Method and apparatus for long-term productivity forecasting of shale gas well, and device
By using fracturing simulation and Markov chain-Monte Carlo sampling methods, the timeliness and accuracy issues of long-term shale gas well production prediction were resolved, enabling accurate production prediction for wells not yet in production and improving resource utilization efficiency.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- PETROCHINA CO LTD
- Filing Date
- 2025-10-21
- Publication Date
- 2026-05-15
AI Technical Summary
In existing technologies, long-term production capacity prediction methods for shale gas wells rely on production dynamics fitting, which has low timeliness and cannot provide timely guidance during fracturing operations, resulting in insufficient resource utilization and inadequate prediction accuracy.
Using fracturing simulation and Markov chain-Monte Carlo sampling method, combined with formation parameters and the utilization rate of wells already in production, we calculate the shale gas reservoir reserves and long-term production capacity predictions of wells not yet in production. We obtain fracture parameters through fracturing simulation, construct the posterior distribution of single-well utilization rate, and quantify the target single-well utilization rate.
It enables accurate calculation of long-term production capacity forecasts for undeveloped wells, improves the overall efficiency of shale gas development, and makes full use of well-controlled resources.
Smart Images
Figure CN2025128957_15052026_PF_FP_ABST
Abstract
Description
A method, apparatus and equipment for predicting the long-term production capacity of shale gas wells.
[0001] Related applications
[0002] This application claims priority to Chinese Patent Application No. 202411586786.X, filed on November 7, 2024, and incorporates the entire contents of the aforementioned patent application as part of this application. Technical Field
[0003] This disclosure relates to the field of shale gas well development technology, and particularly to a method, apparatus, and equipment for predicting the long-term production capacity of shale gas wells. Background Technology
[0004] Shale gas is a man-made gas reservoir. Due to the extremely dense reservoir, industrial gas flow cannot be formed naturally. Large-scale hydraulic fracturing is necessary to create an artificial fracture network to establish gas flow channels. The production capacity of shale gas wells is closely related to the artificial fracture network and its complexity. The assessment and quantification of fracture morphology and controlled fracture volume are core aspects of post-fracturing effect evaluation. However, current methods for assessing artificial fractures remain very limited. Hydraulic fracturing simulation, as a commonly used numerical calculation method, can obtain fracture morphology information by considering various geological and engineering conditions and parameters. Based on dynamic numerical simulation of fracturing, the complexity of fracture morphology can be characterized in some shale gas reservoirs containing numerous natural fractures.
[0005] Hydraulic fracturing is a complex process involving fluid flow and rock deformation, encompassing fluid flow within the wellbore and fractures, fracture formation and propagation, and the interaction between fractures and natural weak surfaces. In recent years, scholars both domestically and internationally have conducted extensive research on simulating the propagation of complex fractures in unconventional oil and gas systems. While existing models using the finite element method (FEM) consider fracture propagation, they still have limitations in accurately simulating the mechanical mechanisms and dynamic growth of fractures. The extended finite element method (EFB) and boundary element method (BEM) each have their own advantages and disadvantages in fracture simulation. Although the discrete element method (DEM) can solve complex three-dimensional fractures, it involves enormous computational costs and requires predefined fracture trajectories.
[0006] Currently, in shale gas exploration and development, the long-term ultimate recovery (EUR) prediction of shale gas wells is mainly calculated based on production dynamics fitting. EUR prediction is a method that predicts the total production that a well may achieve over its entire life cycle (20 to 30 years) by analyzing historical production data and reservoir characteristics, in order to assess the economic value and development potential of oil and gas resources. Because this method relies on existing production data, its prediction timeliness is low, and it cannot provide timely guidance during fracturing implementation to optimize and adjust fracturing schemes. This leads to certain limitations and lags in practical operation. Well-controlled resources are calculated based on well spacing and reserve abundance. However, because the length and height of fracturing fractures are affected by both geological conditions and fracturing technology, there is a significant difference between the actual resources utilized and the theoretically calculated well-controlled resources. This difference results in many cases where, although the calculated well-controlled resources are large, the EUR of a single well is low, failing to fully realize the resource potential.
[0007] Therefore, in the actual exploration and development process, how to improve the accuracy and timeliness of predictions, and how to effectively utilize well-controlled resources have become urgent problems to be solved. Summary of the Invention
[0008] To address the problems existing in the prior art, this disclosure provides a method, apparatus, and equipment for predicting the long-term production capacity of shale gas wells.
[0009] The specific technical solutions of this disclosure are as follows:
[0010] On one hand, this disclosure provides a method for predicting the long-term production capacity of shale gas wells, the method comprising:
[0011] Based on the target fracturing pumping parameters and the formation parameters of the formation where the un-produced well is located, fracturing simulation is performed on the un-produced well to obtain the fracture parameters of the un-produced well under the target fracturing pumping parameters.
[0012] Calculate the shale gas reservoir reserves of unproduced wells based on well parameters and fracture parameters;
[0013] The single-well utilization rate of each well is calculated based on the actual long-term production capacity of multiple wells already in production in the formation, and a posterior distribution of the single-well utilization rate is constructed. The target single-well utilization rate value is determined based on the Markov chain-Monte Carlo sampling method.
[0014] The long-term production capacity forecast of undeveloped wells is calculated based on the shale gas reservoir reserves of the undeveloped wells and the target single-well utilization rate. The long-term production capacity forecast of undeveloped wells is used to guide the development of undeveloped wells.
[0015] Furthermore, the calculation of shale gas reservoir reserves in unproduced wells based on well parameters and fracture parameters further includes:
[0016] Calculate the unit reserve coefficient of the unproduced wells based on the well parameters of the unproduced wells;
[0017] Calculate the fracture control volume of the non-production well based on fracture parameters;
[0018] The reserves of shale gas reservoirs are calculated based on the unit reserve coefficient and the controlled volume of the fracture.
[0019] Furthermore, the well parameters for unproduced wells include average porosity, average water saturation, and the volume factor of gas extracted from the formation.
[0020] The formula for calculating the unit reserve coefficient of unproduced wells based on their well parameters is as follows:
[0021] Where, URI new This represents the unit reserve coefficient of unproduced wells. Indicates average porosity. B represents the average water saturation. g This represents the volume coefficient.
[0022] Furthermore, the fracture parameters include the average fracture half-length and average fracture height for each of the multiple well sections;
[0023] The calculation of the fracture control volume of non-production wells based on fracture parameters further includes:
[0024] According to the formula Calculate the fracture control volume for each well segment, where SRV new_k This represents the fracture control volume of the k-th well segment of a non-producing well. This represents the average fracture half-length of the k-th well segment in a non-production well. L represents the average fracture height of the k-th well segment of a non-production well. wk This represents the length of the k-th well segment of a non-production well;
[0025] The controlled fracture volumes of all well segments are summed to obtain the controlled fracture volumes of the unproduced wells.
[0026] Furthermore, the formula for calculating shale gas reservoir reserves based on the unit reserve coefficient and fracture control volume is: OGIP new =URI new ×SRV new ;
[0027] Among them, OGIP new This indicates shale gas reserves.
[0028] Furthermore, the formula for calculating the single-well utilization rate of each well in production based on the actual long-term production capacity of multiple wells already in production within the formation is as follows:
[0029] in, Indicates the utilization rate of a single well that has been put into production, EUR calibrated URI represents the actual long-term production capacity of wells that have already commenced production. prod This represents the unit reserve coefficient of wells that have been put into production. This indicates the controlled volume of the fractured joints in a well that has been put into production.
[0030] Furthermore, the formula for calculating the long-term production forecast of unproduced wells based on the shale gas reservoir reserves and the target single-well utilization rate is: EUR new =RF new ×OGIP new ;
[0031] Among them, EUR new RF represents the long-term production forecast of wells that have not yet commenced production. new This represents the target single-well utilization rate value.
[0032] On the other hand, this disclosure also provides a long-term production capacity prediction device for shale gas wells, the device comprising:
[0033] The fracturing simulation calculation unit is used to perform fracturing simulation on the un-produced well based on the target fracturing construction pumping parameters and the formation parameters of the formation where the un-produced well is located, and to obtain the fracture parameters of the un-produced well under the target fracturing construction pumping parameters.
[0034] The shale gas reservoir reserve calculation unit is used to calculate the shale gas reservoir reserves of non-production wells based on the well parameters and fracture parameters of non-production wells.
[0035] The target single-well utilization rate calculation unit is used to calculate the single-well utilization rate of each well that has been put into production based on the actual long-term production capacity of multiple wells in the formation, and to construct the posterior distribution of the single-well utilization rate. The target single-well utilization rate value is determined based on the Markov chain-Monte Carlo sampling method.
[0036] The long-term production capacity prediction unit is used to calculate the long-term production capacity prediction of undeveloped wells based on the shale gas reservoir reserves of undeveloped wells and the target single-well utilization rate. The long-term production capacity prediction of undeveloped wells is used to guide the development of undeveloped wells.
[0037] On the other hand, this disclosure also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-described method.
[0038] Finally, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0039] In this embodiment, fracturing simulation is performed on undeveloped wells based on target fracturing pumping parameters to obtain fracture parameters under these parameters. Then, the shale gas reservoir reserves of the undeveloped wells are calculated based on the simulated fracture parameters and the well parameters. Simultaneously, the utilization rate of developed wells in the same formation as the undeveloped wells is calculated. By constructing a posterior distribution of the utilization rate and using a Markov chain-Monte Carlo (MCMC) sampling method, a representative utilization rate value for the undeveloped wells is quantified as the target utilization rate value. Finally, based on the calculated shale gas reservoir reserves and the target utilization rate value, an accurate long-term production capacity prediction value for the undeveloped wells is calculated. This allows researchers to develop the undeveloped wells based on this long-term production capacity prediction, fully exploiting formation resources and improving the overall efficiency of shale gas development. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Figure 1 shows a schematic diagram of the implementation system of a long-term production capacity prediction method for shale gas wells according to an embodiment of this disclosure;
[0042] Figure 2 is a flowchart illustrating a long-term production capacity prediction method for shale gas wells according to an embodiment of this disclosure.
[0043] Figure 3 shows a schematic diagram of the solution process of the pseudo-three-dimensional displacement discontinuous coupling equilibrium seam height algorithm in the embodiments of this disclosure;
[0044] Figure 4 shows a schematic diagram of the process for calculating the shale gas reservoir reserves of unproduced wells based on the well parameters and fracture parameters of unproduced wells in an embodiment of this disclosure.
[0045] Figure 5 shows a schematic diagram of the seam control volume calculated based on a regular cuboid in an embodiment of this disclosure;
[0046] Figure 6 shows a schematic diagram of the fracture control volume calculated based on the fracturing simulation results (transverse and longitudinal irregular SRV) in an embodiment of this disclosure;
[0047] Figure 7 shows a schematic diagram of a one-dimensional geological model in an embodiment of this disclosure;
[0048] Figure 8 is a schematic diagram of a high-precision geomechanical model of the reservoir area in an embodiment of this disclosure;
[0049] Figure 9 shows a schematic diagram of the original construction pumping curve and coarsening results in an embodiment of this disclosure;
[0050] Figure 10 is a schematic diagram of the original construction pumping curve and coarsening results in an embodiment of this disclosure;
[0051] Figure 11 shows a schematic diagram of the three-dimensional visualization effect of the simulated fracture propagation of 34 segments in an embodiment of this disclosure;
[0052] Figure 12 is a schematic diagram of the quantitative statistical results of the fracture length of 34 simulated fracturing segments in this embodiment of the present disclosure;
[0053] Figure 13 is a schematic diagram of the quantitative statistical results of the average height of the hydraulic fractures in a total of 34 simulated hydraulic fractures in this embodiment of the present disclosure;
[0054] Figure 14 shows a top view of the superimposed simulation of 34 hydraulic fracturing segments and microseismic event points in an embodiment of this disclosure;
[0055] Figure 15 shows a side view of the 34 segments of hydraulic fracturing simulation and microseismic event points superimposed in an embodiment of this disclosure;
[0056] Figure 16 shows the reservoir range single reservoir coefficient curve calculated according to the embodiments of this disclosure;
[0057] Figure 17 is a schematic diagram of the total reserves of each small-layer seam control body calculated according to the embodiments of this disclosure;
[0058] Figure 18 shows the utilization curves of each sub-layer obtained by the Markov chain-Monte Carlo sampling method in an embodiment of this disclosure.
[0059] Figure 19 shows a schematic diagram of a long-term production capacity prediction device for a shale gas well according to an embodiment of this disclosure.
[0060] Figure 20 shows a schematic diagram of the structure of a computer device in an embodiment of this disclosure.
[0061] [Explanation of reference numerals in the attached diagram]: 101, Terminal; 102, Server; 1901, Shale gas reservoir reserve calculation unit; 1902, Target single well utilization rate calculation unit; 1903, Target single well utilization rate calculation unit; 1904, Long-term production capacity prediction calculation unit; 2002, Computer equipment; 2004, Processing equipment; 2006, Storage resources; 2008, Drive system; 2010, Input / output module; 2012, Input device; 2014, Output device; 2016, Presentation device; 2018, Graphical user interface; 2020, Network interface; 2022, Communication link; 2024, Communication bus. Detailed Implementation
[0062] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the embodiments of this disclosure.
[0063] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0064] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of this disclosure comply with the relevant provisions of national laws and regulations.
[0065] It should be noted that in the embodiments disclosed herein, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary and are intended only to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used such solutions.
[0066] Figure 1 shows a schematic diagram of an implementation system for a long-term shale gas well production prediction method according to an embodiment of this disclosure, including a terminal 101 and a server 102. The terminal 101 and the server 102 can communicate via a network, which may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and be connected to a website, user equipment (e.g., computing devices), and a backend system.
[0067] Terminal 101 can be an input device for users. Staff can input the target fracturing pumping parameters of the un-produced well and the actual long-term production capacity of the produced well in the same formation as the un-produced well into server 102.
[0068] Server 102 is equipped with a simulator for simulating fracturing. The simulator is invoked to simulate fracturing in undeveloped wells based on the target fracturing pumping parameters input from terminal 101, obtaining the fracture parameters of the undeveloped wells. Then, the shale gas reservoir reserves of the undeveloped wells are calculated. Next, based on the actual long-term production value of the developed wells input from terminal 101, the single-well utilization rate of the developed wells is calculated. From the single-well utilization rates, a representative single-well utilization rate value for the undeveloped wells is determined as the target single-well utilization rate value, and then the predicted long-term production value of the undeveloped wells is calculated. Finally, the calculated predicted long-term production value is provided to the staff through terminal 101 to guide their development of the undeveloped wells.
[0069] Alternatively, server 102 may be a node of a cloud computing system (not shown in the figure), or each server may be a separate cloud computing system comprising multiple computers interconnected by a network and operating as a distributed processing system.
[0070] Furthermore, it should be noted that Figure 1 shows only one application environment provided by the embodiments of this disclosure. In actual applications, other application environments may also be included, and this disclosure does not impose any limitations.
[0071] To address the problems existing in the prior art, this disclosure provides a method for predicting the long-term production capacity of shale gas wells. It achieves accurate calculation of the predicted long-term production capacity of undeveloped wells. Figure 2 shows a flowchart of the method for predicting the long-term production capacity of shale gas wells according to this disclosure. The process of calculating the predicted long-term production capacity of undeveloped wells is described in this figure. The order of steps listed in the embodiment is merely one possible execution order among many steps and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiment or the accompanying drawings can be executed sequentially or in parallel.
[0072] As shown in Figure 2, the specific method may include:
[0073] Step 201: Based on the target fracturing pumping parameters and the formation parameters of the formation where the un-produced well is located, perform fracturing simulation on the un-produced well to obtain the fracture parameters of the un-produced well under the target fracturing pumping parameters.
[0074] Step 202: Calculate the shale gas reservoir reserves of the unproduced wells based on the well parameters and fracture parameters of the unproduced wells;
[0075] Step 203: Calculate the single-well utilization rate of each well based on the actual long-term production capacity of multiple wells already in production in the formation, construct the posterior distribution of the single-well utilization rate, and determine the target single-well utilization rate value based on the Markov chain-Monte Carlo sampling method.
[0076] Step 204: Calculate the long-term production capacity forecast of the undeveloped wells based on the shale gas reservoir reserves and the target single-well utilization rate. The long-term production capacity forecast of the undeveloped wells is used to guide the development of the undeveloped wells.
[0077] In this embodiment, fracturing simulation is performed on undeveloped wells based on target fracturing pumping parameters to obtain fracture parameters under these parameters. Then, the shale gas reservoir reserves of the undeveloped wells are calculated based on the simulated fracture parameters and the well parameters. Simultaneously, the utilization rate of developed wells in the same formation as the undeveloped wells is calculated. By constructing a posterior distribution of the utilization rate and using a Markov chain-Monte Carlo (MCMC) sampling method, a representative utilization rate value for the undeveloped wells is quantified as the target utilization rate value. Finally, based on the calculated shale gas reservoir reserves and the target utilization rate value, an accurate long-term production capacity prediction value for the undeveloped wells is calculated. This allows researchers to develop the undeveloped wells based on this long-term production capacity prediction, fully exploiting formation resources and improving the overall efficiency of shale gas development.
[0078] In this embodiment of the disclosure, the formation parameters include at least Poisson's ratio, static Young's modulus, minimum horizontal principal stress, and maximum horizontal principal stress.
[0079] Specifically, the first step is to perform efficient geomechanical modeling. For fracturing simulation, it is assumed that the mechanical model data input only includes existing vertical well logging curves. This type of data includes: depth, vertical depth, P-wave and S-wave velocities, gamma rate, porosity, density, etc. The mechanical parameters necessary for fracturing simulation can be calculated using the following series of formulas:
[0080] Where v is Poisson's ratio, DTS is the shear wave velocity logging curve value, DTC is the longitudinal wave velocity logging curve value, and GR is the gamma logging curve value.
[0081] Among them, E D For dynamic Young's modulus, ρ b E represents the density logging curve value. S This is the static Young's modulus. α = 1 - (1 - φ) 3.8 (4) P p =9.95TVD (5)
[0082] Where α is the dynamic Biot coefficient, φ is the porosity logging curve value, and P p S is the pore pressure, TVD is the vertical depth, and S is the vertical depth. v For vertical stress, z w For ground elevation depth, z r S represents the depth of the gas reservoir. min For the minimum horizontal principal stress, S max This represents the maximum horizontal principal stress.
[0083] Then, the calculated Poisson's ratio, static Young's modulus, minimum horizontal principal stress, and maximum horizontal principal stress are used as formation parameters for fracture simulation, and fracturing simulation is performed on unexploited wells based on the target fracturing construction pumping parameters.
[0084] This embodiment employs a quasi-three-dimensional displacement discontinuous coupling equilibrium seam height algorithm. The solution process is shown in Figure 3, and the specific simulation process is as follows:
[0085] Step 1: Perform fracturing simulation initialization, including importing formation parameters and fracturing construction pumping parameters;
[0086] Step 2: Calculate the initial time step N i The rock deformation equation and fluid equation were used to calculate the width of the crack propagation unit and the pressure inside the crack;
[0087] In this step, the time step refers to the length of time progression during the fracturing process, calculated using a series of physical equations such as mass conservation and mechanical parameters. The total duration of fracturing fluid injection should be consistent with the sum of the time steps. In fracturing simulations, time steps are typically broken down into multiple steps, and in actual fracturing, fracturing fluid injection is also divided into multiple stages depending on the operational conditions. Due to the complexity of calculating the time step in the mathematical and physical model, its magnitude depends on mass conservation conditions, fracture propagation, mechanical conditions, etc., and its value may be greater or less than the actual duration of each fracturing fluid injection stage. The concept of a time step exists only in the mathematical model of fracturing simulation; it does not exist in the actual fracturing process.
[0088] Step 3: Perform a convergence check. If the solution does not converge, recalculate the time step N′. i(Shrink). Repeat step 2; if convergence occurs, proceed to step 4;
[0089] Step 4: Calculate the proppant concentration and conductivity using a proppant transport model;
[0090] Step 5: If natural cracks exist, calculate the activation status of natural cracks; if no natural cracks exist, skip this step.
[0091] Step 6: Check the current total time step length. If it does not exceed the total pumping duration, calculate the new time step N. i+1 Repeat steps 2 through 5. If the total pumping time is exceeded, complete all calculations.
[0092] It should be noted that the efficient fracturing simulation based on the equilibrium fracture height theory aims to simulate and characterize how artificial fractures form and propagate under subsurface conditions using mathematical and physical models. During the simulated fracturing process, it calculates the fracture length, height, width, pressure within the fracture, proppant (sand) concentration, and the change in fracture conductivity as fracturing progresses. Furthermore, since energy typically concentrates near the wellbore during fracturing, the aforementioned fracture properties differ at different locations within the fracture. Therefore, the goal of the efficient fracturing simulation based on the equilibrium fracture height theory is to calculate the fracture's geometry, physical properties, and their temporal and spatial variations.
[0093] According to one embodiment of this disclosure, as shown in Figure 4, calculating the shale gas reservoir reserves of an unproduced well based on its well parameters and fracture parameters further includes:
[0094] Step 401: Calculate the Unit Reserve Index (URI) of unproduced wells based on their well parameters. The Unit Reserve Index is an evaluation index that evaluates the abundance of reserves per unit area and unit thickness. It is calculated based on the understanding of geological conditions of unconventional shale gas reservoirs and combined with multiple geological and engineering parameters (such as porosity, total organic carbon (TOC), gas saturation, reservoir resources, reservoir thickness, well-controlled area, etc.).
[0095] Step 402: Calculate the Stimulated Rock Volume (SRV) of the non-production well based on the fracture parameters. The Stimulated Rock Volume is the volume controlled by the fracture network. It is the equivalent volume of rock modification after hydraulic fracturing of a shale gas horizontal production well, calculated based on parameters such as fracture length, fracture height, and fracture cluster spacing obtained from the fracturing simulation.
[0096] Step 403: Calculate the shale gas reservoir reserves based on the unit reserve coefficient and the controlled volume of the fracture.
[0097] According to one embodiment of this disclosure, the well parameters of a non-producing well include average porosity, average water saturation, and the volume factor of gas extracted from the formation.
[0098] The formula for calculating the unit reserve coefficient of unproduced wells based on their well parameters is as follows:
[0099] Where, URI new This represents the unit reserve coefficient of unproduced wells. Indicates average porosity. B represents the average water saturation. g This represents the volume coefficient.
[0100] The average water saturation can be obtained through the following methods:
[0101] Well logging data analysis: This involves using logging tools such as resistivity, acoustic, and nuclear magnetic resonance (NMR) logging to obtain formation physical property data. These tools can provide crucial information such as formation water saturation.
[0102] Nuclear magnetic resonance logging: Nuclear magnetic resonance (NMR) logging can directly provide the water saturation of the formation. By using the T2 relaxation time distribution of nuclear magnetic resonance, free water, bound water and hydrocarbons can be distinguished, thereby estimating the water saturation.
[0103] Core analysis: Core samples are taken from horizontal well sections, and their water saturation is determined through laboratory analysis. Core analysis can provide the actual water content of the formation and is an important basis for verifying other measurement methods.
[0104] Geological Modeling and Reservoir Simulation: A three-dimensional geological model is established, and reservoir simulation is performed by combining downhole logging and seismic data. The model can be used to calculate the water saturation along the well section of the horizontal well and estimate the average water saturation.
[0105] Material balance method: Based on the principle of material balance, combined with drainage data and fluid properties, the average water saturation of the formation is estimated.
[0106] Pressure recovery well test method: By analyzing well test data, especially pressure recovery well test, water saturation can be estimated.
[0107] It should be noted that the combined use of these methods can help researchers more accurately assess the average formation water saturation of shale gas horizontal wells. In practice, the appropriate method or combination can be selected based on actual conditions and available data.
[0108] Volume index B gThis refers to the volume factor of gases extracted from a formation, such as the volume factor of methane. It indicates the volume occupied by a unit volume of gas under formation conditions but under standard surface conditions. It represents a volume conversion between different temperature and pressure conditions and is usually expressed as formation volume (m³). 3 ) and ground volume (m 3 The ratio is expressed as (m³). It is used to convert the volume of gas under formation conditions to the volume under standard conditions, and the common unit is m³. 3 / m 3 (No stiffness), volume factor B g There are two methods to obtain it:
[0109] Experimental method: The volume change of gas under different pressures and temperatures was determined in the laboratory by PVT (pressure-volume-temperature) experiment to obtain its volume coefficient under formation conditions.
[0110] Empirical formula method: This method can estimate using gas law equations (such as the ideal gas law or the real gas law).
[0111] Where Z is the compressibility factor (obtained experimentally, from the Standing-Katz empirical chart, or from the Dranchuk-Abou-Kassem empirical equation), T is the formation temperature, P is the formation pressure, and 0.0283 is a constant used for unit conversion.
[0112] Traditional shale gas reserve calculations use a method combining regular volume with a unit storage coefficient. The control volume for reserve calculations is not well constrained by geomechanics, engineering parameters, and fracturing simulations, and its shape is limited to a regular cuboid, as shown in Figure 5. This method has low calculation accuracy.
[0113] To address this issue, the present invention improves and optimizes the calculation of the fracture control volume of unproduced wells. Specifically, the unproduced wells are divided into multiple well segments, and the average fracture half-length and average fracture height of the multiple well segments are extracted from the fracturing simulation results.
[0114] The calculation of the fracture control volume of non-production wells based on fracture parameters further includes:
[0115] Calculate the fracture control volume of each well segment according to formula (11):
[0116] Among them, SRV new_k This represents the fracture control volume of the k-th well segment of a non-producing well. This represents the average fracture half-length of the k-th well segment in a non-production well. L represents the average fracture height of the k-th well segment of a non-production well. wkThis represents the length of the k-th well segment of a non-production well;
[0117] The controlled fracture volumes of all well segments are summed to obtain the controlled fracture volumes of the unproduced wells.
[0118] The quantifiable transverse and longitudinal irregular fracture control volume is obtained by formula (11), as shown in Figure 6. The method of this embodiment of the present disclosure yields more accurate reserve assessment results after constraint.
[0119] The formula for calculating shale gas reservoir reserves based on the unit reserve coefficient and fracture control volume is (12): OGIP new =URI new ×SRV new (12)
[0120] Among them, OGIP new This indicates the shale gas reserves of wells that have not yet commenced production.
[0121] Typically, long-term energy reserve (EUR) predictions for shale gas reservoirs require quantification through numerical simulations of reservoir production capacity. Furthermore, the production simulation model needs to be corrected using actual production data; this process, known as history fitting, is usually time-consuming and labor-intensive (at least half a week to a week) and involves a degree of uncertainty. If some new wells are not yet in production and there is no actual production data, EUR prediction becomes significantly more difficult. The embodiments in this manual utilize the EUR of operational shale gas horizontal wells corrected for history fitting, coupled with the definitions of single-reservoir coefficient and fractured volume, to calculate and quantify the utilization rate of operational single wells. A posterior distribution of the utilization rate is constructed, and representative single-well utilization rates of non-operational horizontal shale gas wells are quantified using the Markov chain-Monte Carlo sampling method. Finally, the EUR of non-operational wells is calculated by combining the reservoir values obtained from fracturing simulations.
[0122] Specifically, the formula for calculating the single-well utilization rate of each well in production based on the actual long-term production capacity of multiple wells in the formation is (13):
[0123] in, Indicates the utilization rate of a single well that has been put into production, EUR calibrated URI represents the actual long-term production capacity of wells that have already commenced production. prod This represents the unit reserve coefficient of wells that have been put into production. This indicates the controlled volume of the fractured joints in a well that has been put into production.
[0124] It should be noted that the actual long-term production capacity of wells already in production is EUR calibrated Unit Reserves Coefficient (URI) prod , control volume of pressure cracks All data were calculated based on actual data from wells already in production, including the unit reserve coefficient URI. prod It is calculated based on formula (9) for the average porosity, average water saturation, and volume coefficient of gas developed from the formation of the wells already in production.
[0125] This embodiment performs historical fitting on the production wells to obtain the corrected EUR, and combines the unit reserve coefficient of formula (9) and the definition of fracture control volume of formula (11) to calculate and quantify the single-well utilization rate of these production wells through formula (13). By constructing the posterior distribution of the utilization rate and based on the Markov chain-Monte Carlo (MCMC) sampling method, the representative utilization rate values of non-production wells are quantified. The specific method is as follows:
[0126] Bayesian inference framework: Bayesian inference is the process of deriving the posterior distribution of parameters by combining prior information and observed data. For the posterior distribution of mobilization rate, Bayes' formula is:
[0127] in, The posterior distribution of the utilization rate of wells already in production; It is the likelihood function, reflecting the adaptability of the mobilization rate to the observed data; P(D) is the prior distribution of the mobilization rate (usually a uniform distribution, normal distribution, or triangular distribution); P(D) is the normalization constant, which can usually be ignored.
[0128] MCMC sampling method: Due to the complexity of the posterior distribution, direct computation is difficult. Therefore, the MCMC algorithm is used to approximate the posterior distribution. The MCMC algorithm generates samples step by step from the posterior distribution by constructing a Markov chain. Commonly used MCMC methods include the Metropolis-Hastings algorithm and Gibbs sampling. The steps of MCMC sampling are as follows:
[0129] Choose initial value RF0: Initialize the initial estimate of the mobilization rate.
[0130] Generate candidate samples: Generate candidate mobilization rate values RF using a jump distribution (such as a normal distribution). new
[0131] Calculate the acceptance probability: The acceptance probability α is calculated based on the posterior probabilities of the candidate samples and the current sample, and is defined as follows:
[0132] Wherein, P(RF) new |D) and P(RF) k |D) represents the posterior probabilities of the candidate mobilization rate and the current mobilization rate, respectively; q(RF) k |RF new) is a jump distribution from the current value to the candidate value.
[0133] Accept or reject: Based on the acceptance probability α, if the generated random number is less than α, the candidate sample is accepted; otherwise, the current sample is retained.
[0134] Repeated sampling: Repeat the above process continuously to generate a sufficient number of samples to approximate the posterior distribution of mobilization rate.
[0135] Then, representative single-well utilization rates obtained by sampling from the posterior distribution of the utilization rates of already operational wells are used as the target single-well utilization rate RF. new For example, if there are relatively few adjacent wells of a non-producing well, the utilization rate of a producing well with similar geological parameters is basically selected; if there are relatively many adjacent wells of a non-producing well, the utilization rates of several producing wells with similar geological conditions are selected, and the average value is taken as the target single-well utilization rate.
[0136] Finally, the long-term production capacity forecast of the unproduced wells is calculated based on the shale gas reservoir reserves and the target single-well utilization rate, using the formula (16): EUR new =RF new ×OGIP new (16)
[0137] Among them, EUR new RF represents the long-term production forecast of wells that have not yet commenced production. new This represents the target single-well utilization rate value.
[0138] This example combines a geomechanical efficient modeling module algorithm, a high-efficiency fracturing simulation algorithm based on balanced fracture height theory, and a fracturing control volume reserve algorithm with a Markov chain-Monte Carlo utilization rate sampling algorithm to predict the 20-year EUR production capacity of multi-stage large-scale fracturing horizontal wells in deep shale gas reservoirs. The specific process includes the following steps:
[0139] 1) Import and calculation of original geomechanical parameters
[0140] First, the original data required for the geomechanical parameters of this example well are imported, including transverse and longitudinal waves, density, porosity, gamma rate, etc. Formation parameters, including Young's modulus, Poisson's ratio, pore pressure, and maximum and minimum horizontal principal stresses, are calculated using formulas (1)-(8) (Figure 7). A high-precision geomechanical model (80 layers in total) of the reservoir range (total thickness 40 meters) is established through geological stratification and target point identification, as shown in Figure 8.
[0141] 2) Batch import of pumping programs
[0142] Based on the established high-precision geomechanical model, approximately 34 segments of construction pumping curves from unproduced wells were imported, including wellhead pumping pressure, flow rate, and sand concentration. Typically, the data frequency of construction pumping curves is on the order of seconds or minutes. Directly using the raw data for fracturing simulation results in excessive data volume and long simulation times. Therefore, coarsening is required based on changes in pumping time, flow rate, and sand concentration. One segment of the curve and the coarsening results are shown in Figure 9.
[0143] 3) Fitting of wellhead pumping pressure
[0144] After importing the pumping program in batches, the actual measured pumping pressures of 34 segments in this example were fitted and corrected by adjusting friction parameters (including the drag-reducing agent coefficient and tubing pressure gradient). The fitted fracturing time range does not include the pre-fracturing fluid stage and the pump shutdown stage (small displacement, no sand stage). Figure 10 shows the fitting effect of one segment in this example, and the overall fitting accuracy of the entire well section is greater than 90%.
[0145] 4) Three-dimensional visualization and quantification of crack morphology based on hydraulic crack simulation results
[0146] After batch running and calibration of the fracturing simulation, Figure 11 shows the three-dimensional visualization of the fracture propagation of 34 segments in this example; Figure 12 shows the calibrated statistical information of the half-length of the fracturing fractures on a cluster basis; and Figure 13 shows the calibrated statistical information of the fracturing fracture height on a cluster basis. The average fracture length in this example is approximately 190 meters, with the maximum and minimum half-lengths being 400 and 52 meters, respectively; the average fracture height is approximately 19.1 meters, with the maximum and minimum heights being 27.7 and 11.1 meters, respectively.
[0147] 5) Constraints based on fracturing simulation results from microseismic data
[0148] This example, based on the pressure fitting and morphological quantification of 34 fracturing pump injection segments, imports three-dimensional spatial microseismic event point data for constraint: Figure 14 shows the overlay diagram of the simulated fracturing morphology and microseismic event points from a top-down perspective, and Figure 15 shows the overlay diagram of the simulated fracturing morphology and microseismic event points from a side-view perspective (looking from point B in the wellbore towards point A in the wellbore). It can be seen that the overall microseismic constraint effect is good.
[0149] 6) Calculation of single storage coefficient, seam control volume SRV, and storage in seam control volume.
[0150] In this example, the reservoir porosity ranges from 6.76% to 9.68%, the reservoir water saturation ranges from 30% to 53%, and the volume factor is approximately 0.0027 surface-to-cubic-meter / subsurface-to-cubic-meter. The single-reservoir coefficient calculated using Formula 9 has an average value of approximately 0.199 billion cubic meters per square kilometer*m in the reservoir vertical depth range of 4240 meters to 4270 meters, and an average value of approximately 0.154 billion cubic meters per square kilometer*m in the reservoir vertical depth range of 4240 meters to 4280 meters. Figure 16 shows the calculated single-reservoir coefficient curve.
[0151] Based on the statistical results of Figures 12 and 13, and combined with the information on the spacing between each cluster (average 8.4 meters), the parameter (SRV)^ in Formula 11 can be obtained, with a calculated result of approximately 1.593 billion cubic meters; using Formula 11, the reserves within the fracture control body can be obtained, with a result of approximately 245 million cubic meters.
[0152] 7) Quantification of the controlled reserves of each sublayer and the utilization rate change curve of the sublayer generated by Markov chain-Monte Carlo.
[0153] Based on the thickness of each sub-layer in the geological stratification, the fracture-controlling volume reserves of each sub-layer can be quantified, as shown in Figure 17. In this example, the reservoir can be divided into 5 sub-layers, and the fracture-controlling volume reserves in each sub-layer (from shallow to deep) are: 0 billion cubic meters, 69.3 million cubic meters, 82.7 million cubic meters, 31.5 million cubic meters, and 61.8 million cubic meters, respectively. The utilization rate of each sub-layer constructed using the Markov chain-Monte Carlo method as a function of time is shown in Figure 18: the utilization rates of each sub-layer (from shallow to deep) after 20 years of production are 0%, 17.6%, 21.8%, 5.5%, and 13.5%, respectively, with a total utilization rate of approximately 58.4%.
[0154] 8) 2020 capacity forecast EUR calculation results
[0155] Based on the results of steps 6) and 7) of the specific implementation method, the predicted EUR production capacity of a single well in this example over 20 years can be obtained as 143 million cubic meters.
[0156] Based on the same inventive concept, this disclosure also provides a long-term production capacity prediction device for shale gas wells, as shown in FIG19. The device includes:
[0157] The fracturing simulation calculation unit 1901 is used to perform fracturing simulation on the un-produced well based on the target fracturing construction pumping parameters and the formation parameters of the formation where the un-produced well is located, and to obtain the fracture parameters of the un-produced well under the target fracturing construction pumping parameters.
[0158] Shale gas reservoir reserve calculation unit 1902 is used to calculate the shale gas reservoir reserves of non-production wells based on the well parameters and fracture parameters of non-production wells.
[0159] The target single-well utilization rate calculation unit 1903 is used to calculate the single-well utilization rate of each well that has been put into production based on the actual long-term production capacity of multiple wells in the formation, and to construct the posterior distribution of the single-well utilization rate. The target single-well utilization rate value is determined based on the Markov chain-Monte Carlo sampling method.
[0160] The long-term production capacity prediction calculation unit 1904 is used to calculate the long-term production capacity prediction of unproduced wells based on the shale gas reservoir reserves of unproduced wells and the target single well utilization rate. The long-term production capacity prediction of unproduced wells is used to guide the development of unproduced wells.
[0161] Since the principle of the above-mentioned device in solving the problem is similar to that of the above-mentioned method, the implementation of the above-mentioned system can refer to the implementation of the above-mentioned method, and the repeated parts will not be described again.
[0162] Figure 20 shows a schematic diagram of the structure of a computer device according to an embodiment of the present disclosure. The computer device in this embodiment is capable of executing the methods described in the embodiments of the present disclosure. The computer device 2002 may include one or more processing devices 2004, such as one or more central processing units (CPUs), each processing unit implementing one or more hardware threads. The computer device 2002 may also include any storage resource 2006 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the storage resource 2006 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any storage resource can use any technology to store information. Further, any storage resource can provide volatile or non-volatile retention of information. Further, any storage resource may represent a fixed or removable component of the computer device 2002. In one case, when the processing device 2004 executes associated instructions stored in any storage resource or combination of storage resources, the computer device 2002 can perform any operation of the associated instructions. The computer device 2002 also includes one or more drive systems 2008 for interacting with any storage resources, such as hard disk drive systems, optical disk drive systems, etc.
[0163] Computer device 2002 may also include an input / output module 2010 (I / O) for receiving various inputs (via input device 2012) and providing various outputs (via output device 2014). A specific output mechanism may include a presentation device 2016 and an associated graphical user interface (GUI) 2018. In other embodiments, the input / output module 2010 (I / O), input device 2012, and output device 2014 may be omitted, and the device may function solely as a computer device within a network. Computer device 2002 may also include one or more network interfaces 2020 for exchanging data with other devices via one or more communication links 2022. One or more communication buses 2024 couple the components described above together.
[0164] Communication links 2022 can be implemented in any way, such as via a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication links 2022 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
[0165] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0166] This disclosure also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the above-described method.
[0167] It should be understood that in various embodiments of the present disclosure, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
[0168] It should also be understood that, in the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, in the embodiments of this disclosure, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.
[0169] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this disclosure can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure.
[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0171] In the embodiments provided in this disclosure, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.
[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this disclosure, depending on actual needs.
[0173] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this disclosure, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0175] This disclosure uses specific embodiments to illustrate the principles and implementation methods of the embodiments of this disclosure. The above description of the embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this disclosure. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this disclosure. Therefore, the content of this disclosure should not be construed as a limitation on the embodiments of this disclosure.
Claims
1. A method for predicting the long-term production capacity of shale gas wells, characterized in that, The method includes: Based on the target fracturing pumping parameters and the formation parameters of the formation where the un-produced well is located, a fracturing simulation is performed on the un-produced well to obtain the fracture parameters of the un-produced well under the target fracturing pumping parameters. The shale gas reservoir reserves of the unproduced wells are calculated based on the well parameters and fracture parameters of the unproduced wells. The single-well utilization rate of each of the put-in wells is calculated based on the actual long-term production capacity of multiple put-in wells in the formation, and a posterior distribution of the single-well utilization rate is constructed. The target single-well utilization rate value is determined based on the Markov chain-Monte Carlo sampling method. The long-term production capacity forecast of the undeveloped well is calculated based on the shale gas reservoir reserves of the undeveloped well and the target single-well utilization rate. The long-term production capacity forecast of the undeveloped well is used to guide the development of the undeveloped well.
2. The method according to claim 1, characterized in that, The calculation of the shale gas reservoir reserves of the unproduced wells based on the well parameters and fracture parameters further includes: Calculate the unit reserve coefficient of the unproduced well based on the well parameters of the unproduced well; Calculate the fracture control volume of the unproduced well based on the fracture parameters; The shale gas reservoir reserves are calculated based on the unit reserve coefficient and the controlled volume of the fracture.
3. The method according to claim 2, characterized in that, The well parameters of the unproduced wells include average porosity, average water saturation, and the volume factor of the gas extracted from the formation. The formula for calculating the unit reserve coefficient of the non-producing well based on the well parameters is as follows: Where, URI new This represents the unit reserve coefficient of unproduced wells. Indicates average porosity. B represents the average water saturation. g This represents the volume coefficient.
4. The method according to claim 3, characterized in that, The fracture parameters include the average fracture half-length and average fracture height for each of the multiple well sections. Calculating the controlled fracture volume of the unproduced well based on the fracture parameters further includes: According to the formula Calculate the fracture control volume for each well segment, where SRV new_k This represents the fracture control volume of the k-th well segment of a non-producing well. This represents the average fracture half-length of the k-th well segment in a non-production well. L represents the average fracture height of the k-th well segment of a non-production well. wk This represents the length of the k-th well segment of a non-production well; The fracture control volume of the unproduced well is obtained by summing the fracture control volumes of all well segments.
5. The method according to claim 4, characterized in that, The formula for calculating the shale gas reservoir reserves based on the unit reserve coefficient and fracture control volume is: OGIP new =URI new ×SRV new ; Among them, OGIP new This indicates shale gas reserves.
6. The method according to claim 5, characterized in that, The formula for calculating the single-well utilization rate of each of the multiple wells already in production in the formation, based on the actual long-term production capacity of each well, is as follows: in, Indicates the utilization rate of a single well that has been put into production, EUR calibrated URI represents the actual long-term production capacity of wells that have already commenced production. prod This represents the unit reserve coefficient of wells that have been put into production. This indicates the controlled volume of the fractured joints in a well that has been put into production.
7. The method according to claim 5, characterized in that, The formula for calculating the long-term production forecast of the unproduced well based on the shale gas reservoir reserves of the unproduced well and the target single-well utilization rate is: EUR new =RF new ×OGIP new ; Among them, EUR new RF represents the long-term production forecast of wells that have not yet commenced production. new This represents the target single-well utilization rate value.
8. A long-term production capacity prediction device for shale gas wells, characterized in that, The device includes: The fracturing simulation calculation unit is used to perform fracturing simulation on the un-produced well based on the target fracturing construction pumping parameters and the formation parameters of the formation where the un-produced well is located, and to obtain the fracture parameters of the un-produced well under the target fracturing construction pumping parameters. The shale gas reservoir reserve calculation unit is used to calculate the shale gas reservoir reserves of the unproduced wells based on the well parameters and fracture parameters of the unproduced wells. The target single-well utilization rate calculation unit is used to calculate the single-well utilization rate of each of the multiple wells that have been put into production in the formation according to the actual long-term production capacity of each well, construct the posterior distribution of the single-well utilization rate, and determine the target single-well utilization rate value based on the Markov chain-Monte Carlo sampling method. The long-term production capacity prediction unit is used to calculate the long-term production capacity prediction value of the un-produced well based on the shale gas reservoir reserves of the un-produced well and the target single-well utilization rate value. The long-term production capacity prediction value of the un-produced well is used to guide the development of the un-produced well.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.