or automatically performed: adjusting a choke size of the target well, adjusting a gas injection amount or rate for the target well, adjusting a pump stroke rate for the target well (e.g., for an SRP well), and/or adjusting a pump revolution per minute for the target well (e.g., for a PCP well).
More generally, the method 200 may be used for any, or any combination, of: optimization, inventory, diagnosis, and/or analysis of well production. Optimization may include attempting to increase the liquid flow rate, e.g., as described herein in reference to 216. Inventory may include providing sufficient storage (e.g., storage tanks) and/or refinery resources (e.g., refinery throughput) for the oil produced by a well. Diagnosis may include determining whether a well's production is increasing, staying the same, or decreasing. Analysis may include performing a well test, which may include identifying a test well candidate using an embodiment as described herein.
The predicted flow rate associated with the optimized coefficients may be validated against user defined tolerance (e.g., a percentage). (Note that the defined tolerance discussed presently is different from the TOLERANCE_CONST of Table 2.) This validation may compare the difference between an actual well-test rate and a rate determined according to the embodiment to a predetermined set tolerance. The validation may proceed in stages.
A non-limiting example of such a staged validation with a TESTS_RANGE value of five well-test datasets is presented. If ten well-test datasets T1, T2, T3, T4, T5, T6, T7, T8, T9, T10 are available, enumerated from least recent to most recent, a first stage may use T1, T2, T3, T4, and T5 to estimate coefficients for the target well. These estimated coefficients may then be used to get a new correlation as per Equation (1) for the target well. With the obtained correlation and T6 data (choke size, gas liquid ratio, well head pressure), a liquid rate is predicted. The difference from such a rate to the actual rate of T6 may be compared to the tolerance. For the next stage, T2, T3, T4, T5 and T6 may be used to estimate a rate, and the difference between the estimated rate and the actual rate from T7 may be compared to the tolerance. The stages may continue as described.
If validation is successful (e.g., difference between the latest well-test rate and estimated rate is within the set tolerance) then the computed coefficients may be used in Equation (1), resulting in a new empirical correlation. Further, if the validation is successful, the estimation of production rate by using daily operational parameters may be stored in the database along with the computed coefficients.
If the validation is unsuccessful, e.g., the process fails to identify the best fit, then the well may get identified as a potential well-test candidate. Well name, last well-test date, and error may be stored in a database of all of the potential well-test candidates.
In general, a use of various embodiments may include identifying a well-test candidate. Typically, identifying a well-test candidate well from among a field of dozens or hundreds of wells presents a difficult problem for an engineer. Use of various embodiments may solve this problem, because embodiments may be used to identify a well for which a liquid flow rate estimated according to an embodiment significantly differs from its actual flow rate.
A description and evaluation of the results obtained from the reduction to practice follows.
On evaluating the reduction to practice using a public dataset that included more than 1200 production data points, the correlation of the reduction to practice was ˜25% more accurate than the most applicable standard empirical correlation (Gilbert). Table 4 compares the accuracy of the correlation used in the reduction to practice.
In general, embodiments may be integrated with production management systems and related products/services.
Certain examples can be performed using a computer program or set of programs. The computer programs can exist in a variety of forms both active and inactive. For example, the computer programs can exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats; firmware program(s), or hardware description language (HDL) files. Any of the above can be embodied on a transitory or non-transitory computer readable medium, which include storage devices and signals, in compressed or uncompressed form. Exemplary computer readable storage devices include conventional computer system RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), and magnetic or optical disks or tapes.
Aspects of the present disclosure 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 disclosure. 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 using computer readable program instructions that are executed by an electronic processor.
These computer readable program instructions may be provided to an electronic 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 electronic processor of the computer or other programmable data processing apparatus, create a machine that implements 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.
In embodiments, the computer readable program instructions 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 a user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
As used herein, the terms “A or B” and “A and/or B” are intended to encompass A, B, or {A and B}. Further, the terms “A, B, or C” and “A, B, and/or C” are intended to encompass single items, pairs of items, or all items, that is, all of: A, B, C, {A and B}, {A and C}, {B and C}, and {A and B and C}. The term “or” as used herein means “and/or.”
As used herein, language such as “at least one of X, Y, and Z,” “at least one of X, Y, or Z,” “at least one or more of X, Y, and Z,” “at least one or more of X, Y, or Z,” “at least one or more of X, Y, and/or Z,” or “at least one of X, Y, and/or Z,” is intended to be inclusive of both a single item (e.g., just X, or just Y, or just Z) and multiple items (e.g., {X and Y}, {X and Z}, {Y and Z}, or {X, Y, and Z}). The phrase “at least one of” and similar phrases are not intended to convey a requirement that each possible item must be present, although each possible item may be present.
The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] . . . ” or “step for [perform]ing [a function] . . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. § 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. § 112(f).
While the invention has been described with reference to the exemplary examples thereof, those skilled in the art will be able to make various modifications to the described examples without departing from the true spirit and scope. The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. In particular, although the method has been described by examples, the steps of the method can be performed in a different order than illustrated or simultaneously. Those skilled in the art will recognize that these and other variations are possible within the spirit and scope as defined in the following claims and their equivalents.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202311013162 | Feb 2023 | IN | national |
