Patent Application
20250237142
This disclosure generally relates to reservoir characterization in the context of geo-exploration for oil and gas.
Accurate reservoir characterization can be instrumental in developing, monitoring, and managing reservoir production. Characterizing a reservoir by updating both static and dynamic reservoir properties during the life of the field is referred to as dynamic reservoir characterization. A large portion of oil and gas field development is based on three-dimensional (3D) numerical simulation results. These 3D numerical simulation results can leverage a 3D geo-model that uses core and log data obtained from wells as inputs to create a prototype of the reservoir.
In one aspect, implementations provide a computer-implemented method that includes: accessing a full-field multi-well model for a reservoir containing a first plurality of wells each without no well-test event and a second plurality of wells each with a well-test event; importing, into the full-field multi-well model, historical flowrate records from both the first and second plurality of wells, and well-test results from the second plurality of wells; creating, in the full-field multi-well model, a local grid refinement in a vicinity of each well from the second plurality of wells; launching a first iteration of full-field simulation using the full-field multi-well model covering both the first and the second plurality of wells of the reservoir; using artificial intelligence to determine, for each well from the second plurality of wells, a first ratio of a simulated pressure derivative from results of the first iteration of full-field simulation and an observed pressure derivative from the well-test results; launching a second iteration of full-field simulation using the full-field multi-well model for the reservoir where the first ratio is used as a permeability multiplier in the vicinity of each well from the second plurality of wells such that the observed pressure derivative and the simulated pressure derivative become more matched; and responsive to meeting an objective of the full-field simulation, generating a report delineating re-distributed permeability in the reservoir based on combining the historical flowrate records—from the first and second plurality of wells—and the well-test results from the second plurality of wells corrected by respective permeability multipliers.
Implementations may include one or more of the following features.
The objective of the full-field simulation may include one of: a number of iterations, or a difference between the simulated pressure derivative and the observed pressure derivative. The vicinity may be defined by a radius configurable by an operator through a user interface. The vicinity of each well from the second plurality of wells may define a drainage area for a corresponding well. The simulated pressure derivative and the observed pressure derivative may be respective averages within a middle-time-region (MTR) of a corresponding pressure derivative curve. The MTR may be where the corresponding pressure derivative curve is characterized by an approximately constant level that varies within about 10%. Responsive to determining that the objective of the full-field simulation is not met, the method may calculate, for each well from the second plurality of wells, a second ratio of a simulated pressure derivative from results of the second iteration of full-field simulation and the observed pressure derivative from the well-test results. The method may further include: launching a third iteration of full-field simulation using the full-field multi-well model for the reservoir where the second ratio is multiplied with the first ratio to product a new permeability multiplier in the vicinity of each well from the second plurality of wells such that the observed pressure derivative and the simulated pressure derivative become more matched. The method may further include: planning a location of a new well in the reservoir based on, at least in part, the report generated using the full-field multi-well model. The method may further include: estimating oil production in the reservoir based on, at least in part, the report generated using the full-field multi-well model.
In another aspect, some implementations provide a computer system comprising one or more computer processors configured to perform operations of: accessing a full-field multi-well model for a reservoir containing a first plurality of wells each without no well-test event and a second plurality of wells each with a well-test event; importing, into the full-field multi-well model, historical flowrate records from both the first and second plurality of wells, and well-test results from the second plurality of wells; creating, in the full-field multi-well model, a local grid refinement in a vicinity of each well from the second plurality of wells; launching a first iteration of full-field simulation using the full-field multi-well model covering both the first and the second plurality of wells of the reservoir; using artificial intelligence to determine, for each well from the second plurality of wells, a first ratio of a simulated pressure derivative from results of the first iteration of full-field simulation and an observed pressure derivative from the well-test results; launching a second iteration of full-field simulation using the full-field multi-well model for the reservoir where the first ratio is used as a permeability multiplier in the vicinity of each well from the second plurality of wells such that the observed pressure derivative and the simulated pressure derivative become more matched; and responsive to meeting an objective of the full-field simulation, generating a report delineating re-distributed permeability in the reservoir based on combining the historical flowrate records—from the first and second plurality of wells—and the well-test results from the second plurality of wells corrected by respective permeability multipliers.
Implementations may include one or more of the following features.
The objective of the full-field simulation may include one of: a number of iterations, or a difference between the simulated pressure derivative and the observed pressure derivative. The vicinity may be defined by a radius configurable by an operator through a user interface. The vicinity of each well from the second plurality of wells may define a drainage area for a corresponding well. The simulated pressure derivative and the observed pressure derivative may be respective averages within a middle-time-region (MTR) of a corresponding pressure derivative curve. The MTR may be where the corresponding pressure derivative curve is characterized by an approximately constant level that varies within about 10%. Responsive to determining that the objective of the full-field simulation is not met, the operations may include: calculating, for each well from the second plurality of wells, a second ratio of a simulated pressure derivative from results of the second iteration of full-field simulation and the observed pressure derivative from the well-test results. The operations may further include: launching a third iteration of full-field simulation using the full-field multi-well model for the reservoir where the second ratio is multiplied with the first ratio to product a new permeability multiplier in the vicinity of each well from the second plurality of wells such that the observed pressure derivative and the simulated pressure derivative become more matched. The method may further include: planning a location of a new well in the reservoir based on, at least in part, the report generated using the full-field multi-well model. The method may further include: estimating oil production in the reservoir based on, at least in part, the report generated using the full-field multi-well model.
Implementations according to the present disclosure may be realized in computer implemented methods, hardware computing systems, and tangible computer readable media. For example, a system of one or more computers can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.
The details of one or more implementations of the subject matter of this specification are set forth in the description, the claims, and the accompanying drawings. Other features, aspects, and advantages of the subject matter will become apparent from the description, the claims, and the accompanying drawings.
Like reference numbers and designations in the various drawings indicate like elements.
During geophysical explorations, reservoir modelling is generally involved to, for example, characterize a reservoir and predict outputs. For an integrated reservoir study, the geological model may be constructed from limited cored wells. The porosity and permeability measured from these limited cored wells may then be extrapolated statistically or probabilistically across the grid-blocks in the model. In certain instances, the non-cored wells where cored data had not been taken have well-test data based on which the permeability-thickness (kh) measurements can be derived, which can differ from kh values predicted by the geological modeling process at the location of the non-cored wells. This process of modifying the static model's permeability field in the vicinity of wells that have well-test data giving rise to kh measurements is known as kh-conditioning. Here, kh refers to the permeability-thickness product, in md-ft, where h is a pay thickness in feet, and k is permeability in the horizontal direction in milli Darcy (md).
During the traditional approach of kh-conditioning, the kh interpreted from well-test is compared to the kh within a representative volume of the static model, and a permeability multiplier is applied to the model as and where required. Such an approach is associated with several limitations. First, although well test interpretation provides an interpretation of radius of investigation, the geometry of the investigated volume is not necessarily radial, but rather depends on the reservoir heterogeneity arrangements. As a result, the actual geometry of the investigated volume remains unknown. For this reason, uncertainty persists during the step of defining the representative equivalent volume of the model within which model kh is to be averaged. Second, after a representative equivalent volume has been defined, the question remains as to the type ‘averaging’ (arithmetic, harmonic or geometric) to be performed in order to determine the model-based kh for comparison with kh measurements derived from physical well-test data. Third, well-test kh is an average value derived from dynamic conditions, which factors in not just the magnitudes of the permeability heterogeneities, but also the arrangement of the heterogeneities. As a result, the kh calculated from the geological model using traditional methods may not be suitable for direct comparison to that derived from well-test data.
In the novel methodology for kh conditioning described in this disclosure, the implementations incorporate a full-field simulator equipped with artificial intelligence for automatic multi-well kh-conditioning. The simulator automatically determines the middle time region (MTR) section of each well's pressure derivative plot based on the measured well-test data and calculates the permeability multiplier factor, which can be used to condition the model permeability according to the well-test permeability to ensure similar kh between the observed MTR derivative and simulated MTR derivative by modifying the geological model permeability around each well in the reservoir that has well-test event. Here, permeability-thickness (kh) conditioning is the process of modifying a geological model's permeability field so that the model's kh around certain wells that have historical well-test data can become similar to the well-test derived kh of the wells. Similarity of kh can be implied when the Middle Time Region (MTR) of the derivative of historical and simulated well-test pressures transient have similar magnitude. Accordingly, the implementations can interpret the kh measurements from actual well-test data and then compare the derived kh measurements with the kh values predicted by the geological modeling process at the location of the wells having well-test records. As a result, the implementations can obtain a permeability correction factor, which can be used to condition the model permeability to the well-test permeability.
Significantly, kh conditioning improves the prediction quality of infill wells. In large reservoirs with test data from hundreds of wells, thorough kh-conditioning to well test data can be technically challenging in terms of computation. By virtue of the permeability correction approach presented in the present disclosure, implementations can more realistically calibrate the full field, rather than being limited to the locations of the cored wells in the field. In other words, implementations can leverage the kh multipliers at wells that have well test events to extrapolate positions elsewhere in the field to provide a more realistic rendering of the full field using the new geological model. The salient features are similar to improved computerized animation. Moreover, the data-driven computational aspects entail voluminous data obtained from a vast geophysical exploration site. Indeed, the implementations are not limited by, for example, an upper bound of wells at the geophysical site. In fact, the technical improvements scale up with the number of wells at the geophysical exploration site. This scale-up aspect is another hallmark of the technical improvement directed to the underlying computerized technology. More details are provided below, in association with
Core Data can include core samples taken out of actual reservoir formations under in-situ conditions during drilling phase of the wells, which can provide valuable data on reservoirs and fluids. Core data may only be collected in a few wells depending upon the objectives. Core data samples can be transferred to a laboratory for detailed analyses. When available, core data can provide more reliable reservoir fluid properties than petrophysical log data. In some cases, core data can be used to adjust or calibrate log data. This may be done because core data can be considered more reliable than the log data. In cases in which core data is not available, techniques can rely on petrophysical log data. If core data in offset wells is available, then the core data can also be used for enhancing reservoir descriptions.
Geology and Geophysics Data can be collected from the field seismic survey. Collected seismic field data can be input into the workflow where the data can be analyzed and interpreted to derive geological structures, rock typing, and reservoir features (including fractures, faults, and unconformity) of the reservoir. As the seismic data has the capability of capturing only large features in the field or the reservoir, localized geological features may be missed, such as fractures, faults, and unconformity. Based on the shape of the reservoirs, structural maps (for example, contour maps) can be generated by using depth scales. By using contour maps along with seismic interpretation, rock typing can be determined. Reservoir structures as interpreted from seismic data can be incorporated in numerical models if structural contour maps are available from seismic data.
An Operational Platform can serve as a computer-aided enabler in performing specific operations on a sector model that is regarded as an operational platform. Such a platform can execute requests for visualization of, and computational operations on, uploaded models. The operational platform can also display input parameters and field data, compute model outputs, and compare model outputs to field data. The operational platform can also have the capability of simplifying well trajectories, production data, and injection data to reduce the computational burden. Manipulation of grids, including upscaling and refining as needed, can also be performed on sector models.
Petrophysics can refer to reservoir properties (for example, permeability, porosity, saturations, and pay thickness) originating from petrophysical log data to build static geological models. Petrophysical logs can be built during the drilling phase of the well. Logging tools can be run in-hole. Wellbore, rock, and fluid information can be collected, which can later be processed and analyzed to estimate detailed reservoir properties such as permeability, porosity, saturations, and thickness. Petrophysical logs can provide the resolution needed to pick up localized features in the well or in the vicinity of the well. Logs can be the primary sources of most important and reliable data, providing a detailed description of the rock, fluid, and well. This information can be input to static geological models. In case a given subject well does not have petrophysical information, modelers can turn to other offset wells for petrophysical data for building the models.
PVT Data includes pressure, volume, and temperature data, which serve as reservoir fluid properties. A PVT analysis can include the process of determining the fluid behaviors and properties of oil, water, and gas samples from a reference well. Fluid samples for PVT analyses can be collected from a well during a drilling phase or a production phase of the well. The PVT data can also help in defining the phase behavior of reservoir fluids. Formation volume factors, viscosity, gas gravity, gas-oil ratio, and water salinity data can be used in a dynamic reservoir model. The PVT data use can be based on the number of phases (for example, two or three phases) in the reservoir.
A Reference Point is a depth at which all gauges are set to measure pressure data. The pressure at the reference point (for example, the gauge depth of the pressure measurement) can be required to initialize and simulate the pressure transient data in the transient model. Models can calculate simulated pressures at the reference point.
Relative Permeability refers to a concept used to enforce a preferential level of flow capacity due to the presence of multiple fluids at a given location in the reservoir. Relative Permeability can depend upon pore geometry, wettability, fluid distribution, and fluid saturation history. Relative permeability measurements can be conducted on core samples in a laboratory. Relative permeability measurements can be both time-consuming and expensive to produce.
As an example, in a single-phase fluid system, such as a dry gas or an under-saturated oil reservoir, the effective permeability of flow of the mobile fluid through the reservoir may vary a little during production because the fluid saturations do not change much. However, when more than one phase is mobile, the effective permeability to each mobile phase can change as the saturations of the fluids change in the reservoir. In the multiphase flow of fluids through porous media, the relative permeability of a phase can be a dimensionless measure of the effective permeability of that phase. The relative permeability can be represented as the ratio of the effective permeability of that phase to the absolute permeability. Relative permeability can be required for the calculation of permeability in each phase.
Reservoir Initial Conditions refer to the conditions when a well was drilled or before the well was subjected to any production or injection. The pressure and temperature data collected at that time is called the initial pressure and temperature of the reservoir. In addition, depths of the oil-water contact (OWC) and the gas-oil contact (GOC) need to be captured as well. These initial conditions can be utilized to build a hydro-dynamically balanced version of the transient model before the production and injection occur.
Well Control, Pressure-Transient Data, and Production Rates, when used in executing transient modeling, help to define well data in the well. In well control parameters, well history with reference to transient time can be defined. The production or injection history in different phases (for example, oil, water, or gas) separately can also be defined. The production or injection history can be required to match the pressure-transient data. Information for all flow, buildup, and fall-off periods of the wells can be defined in the data. Transient data of the measured pressures and production rates can be input into the transient model so that the information can be matched with the corresponding model predictions during simulation runs. The transient data of the measured pressures and the production rates can also help to accommodate any constraints. The constraints can be used, for example, to assure that well production rates and pressures do not go below or exceed certain limits during production or the shut-in phase. Constraints can be optional.
A Pressure Transient Analysis (PTA) well-test, also known as pressure transient testing or well testing, is a method used in reservoir engineering to evaluate the properties of a reservoir and assess the performance of a well. PTA involves measuring pressure changes in the wellbore or reservoir over time in response to controlled variations in production or injection rates. PTA provides valuable information about reservoir characteristics, including permeability, reservoir pressure, skin, and other parameters.
Well Trajectory and Completion Data includes a well trajectory defining the well path along which the well is drilled in a reservoir. In the past, wells were drilled vertically into the ground, and the well trajectory was essentially a straight, vertical line. In current operations, wells can be drilled so that the well trajectory can be horizontal, deviated, and curved. A numerical model can be used to capture the actual well trajectory. However, complex well trajectories may be numerically expensive for generating simulated pressures.
Well Completion is the process of making a well hydraulically connected to the intersecting reservoir to facilitate production or injection. Well completion principally involves preparing the bottom of a hole to required specifications, and running in the production tubing. Well completion is associated with downhole tools, including perforating and stimulating as required. Types of perforation can be based on the type of completion, for example, open-hole or cased-hole completions.
A Geological (Static) Model is a geological model that can be built using all static data (including geology, geophysics, petrophysical, fluid contacts, and core data) that provide characteristics of reservoir properties. The geological model also includes drilled wells with their trajectories. The geological model is the first step in modeling any field, and is usually built for the full field before being converted to a full-field dynamic simulation model. The geological model usually does not include dynamic data.
Infinite Acting Radial Flow (IARF) regime, refers to, in the context of reservoirs, fluid flow from the reservoir toward a wellbore where the reservoir is sufficiently large or the flow rates are sufficiently slow that the effects of the reservoir's outer boundary on the well's pressure transient are not yet felt. In other words, the reservoir is behaving as if it extends infinitely in all directions.
By way of additional illustration, the Early Time Region (ETR) may not be significant for the purpose of kh-conditioning. Rather, the focus of kh-conditioning is directed to the middle time region of the derivative plot, also known as the IARF regime or MTR which is characterized by the substantially zero slope in
The implementations of the present disclosure generally focus on the MTR region where the effects of the reservoir's outer boundary on the well's pressure transient are small enough so that the effects are negligible. Notably, in reservoir studies comprising of tens or hundreds of pressure transient surveys, a manual single-well approach become inefficient, cumbersome for the engineer and errors due to fatigue are more likely. This is because, in the manual, single well approach, each well test is simulated as if the single well was the only well in the field when well-test data was acquired. For wells tested after several years of production, only the relevant test period is used instead of the well's full production history. One limitation of this practice is that the calibration process does not incorporate the interference effect. Such omission may mislead the engineer to calibrate an LTR dip in derivative (for example, the dip in
Implementations can perform a well-testing process by imposing a flow-rate sequence on a well (e.g., the test well) and using down-hole gauges to record the corresponding pressure transient response.
First, the implementations may operate from an initial full-field simulation model covering all wells in the reservoir and the corresponding historical production data including the daily record of such information as flow rate, volume, pressure, temperature, formation volume factors, viscosity, gas gravity, gas-oil ratio, and water salinity data.
Second, the implementations may pass the dates and well-test flowrate history for each tested well as input to the full-field simulator. For example, the implementations may inject these well-test flowrates into the appropriate sections of the full-field simulator's configuration for time-step control and rate constraint. The efficacy of the implementations has been demonstrated on a reservoir that has been producing from January 1962 till date with a total of over 100 producers (e.g., producing wells) coming up at different times. One of the wells which started producing in February 2014 has a well-test event conducted over a 20 hrs period on Oct. 14, 2019, when well-test flow profile was generated. According to some implementations, the full-field simulator can incorporate the well-test date as the simulator's time-steps with the provided well-test flow rate profile.
Third, the implementations may then automatically create Local Grid refinement (LGR) for the simulator around the drainage regions of all tested wells so that the quality of the subsequent pressure transient simulation can be improved on the refined grid.
Fourth, the simulator runs in full-field mode with all the wells in compliance with the refined time-step control at the dates of every input well-test event.
Fifth, at the end of the full-field simulation run, the simulator calculates the diagnostic derivative plot for each tested well during the buildup test period as well as the diagnostic derivative plot for the historical buildup well-test data. In this implementation, derivative plots refers to derivative pressure plot as presented in
Sixth, the full-field simulator scans the observed derivative pressure curve for the MTR region where the data values remain approximately constant, for example, within ±10%. The full-field simulation then records this average constant value (mo), The simulator also scan the simulated derivative in similar fashion and record the average constant value (ms) for the simulated curve. The subscript “o” stands for observed and “s” for simulated.
Seventh, the implementations may then use the ratio of the simulated derivative to the observed derivative (ms/mo) as the permeability multiplier factor in the next iteration.
Lastly, the simulator automatically creates a new instance for the next iteration of simulation and defines the calculated permeability multiplier factor for each well-test around the drainage region of corresponding the tested well based on the multiplier calculated for each tested well in the above seven steps.
On completion of each simulation run, the full-field simulator calculates the new ratio of the simulated derivative to the observed derivative according to descriptions above for under the sixth and seventh steps, and uses the product of this new ratio and the ratio used in the preceding iteration as the permeability multiplier for the next iteration. Additional simulation iterations can be created until the total number of simulation iterations reaches the user input maximum number of iterations. At the end of maximum iteration, a report is created containing the permeability multiplier factor required for calibrating the geological model kh at the vicinity of each tested well to its observed well-test kh.
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In various implementations, the near-well permeability multiplier factors obtained during the above-described conditioning process may then be used to refine the global geological model. In a typical reservoir, the number of cored wells tend to be a small fraction of the total wells. The reservoir's geological model may be therefore constructed from limited cored wells. By way of illustration, porosity and permeability in intra-well locations may be based on probabilistic/statistical estimates. In the areas of the geological model with no cored wells, other wells may exist on which well-tests have been conducted. The methodology according to some implementations of the present disclosure provides the determination of permeability correction factor that would allow the current geological model kh to be conditioned to the kh from the well-test data. A conditioned model occurs when the middle-time-region (MTR) derivative of observed well-test overlies the MTR derivative of simulated pressure derived from the numerical simulation of the well-test event.
A salient objective of a well-test is to determine the permeability around a well, among other uses.
The implementations may then construct a new geological model where, instead of using only the original 10 cored wells for permeability distribution, 18 wells are being used, thereby providing improved spatial coverage in the reservoir area under investigation. While the example shows the addition of 8 corrected permeability logs from the conditioning process, additional control points can be used in creating the geological model to produce an even more reliable model and provide a more realistic rendering of the potentially evolving geophysical status of full-field of the reservoir over time and with confidence. Such reliable geological model can used for planning location of new wells and for estimation of oil in a reservoir where the projection results can be disclosed to regulatory agencies,
The process may then import, into the model, historical rates from all wells and in addition, available well-test results (1002). Notably, well-test can also be done in cored wells, though not often. Because coring and subsequent interpretations are expensive, it is done only in a few wells. The permeability at other wells are then accessed by cheaper well test interpretation. Also note that not all non-cored wells get to have well-test conducted. Therefore, the division of the wells is not about ‘cored and non-cored’ rather about ‘wells with and wells without well-test events.’ As explained above in association with
The process may then create local grid refinement around drainage areas of wells having well-test events in order to improve the quality of the simulated pressure transient that would be generated in response to the input well test event (1003). For example, the simulator can automatically create local grid refinement (LGR) around the drainage regions of all tested wells to improve the quality of the subsequent pressure transient simulation. Significantly, the process automatically identifies the well of interest (e.g., a well that has a well test event), and creates LGR around these identified wells within an automatically derived drainage area specific to each identified well. The drainage area can be configured as a distance away from the tested well where the change in grid-block pressure across the well immediately before shut-in is less than 0.1 psi, as illustrated in the example provided by
The process may then conduct a full-field simulation for the reservoir covering both tested and non-tested wells (1004). For example, the full-field simulation can execute embedded python scripts that calculate, for every well having well-test data, observed pressure derivative of as well as the simulated pressure derivative, e.g., using measurements from the wells having well test events.
The process then determine a ratio of the simulated pressure derivative and the observed pressure derivative in a middle-time-region (MTR) for the drainage area of each wells having well-test event (1005). As explained above in association with
The process may then start a new full-field simulation for the reservoir by applying a correction based on the above-identified ratio to the drainage area of each well with well-test records (1006). For example, the full-field simulator can automatically create a new instance for the next iteration of simulation and define the calculated permeability multiplier factor for each well-test around the drainage region of the corresponding tested well based on the multiplier calculated for each tested well. In some cases, the surrounding region can be the vicinity with a radius of 1 km around the well's location. In these cases, the radius may be configured by an operating engineer.
The process may then determine whether the maximum number of simulations have been reached (1007). In response to determining that the maximum number of simulations have been reached have not been reached, the implementations may revert to block 1005 by determining a ratio for the next iteration. In response to determining that the maximum number of simulations have been reached, the process may generate a report showing the permeability correction factor needed to condition all wells. The implementations may apply the correction factor obtained at each tested well to multiply with the permeability log (as predicted at the well location by the wells with no well-test events) and then re-distribute permeability in the geological model using the combination of the tested-wells' corrected permeability logs and historical production records of all wells. Additionally or alternatively, the implementation may use other termination indicators, for example, when the middle-time-region (MTR) pressure derivative of observed well-test substantially overlies (e.g., matching with a least squared error of less than 10%) the MTR pressure derivative of simulated pressure derived from the numerical simulation of the well-test event.
During numerical well-test simulation, traditional tools depend on single-well within sector geological models. A sector geological model means that only the section of the model near the reference well can be simulated. Other grid-blocks of the model are generally deleted. In certain cases, numerical well-testing result may be different depending on the size of the sector model chosen. Moreover, the absence of other wells does not allow to incorporate the impact of inter-well interference in the well-test match of the reference well.
In contrast, the implementations can simulate reservoir performance using a full-field model, which factors in the presence of all other wells in the reservoir to incorporate likely inter-well interference. The implementations obviate the need for sensitivity cases on the size of sector model, thereby saving considerable computational time.
To be more specific, in cases where hundreds of wells are available with well-test data, traditional tools generally entail a unique sector model for each well, resulting in hundreds of single-well sector models. Additionally, each single-well model would lead to several iterations in order to calibrate the model kh around the well to its well-test kh. Thus, when faced with 200 wells having well-test data, at least six-hundred single-well sector models may be needed in order to obtain permeability correction factors (assuming three iterations per single-well sector model). Such scaled-up computational task can take several weeks to complete. Moreover, the combined effect of sectorization and property upscale may lead to a different kh-calibration result than would be obtain in the un-sectorized, non-upscaled model, which can result in human involvement to iteratively modify the sector model permeability until the model kh is conditioned to well-test kh. In cases where the simulation study time is short, then only a few of the well-test data may be incorporated in the study's dynamic model, which may not improve the quality of the results.
Using implementations of the present disclosure, regardless of the number of wells having well-test data, approximately only three model iterations may be needed to obtain permeability correction factors for all associated well-tests with sufficient quality. Once the first model is launched, subsequent models are automatically created and submitted by the simulator and a final report generated in matters of days for incorporation into the study's dynamic model.
In the context of geological modeling, a reliable simulation model to assess volumetric assets and plan the drilling of wells especially in a mature field may require the incorporation of well-test data to complement and sometimes refine the geological model's permeability field which had been built purely on limited cored wells' data. Because of the manual, and tedious nature of traditional kh-conditioning tools, this refinement has been impractical. Using implementations of the present disclosure, the available well-test data are incorporated and the permeability-thickness (kh) match is done automatically, leading to a more reliable simulation model being constructed for more realistic rendering of the reservoir. The implementations thus can provide an application or tool using a full-field model capable of incorporating multi-well well-test data to calculate pressure derivatives, determine the MTR derivative values and compare between observed and simulated MTR derivatives, then calculate the permeability multiplier factor and automatically generate a new instance of simulation run by introducing the calculated factor as a permeability multiplier around applicable wells and submit the new simulation case. The methodology can enforce rapid matching of the kh condition of well-test data into geological models without significant interaction with a human operator. The process can be on auto-mode using a script so that the process can run through the night-hours in the absence of a human engineer, thereby not just saving the engineers official work-hours, but additionally gaining more hours of work during non-official working hours. Indeed, the computation can also be scheduled around off-peak hours when, e.g., the computing server (e.g., at a data center) is less loaded. As detailed above, the implementations may not require model mutilation (e.g., upscaling and sectorization). Instead, the conditioning process involves iteratively applying a multiplicator factor which is calculated, rather than trial-and-error. Using scripts to automate the iterative process using the light-weight correction factor from one iteration to the next, the implementation can achieve significant savings in computation time and memory usage, while achieving more realistic rendering of computer-generated simulation of the production field of a reservoir.
Examples of field operations 1110 include surveying operations, forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 1110. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operations 1110 and responsively triggering the field operations 1110 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 1110. Alternatively or in addition, the field operations 1110 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 1110 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.
Examples of computational operations 1112 include one or more computer systems 1120 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. A more detailed example can be found in
In some implementations, one or more outputs 1122 generated by the one or more computer systems 1120 can be provided as feedback/input to the field operations 1110 (either as direct input or stored in the databases 1118). The field operations 1110 can use the feedback/input to control physical components used to perform the field operations 1110 in the real world.
For example, the computational operations 1112 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 1112 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 1112 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.
The one or more computer systems 1120 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 1112 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 1112 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 1112 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.
In some implementations of the computational operations 1112, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.
The presented information can include feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction.
In some implementations, the feedback can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.
The computer 1202 can serve in a role in a computer system as a client, network component, a server, a database or another persistency, another role, or a combination of roles for performing the subject matter described in the present disclosure. The illustrated computer 1202 is communicably coupled with a network 1230. In some implementations, one or more components of the computer 1202 can be configured to operate within an environment, including cloud-computing-based, local, global, another environment, or a combination of environments.
The computer 1202 is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer 1202 can also include or be communicably coupled with a server, including an application server, e-mail server, web server, caching server, streaming data server, another server, or a combination of servers.
The computer 1202 can receive requests over network 1230 (for example, from a client software application executing on another computer 1202) and respond to the received requests by processing the received requests using a software application or a combination of software applications. In addition, requests can also be sent to the computer 1202 from internal users, external or third-parties, or other entities, individuals, systems, or computers.
Each of the components of the computer 1202 can communicate using a system bus 1203. In some implementations, any or all of the components of the computer 1202, including hardware, software, or a combination of hardware and software, can interface over the system bus 1203 using an application programming interface (API) 1212, a service layer 1213, or a combination of the API 1212 and service layer 1213. The API 1212 can include specifications for routines, data structures, and object classes. The API 1212 can be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer 1213 provides software services to the computer 1202 or other components (whether illustrated or not) that are communicably coupled to the computer 1202. The functionality of the computer 1202 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 1213, provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, another computing language, or a combination of computing languages providing data in extensible markup language (XML) format, another format, or a combination of formats. While illustrated as an integrated component of the computer 1202, alternative implementations can illustrate the API 1212 or the service layer 1213 as stand-alone components in relation to other components of the computer 1202 or other components (whether illustrated or not) that are communicably coupled to the computer 1202. Moreover, any or all parts of the API 1212 or the service layer 1213 can be implemented as a child or a sub-module of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.
The computer 1202 includes an interface 1204. Although illustrated as a single interface 1204 in
The computer 1202 includes a processor 1205. Although illustrated as a single processor 1205 in
The computer 1202 also includes a database 1206 that can hold data for the computer 1202, another component communicatively linked to the network 1230 (whether illustrated or not), or a combination of the computer 1202 and another component. For example, database 1206 can be an in-memory, conventional, or another type of database storing data consistent with the present disclosure. In some implementations, database 1206 can be a combination of two or more different database types (for example, a hybrid in-memory and conventional database) according to particular needs, desires, or particular implementations of the computer 1202 and the described functionality. Although illustrated as a single database 1206 in
The computer 1202 also includes a memory 1207 that can hold data for the computer 1202, another component or components communicatively linked to the network 1230 (whether illustrated or not), or a combination of the computer 1202 and another component. Memory 1207 can store any data consistent with the present disclosure. In some implementations, memory 1207 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer 1202 and the described functionality. Although illustrated as a single memory 1207 in
The application 1208 is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer 1202, particularly with respect to functionality described in the present disclosure. For example, application 1208 can serve as one or more components, modules, or applications. Further, although illustrated as a single application 1208, the application 1208 can be implemented as multiple applications 1208 on the computer 1202. In addition, although illustrated as integral to the computer 1202, in alternative implementations, the application 1208 can be external to the computer 1202.
The computer 1202 can also include a power supply 1214. The power supply 1214 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable. In some implementations, the power supply 1214 can include power-conversion or management circuits (including recharging, standby, or another power management functionality). In some implementations, the power-supply 1214 can include a power plug to allow the computer 1202 to be plugged into a wall socket or another power source to, for example, power the computer 1202 or recharge a rechargeable battery.
There can be any number of computers 1202 associated with, or external to, a computer system containing computer 1202, each computer 1202 communicating over network 1230. Further, the term “client,” “user,” or other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer 1202, or that one user can use multiple computers 1202.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal, for example, a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums. Configuring one or more computers means that the one or more computers have installed hardware, firmware, or software (or combinations of hardware, firmware, and software) so that when the software is executed by the one or more computers, particular computing operations are performed.
The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),” “near(ly) real-time (NRT),” “quasi real-time,” or similar terms (as understood by one of ordinary skill in the art), means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.
The terms “data processing apparatus,” “computer,” or “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware and encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also be, or further include special purpose logic circuitry, for example, a central processing unit (CPU), an FPGA (field programmable gate array), or an ASIC (application-specific integrated circuit). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with an operating system of some type, for example LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, another operating system, or a combination of operating systems.
A computer program, which can also be referred to or described as a program, software, a software application, a unit, a module, a software module, a script, code, or other component can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including, for example, as a stand-alone program, module, component, or subroutine, for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, for example, files that store one or more modules, sub-programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
While portions of the programs illustrated in the various figures can be illustrated as individual components, such as units or modules, that implement described features and functionality using various objects, methods, or other processes, the programs can instead include a number of sub-units, sub-modules, third-party services, components, libraries, and other components, as appropriate. Conversely, the features and functionality of various components can be combined into single components, as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
Described methods, processes, or logic flows represent one or more examples of functionality consistent with the present disclosure and are not intended to limit the disclosure to the described or illustrated implementations, but to be accorded the widest scope consistent with described principles and features. The described methods, processes, or logic flows can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output data. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computers for the execution of a computer program can be based on general or special purpose microprocessors, both, or another type of CPU. Generally, a CPU will receive instructions and data from and write to a memory. The essential elements of a computer are a CPU, for performing or executing instructions, and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to, receive data from or transfer data to, or both, one or more mass storage devices for storing data, for example, magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable memory storage device.
Non-transitory computer-readable media for storing computer program instructions and data can include all forms of media and memory devices, magnetic devices, magneto optical disks, and optical memory device. Memory devices include semiconductor memory devices, for example, random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Magnetic devices include, for example, tape, cartridges, cassettes, internal/removable disks. Optical memory devices include, for example, digital video disc (DVD), CD-ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and other optical memory technologies. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories storing dynamic information, or other appropriate information including any parameters, variables, algorithms, instructions, rules, constraints, or references. Additionally, the memory can include other appropriate data, such as logs, policies, security or access data, or reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, for example, a CRT (cathode ray tube), LCD (liquid crystal display), LED (Light Emitting Diode), or plasma monitor, for displaying information to the user and a keyboard and a pointing device, for example, a mouse, trackball, or trackpad by which the user can provide input to the computer. Input can also be provided to the computer using a touchscreen, such as a tablet computer surface with pressure sensitivity, a multi-touch screen using capacitive or electric sensing, or another type of touchscreen. Other types of devices can be used to interact with the user. For example, feedback provided to the user can be any form of sensory feedback. Input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with the user by sending documents to and receiving documents from a client computing device that is used by the user.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server, or that includes a front-end component, for example, a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication), for example, a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) using, for example, 802.11 a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or other protocols consistent with the present disclosure), all or a portion of the Internet, another communication network, or a combination of communication networks. The communication network can communicate with, for example, Internet Protocol (IP) packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice, video, data, or other information between networks addresses.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what can be claimed, but rather as descriptions of features that can be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any sub-combination. Moreover, although previously described features can be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination can be directed to a sub-combination or variation of a sub-combination.
Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations can be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) can be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
