A method, system, and equipment for identifying oil well production and heat extraction development modes.

Abstract

This invention relates to the field of oil and gas reservoir and geothermal resource development technology, specifically a method, system, and equipment for determining oil well production and geothermal development modes. The method first generates standardized logging data by selecting standard layers, establishing a standard logging model, and correcting deviations. Then, based on logging and seismic data, well-seismic calibration and structural interpretation are performed to complete the preliminary delineation of development potential areas. Next, a coupled oil and geothermal production numerical model is constructed to simulate production capacity under different operating conditions and identify key influencing factors through sensitivity analysis. Finally, by integrating multi-dimensional results, the optimal mode for separate oil production, separate geothermal production, or simultaneous oil and geothermal production is scientifically determined. This overcomes the shortcomings of traditional methods, such as strong subjectivity and insufficient resource utilization, and significantly improves the efficiency and overall benefits of coordinated development of oil, gas, and geothermal resources.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir and geothermal resource development technology, specifically to a method, system and equipment for identifying oil well production and geothermal development modes. Background Technology

[0002] With the rapid rise in energy consumption, the proven reserves of oil and natural gas are only about 50 and 52 years of production, respectively (as of the end of 2022), making energy security an increasingly serious issue. Meanwhile, geothermal resources, as a vast, clean, and stable renewable energy source, are receiving widespread attention for their development and utilization (e.g., power generation, heating, agriculture). In oilfield development practice, many oil wells possess both oil / gas and geothermal resource potential. However, current development models are mostly limited to single-objective driven approaches, focusing only on "oil extraction" or "thermal extraction," without forming a systematic collaborative extraction judgment and decision-making mechanism. In the oil extraction model, a large amount of associated geothermal energy is reinjected with fluids or directly discharged, resulting in significant energy waste; in the thermal extraction model, the potential associated oil and gas resources within the same geological body are often overlooked, leading to an overall loss of resource value.

[0003] Therefore, in the early stages of development, how to scientifically and quantitatively determine the most suitable development mode for a specific oil well based on geological and engineering data is a technical problem that urgently needs to be solved in this field. Existing technologies lack a collaborative determination method that comprehensively considers geological reservoir formation patterns, reservoir distribution patterns, and engineering parameters. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and equipment for identifying oil well production and heat extraction development modes.

[0005] The technical solution of this invention is as follows: A method for identifying oil well production and heat extraction development modes, comprising: S1. Obtain multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as reference, statistically analyze the frequency distribution and characteristic values ​​of logging parameters of standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. S2. Based on standardized well logging datasets and seismic data, a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region are established by generating synthetic seismic traces and well-seismic calibration. Based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time-stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results. S3. Obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation and solve the problem, calculate the oil production and heat production under different operating conditions, and obtain the numerical simulation results; based on the numerical simulation results, use the sensitivity analysis method to calculate the influence factors of each geological and engineering parameter on the oil production and heat production, and identify key sensitive factors; based on the preliminary regional division results, numerical simulation results and key sensitive factors, determine the final development mode of the target oil well, which includes separate oil production, separate heat production, or simultaneous oil and heat production.

[0006] The specific steps for correcting and normalizing non-formation factor deviations in S1's full-field logging data are as follows: using histogram matching or trend surface analysis techniques, systematic deviations caused by non-formation factors are identified; using the standard distribution pattern of key wells as a benchmark, frequency distribution normalization or mean-variance correction models are employed to perform consistency transformation on all logging curves.

[0007] The operation to establish a unified time-depth conversion relationship across the entire area in S2 is as follows: the reflection coefficient sequence is calculated using sonic logging curves, and a composite seismic trace is generated by convolution operation using extracted seismic wavelets; by comparing the waveform, amplitude, and phase characteristics of the composite seismic trace with the actual seismic trace near the well, quantitative evaluation and dynamic time-depth correction are performed using cross-correlation coefficients; through multi-well joint calibration and stratigraphic consistency verification, the time-depth differences between wells are eliminated, and a unified time-depth conversion relationship across the entire area is established.

[0008] The seismic geological correspondence model in S2 is established through an iterative optimization mechanism: initial seismic wavelets are extracted from well-side seismic trace data, and composite seismic records are created by convolution with well logging data to complete preliminary single-well horizon calibration, establish initial time-depth conversion relationships, and use them as constraints for seismic inversion to generate wave impedance attribute volumes; the inversion results are re-extracted to the well point location and compared with the actual well logging curves to correct the wavelet phase, frequency, and horizon interpretation scheme, and iteratively repeated until the preset convergence accuracy is achieved.

[0009] The seismic tectonic interpretation in S2 includes: establishing a core profile network through wells and ensuring stratigraphic consistency through closure error verification; identifying complex fault zones and completing stratigraphic tracing across the entire area based on wave group characteristics to form a temporal stratigraphic framework.

[0010] The method for obtaining the impact factor in S3 is as follows: Based on the numerical simulation results, a random forest regression model is constructed, with geological parameters and engineering parameters as input features, and oil production and heat recovery as target variables, respectively. The feature importance scores of each parameter are calculated through the model and used as the impact factor.

[0011] The final development mode of the target oil well can be determined in S3 by comparing the preset multi-condition joint judgment threshold range with parameters such as oil saturation, oil-water ratio, reservoir temperature, thermal conductivity, well spacing, discharge rate and fracture aperture.

[0012] A system for distinguishing oil well production and thermal development modes, used to implement the aforementioned method for distinguishing oil well production and thermal development modes, comprising: The standardized logging dataset generation module is used to acquire multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as references, statistically analyze the frequency distribution and characteristic values ​​of logging parameters in the standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. The preliminary regional division result generation module is used to establish a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region by generating synthetic seismic traces and well-seismic calibration based on standardized well logging datasets and seismic data; based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results. The development mode generation module is used to obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation, calculate the oil production and heat production under different operating conditions, and obtain numerical simulation results. Based on the numerical simulation results, the sensitivity analysis method is used to calculate the influence factors of various geological and engineering parameters on oil production and heat production, and identify key sensitive factors. Based on the preliminary regional division results, numerical simulation results, and key sensitive factors, the final development mode of the target oil well is determined. The development mode includes separate oil production, separate heat production, or simultaneous oil and heat production.

[0013] An oil well oil production and heat extraction development mode discrimination device includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the above-mentioned oil well oil production and heat extraction development mode discrimination method.

[0014] A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method for determining oil well production and heat extraction development modes.

[0015] The beneficial effects of this invention are as follows: This invention provides a method for determining the oil and heat production development modes of oil wells. First, by selecting stable and homogeneous standard layers within the oilfield, a standard distribution pattern for logging parameters is established. Then, non-formational factor deviation correction and normalization processing are performed on the logging data from the entire oilfield to construct a unified and reliable standardized logging dataset. Next, based on the standardized logging data and seismic data, well-seismic calibration, seismic tectonic interpretation, and regional division are carried out to establish a high-precision seismic-geological correspondence model, achieving preliminary delineation of oilfield development potential areas. Finally, a three-dimensional oil and heat production coupling model is constructed by combining the geological and engineering parameters of the target well. Numerical models were used to simulate oil and heat production under different operating conditions. Sensitivity analysis was conducted to quantify the influencing factors of each parameter and identify key sensitive factors affecting development effectiveness. Finally, by combining preliminary regional division results, numerical simulation production capacity prediction, and key sensitive factor analysis, the optimal development mode for target oil wells was scientifically determined, including separate oil production, separate heat production, or simultaneous oil and heat production. This integrated and coordinated approach of geological modeling, numerical simulation, and sensitivity analysis overcame the shortcomings of traditional development mode selection, which was characterized by strong subjectivity and insufficient resource utilization. This significantly improved the efficiency and overall benefits of coordinated development of oil and gas and geothermal resources in the oilfield.

[0016] This invention provides a method for determining the oil and heat extraction development modes of oil wells, which solves the problems of single-purpose development, lack of quantitative basis for mode selection, and low synergistic utilization rate of oil and heat resources in traditional oilfield development. Through this method, a scientific decision can be made on whether an oil well with dual potential is suitable for separate oil extraction, separate heat extraction, or simultaneous oil and heat extraction, thereby maximizing the comprehensive development benefits of underground oil and gas and geothermal resources and avoiding resource waste. Furthermore, this invention, through a software-based and model-based determination process, reduces the over-reliance on expert experience in early-stage development decisions, and has replicability and scalability. Attached Figure Description

[0017] The solutions and advantages of this application will become clear to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the attached diagram: Figure 1 This is a flowchart illustrating the method in this embodiment of the invention. Figure 2 This is a schematic diagram illustrating the selection of standard layers and the establishment of a standard distribution pattern for logging parameters in this embodiment of the invention. Figure 3 This is a schematic diagram of fine-scale stratigraphic calibration using a combination of well and seismic testing in this embodiment of the invention. Figure 4This is a simulation result diagram of oil and heat recovery under different geological parameter conditions in this embodiment of the invention; attached. Figure 4 (a) shows the changes in extracted temperature over production time under different fracture aperture conditions, with appendix... Figure 4 (b) shows the results of the cumulative oil production and oil recovery rate as a function of production time under different fracture aperture conditions, with attached figures. Figure 4 (c) shows the results of the cumulative oil production and oil recovery rate as a function of production time under different thermal conductivity conditions. Figure 4 (d) shows the results of the cumulative oil production and oil production rate as a function of production time under different thermal conductivity conditions; Figure 5 This is a diagram showing the calculation results of the influence factors of different influencing factors on heat and oil recovery in this embodiment of the invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the exemplary embodiments of this application clearer, the technical solutions in the exemplary embodiments of this application are described clearly and completely below. Obviously, the described exemplary embodiments are only some embodiments of this application, and not all embodiments.

[0020] This embodiment provides a method for determining the oil and heat production development mode of an oil well. It is applied to the early-stage development decision-making of oil wells with both oil and gas and geothermal potential. Taking a sandstone formation in a certain area as an example, this area has a stable water layer with a thickness of 30-42m and good connectivity, possessing geothermal development potential. Simultaneously, the initial average daily oil production of the development well is 5t / d, with a water cut of 98.8% and a water temperature of 73℃. Later, the water cut reaches 99.3% and the water temperature reaches 78℃. The development mode is unclear. The method is described in [reference needed]. Figure 1 ,include: S1. Obtain multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as reference, statistically analyze the frequency distribution and characteristic values ​​of logging parameters of standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. S2. Based on standardized well logging datasets and seismic data, a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region are established by generating synthetic seismic traces and well-seismic calibration. Based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time-stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results. S3. Obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation, calculate the oil production and heat production under different operating conditions, and obtain the numerical simulation results; based on the numerical simulation results, use sensitivity analysis to calculate the influence factors of various geological and engineering parameters on oil production and heat production, and identify key sensitive factors; based on the preliminary regional division results, numerical simulation results, and key sensitive factors, determine the final development mode of the target oil well, which includes separate oil production, separate heat production, or simultaneous oil and heat production; The specific steps are detailed below.

[0021] S1. Obtain multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, use key wells as references, statistically analyze the frequency distribution and characteristic values ​​of logging parameters in the standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset.

[0022] First, obtain multi-well logging data within the target oilfield area and select marker layers with stable distribution and homogeneous lithology as standard layers.

[0023] Then, using key wells as a reference, the frequency distribution and characteristic values ​​of logging parameters in standard layers are statistically analyzed to establish a standard distribution model of logging parameters. Specifically, using key wells as a reference, the frequency distribution and characteristic values ​​(such as mode and mean) of logging parameters in standard layers are statistically analyzed using the histogram method and / or trend surface method, thereby establishing a standard distribution model of logging parameters that represents the original geological characteristics of the oilfield, providing a unified reference benchmark for the data normalization processing of all subsequent wells.

[0024] First, core wells or standard wells within the study area with complete data, high logging quality, stable formation development, and representative characteristics are selected as key wells. Logging data can be obtained from field logging acquisition data, logging interpretation databases, or existing oilfield logging databases, mainly including curves for natural gamma ray, density, sonic transit time, and resistivity. After depth correction, outlier removal, and standardized sampling interval processing of the above logging data, mudstone layers or marker layers with horizontal continuity and obvious lithological characteristics are selected as standard layers based on the formation correlation results. Logging parameter values ​​within these standard layers are then extracted to form a sample set.

[0025] The sample collection is as follows: , X For the sample set, x N For the first N One sample, N The total number of samples.

[0026] When using the histogram method to statistically analyze the frequency distribution of logging parameters in a standard layer, the sample values ​​are divided into certain intervals, and the number of data points within each interval is counted. Let the first interval be... j The number of samples within each interval is n j The total number of samples in the standard layer is N Then the first j Frequency intervals f j for: .

[0027] Simultaneously calculate the mean and standard deviation of the logging parameters for the standard layer, where the mean... μ for: , Standard deviation σ for: , The mode can be taken as the center value of the interval with the highest frequency in the histogram.

[0028] When using the trend surface method, the coordinates of the key well locations and the mean or mode of the logging parameters of the standard layers are used as inputs to establish a spatial trend function. For example, a first-order trend surface can be represented as: , z(u,v) plane coordinates (u,v) The regional trend prediction values ​​of logging parameters for the standard layer are given, where a0 is the constant intercept term, and a1 and a2 are the first and second gradient coefficients, respectively.

[0029] The obtained standard distribution pattern of logging parameters includes: frequency distribution histograms, mean, standard deviation, mode, and reasonable value ranges for each logging parameter within the standard layer; if the trend surface method is used, it also includes the spatial variation trend function of logging parameters across the entire area. This standard distribution pattern serves as a unified reference benchmark for the subsequent normalization processing of logging data from various wells.

[0030] Finally, non-formation factor bias correction and normalization processing were performed on the logging data of the entire oilfield to generate a standardized logging dataset. Specifically, histogram matching or trend surface analysis techniques were used to identify systematic biases caused by non-formation factors such as differences in instrument performance, changes in wellbore environment, or inconsistent operating standards through multi-well comparisons. Based on the standard distribution pattern of key wells, frequency distribution normalization or mean-variance correction models were used to perform consistency transformation on all logging curves (such as natural gamma, sonic transit time, density, etc.), ultimately generating a standardized logging dataset with unified dimensions and high precision, eliminating non-geological interferences. Figure 2As shown, this provides a reliable data foundation for subsequent accurate stratigraphic division, lithological identification, and reservoir parameter (such as porosity and saturation) calculations.

[0031] S2. Based on standardized well logging datasets and seismic data, a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region are established by generating synthetic seismic traces and well-seismic calibration. Based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time-stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results.

[0032] First, based on standardized well logging datasets and seismic data, a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region were established through synthetic seismic traces and well-seismic calibration. The specific steps for establishing this unified time-depth conversion relationship are as follows: Reflection coefficient sequences are calculated using sonic logging curves, and combined with extracted seismic wavelets for convolution operations to generate synthetic seismic traces; by comparing the waveform, amplitude, and phase characteristics of the synthetic seismic traces with those of actual well-side seismic traces, quantitative evaluation and dynamic time-depth correction are performed using cross-correlation coefficients; and through multi-well joint calibration and stratigraphic consistency verification, inter-well time-depth differences are eliminated, thus establishing a unified time-depth conversion relationship for the entire region. The seismic-geological correspondence model is established through an iterative optimization mechanism: initial seismic wavelets are extracted from well-side seismic trace data, and then convolved with well logging data to create a synthetic seismic record. This completes the initial single-well stratigraphic calibration, establishes an initial time-depth conversion relationship, and serves as a constraint for seismic inversion, including but not limited to model-based inversion or sparse pulse inversion, generating a wave impedance attribute volume. The inversion results are then re-extracted to the well location and compared with the actual well logging curves to correct errors. Feedback is then used to correct the wavelet phase, frequency, and stratigraphic interpretation scheme. This process is iterated repeatedly until the preset convergence accuracy is achieved, thereby ultimately realizing a high-precision deterministic correspondence between seismic data and well geological strata.

[0033] Specifically, the logging curves of each well in the study area were standardized, unifying the dimensions and sampling intervals of logging data such as sonic transit time, density, natural gamma, and resistivity. Anomalies, missing segments, and wellbore enlargement-affected segments were corrected or removed. Based on this, reflection coefficient sequences were calculated using sonic transit time and density curves, and combined with seismic wavelets in the study area to produce synthetic seismic records for each well. The synthetic seismic records were compared with the well-side seismic traces. By adjusting the wellhead reference level, velocity curves, and wavelet phase, the optimal correspondence between the main top and bottom plate interfaces and regionally stable reflecting layers was achieved on the synthetic records and actual seismic profiles, completing the well-seismic calibration. Based on the well-seismic calibration results of multiple wells, the seismic reflection phase axis times of each target layer were extracted and correlated with the measured depths above ground, establishing the conversion relationship between the time domain and depth domain of the study area. Furthermore, a layered velocity modeling method was adopted to comprehensively constrain logging velocity, correction velocity, and seismic layer velocity, forming a velocity model covering the entire area. This velocity model was then used to perform time-depth conversion on the target layer and its surrounding rock layers above and below, obtaining the depth structural surfaces of each layer. Through statistical analysis and iterative correction of calibration errors at different well locations, the time-depth conversion errors of the main target layers in the entire area were controlled within the allowable range, thus establishing a unified time-depth conversion relationship for the entire area. Correspondence analysis was performed on the thickness of the identified seismic layers, the lithology of the top and bottom plates, gas content, and key geological interfaces at the well points with seismic reflection characteristics. The geological meanings represented by different seismic reflection axes, amplitudes, frequencies, and continuity characteristics were clarified, forming a correspondence model between seismic reflection characteristics and coal seams, surrounding rocks, and structural interfaces, i.e., a seismic-geological correspondence model. Figure 3 As shown.

[0034] Then, based on the seismic geological correspondence model, seismic tectonic interpretation was carried out to generate a time-stratigraphic framework. Combined with geological information, regional division was carried out to obtain preliminary regional division results, including oil reservoir development areas, and / or thermal development areas, and / or oil-thermal co-production development areas, as shown in Table 1. Substitute well areas were identified, such as Hua 9 and Hei 47, with water layer thickness of 35-90m, fault-step structures, and oil-water transition zones, where oil and heat can be produced simultaneously through large-volume fluid extraction. Low-saturation development areas, such as Hua 16 and Hei 53, have favorable structures and can be improved by increasing fluid volume and oil production.

[0035] Table 1. Optimized Areas Based on Geological Characteristics

[0036] In the process of seismic tectonic interpretation, this includes establishing a high-quality backbone profile network through wells, ensuring stratigraphic consistency through closure error verification, identifying complex fault zones, and completing full-area stratigraphic tracing based on wave group characteristics to form a temporal stratigraphic framework. Preferably, further verification can be performed by combining the interpretation results with three-dimensional data volumes to further improve the closure reliability of the interpretation results and lay the foundation for subsequent research.

[0037] S3. Obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation and solve the problem, calculate the oil production and heat production under different operating conditions, and obtain the numerical simulation results; based on the numerical simulation results, use the sensitivity analysis method to calculate the influence factors of each geological and engineering parameter on the oil production and heat production, and identify key sensitive factors; based on the preliminary regional division results, numerical simulation results and key sensitive factors, determine the final development mode of the target oil well, which includes separate oil production, separate heat production, or simultaneous oil and heat production.

[0038] First, obtain the geological and engineering parameters of the target well and the target layer. The geological parameters include the thickness of the sublayer, geothermal gradient, sublayer permeability, sublayer porosity, oil saturation, water saturation, thermal conductivity, and the ratio of oil to water layers. The engineering parameters include whether fracturing is required, injection-production well spacing, discharge rate, and injection temperature.

[0039] Then, the geological and engineering parameters of the target well and the target layer are obtained, and a three-dimensional oil and heat production numerical model of the target well is established. Boundary conditions and initial values ​​are set. In this embodiment, the horizontal heat production well adopts a one-injection-one-production well arrangement. Both the injection well and the production well are horizontal wells and are arranged parallel to the main seepage direction of the reservoir. The outer boundary of the model is set as a closed boundary in terms of seepage, satisfying the following conditions. q•n=0, q The Darcy seepage velocity vector, n The unit outward normal vector of the model's outer boundary; the injection well adopts a constant flow injection boundary, satisfying... Q inj =Q 0 , Q inj The instantaneous injection volumetric flow rate of the injection well. Q 0 To preset a constant injection flow rate constant, and simultaneously set the injection fluid temperature to... T inj =T c， T inj Temperature of the fluid injected into the wellbore. T inj To establish a constant cryogenic injection temperature constant, numerical simulations were performed to calculate the oil recovery and heat recovery under different operating conditions, yielding the numerical simulation results. Some initial values ​​are shown in Table 2, and the optimal development method conditions are shown in Table 3. Some numerical simulation results are also available in [Table 3]. Figure 4 Table 3 presents the simulation results of oil and heat recovery under different engineering parameters. Among them, Scheme 12 (displacement 900 m³ / day) has a higher cumulative oil recovery, but the outlet temperature drops faster.

[0040] Table 2. Basic Parameter Settings

[0041] Table 3. Preferred Conditions for Each Development Method

[0042] The modeling process includes: Step 1, establishing a 3D geometric model, with the model size controlled within a well depth of 2000m; Step 2, constructing the wellbore in the geometric model based on the well coordinates, and designing the fracturing point locations based on cluster spacing and cluster number; Step 3, constructing a mesh and assigning permeability and porosity attributes to the mesh cells; Step 4, adding material property parameters: formation temperature, sublayer thickness, sublayer permeability, sublayer porosity, oil saturation ratio, water saturation, thermal conductivity, and oil-water ratio; Step 5, adding boundary conditions and initial values: whether fracturing is required, injection-production well spacing, displacement, and injection temperature; Step 6, setting the solution time step; Step 7, solving the model to establish a numerical model for oil and heat recovery calculations. The simulation results for oil and heat recovery under different geological parameters (taking fracture aperture and recovery temperature as examples) can be found in [reference needed]. Figure 4 , Figure 4 (a) shows the changes in extraction temperature over production time under different fracture aperture conditions. Figure 4 (b) shows the results of the cumulative oil production and oil recovery rate as a function of production time under different fracture aperture conditions. Figure 4 (c) shows the results of the cumulative oil recovery and oil recovery rate as a function of production time under different thermal conductivity conditions. Figure 4 (d) shows the results of the cumulative oil production and oil production rate as a function of production time under different thermal conductivity conditions.

[0043] Next, based on the numerical simulation results, sensitivity analysis was used to calculate the influencing factors of various geological and engineering parameters on oil production and heat recovery, and key sensitive factors were identified, such as... Figure 5 As shown, well spacing, discharge rate, oil saturation, and oil-water ratio were identified as key sensitive factors.

[0044] The method for obtaining the influencing factors is as follows: based on the numerical simulation results, a random forest regression model is constructed. Geological parameters and engineering parameters are used as input features, and oil production and heat production (exit temperature) are used as target variables to train the random forest regression model respectively. The feature importance score of each parameter is calculated by the model and used as the influencing factor to quantify its impact on oil and heat production.

[0045] Finally, based on the preliminary regional division results, numerical simulation results, and key sensitive factors, the final development mode of the target oil well is determined. The decision can be made by comparing the parameters of oil saturation, oil-water ratio, reservoir temperature, thermal conductivity, well spacing, discharge rate, and fracture aperture with the preset multi-condition joint discrimination threshold range, as shown in Table 4. The development modes include separate oil production, separate heat production, or simultaneous oil and heat production.

[0046] Table 4. Preferred Conditions for Each Development Method

[0047] This embodiment also provides a system for distinguishing oil well production and thermal development modes, used to implement the above-mentioned method for distinguishing oil well production and thermal development modes, including: The standardized logging dataset generation module is used to acquire multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as references, statistically analyze the frequency distribution and characteristic values ​​of logging parameters in the standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. The preliminary regional division result generation module is used to establish a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region by generating synthetic seismic traces and well-seismic calibration based on standardized well logging datasets and seismic data; based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results. The development mode generation module is used to obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation, calculate the oil production and heat production under different operating conditions, and obtain numerical simulation results. Based on the numerical simulation results, the sensitivity analysis method is used to calculate the influence factors of various geological and engineering parameters on oil production and heat production, and identify key sensitive factors. Based on the preliminary regional division results, numerical simulation results, and key sensitive factors, the final development mode of the target oil well is determined. The development mode includes separate oil production, separate heat production, or simultaneous oil and heat production.

[0048] This embodiment also provides an oil well oil production and heat extraction development mode discrimination device, including a processor and a memory, wherein the processor executes the computer program stored in the memory to implement the above-mentioned oil well oil production and heat extraction development mode discrimination method.

[0049] This embodiment also provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described method for determining oil well production and heat extraction development modes.

[0050] This embodiment provides a method for determining the oil and heat recovery development modes of oil wells. First, by selecting standard layers and standardizing logging data, non-geological interference is eliminated, providing a unified and reliable data foundation for subsequent analysis. Second, through well-seismic calibration and iterative inversion, an accurate seismic-geological correspondence model is established, and based on this, structural interpretation and regional division are performed, achieving a preliminary macroscopic assessment of reservoir and heat recovery potential. Then, geological parameters, including reservoir properties, fluid properties, and thermal conductivity, as well as engineering parameters, including fracturing, well spacing, displacement, and injection temperature, are systematically introduced to construct an oil and heat recovery numerical model, enabling quantitative simulation of oil and heat recovery under different development conditions. Finally, by identifying key control factors through sensitivity analysis and combining them with preliminary zoning results, a complete and quantitative development mode discrimination chain is formed, encompassing preliminary geological assessment, numerical simulation, sensitivity analysis, and comprehensive decision-making.

[0051] This embodiment provides a method for determining the oil and heat extraction development modes of oil wells, which solves the problems of single-purpose development, lack of quantitative basis for mode selection, and low synergistic utilization rate of oil and heat resources in traditional oilfield development. Through this method, a scientific decision can be made on whether an oil well with dual potential is suitable for separate oil extraction, separate heat extraction, or simultaneous oil and heat extraction, thereby maximizing the comprehensive development benefits of underground oil and gas and geothermal resources and avoiding resource waste. Furthermore, this method, through a software-based and model-based determination process, reduces the over-reliance on expert experience in early-stage development decisions, and has replicability and scalability.

[0052] While exemplary embodiments of the invention have been described herein, many other variations or modifications conforming to the principles of the invention can be directly determined or derived from the disclosure of this invention without departing from its spirit and scope. Therefore, the scope of the invention should be understood and recognized to cover all such other variations or modifications.

Claims

1. A method for identifying oil well production and heat extraction development modes, characterized in that, include: S1. Obtain multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as reference, statistically analyze the frequency distribution and characteristic values ​​of logging parameters of standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. S2. Based on standardized well logging datasets and seismic data, a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region are established by generating synthetic seismic traces and well-seismic calibration. The operation to establish a unified time-depth conversion relationship for the entire region is as follows: the reflection coefficient sequence is calculated using sonic logging curves, and a synthetic seismic trace is generated by convolution operation using extracted seismic wavelets; by comparing the waveform, amplitude, and phase characteristics of the synthetic seismic traces with the actual seismic traces near the wells, quantitative evaluation and dynamic time-depth correction are performed using cross-correlation coefficients; through multi-well joint calibration and stratigraphic consistency verification, the time-depth differences between wells are eliminated, and a unified time-depth conversion relationship for the entire region is established. The seismic-geological correspondence model is established through an iterative optimization mechanism: the initial seismic wavelet is extracted using the seismic trace data near the wells, convolved with well logging data to produce a synthetic seismic record, and preliminary single-well stratigraphic calibration is completed to establish an initial time-depth conversion relationship, which is used as a constraint for seismic inversion to generate a wave impedance attribute volume; the inversion results are re-extracted to the well point location and compared with the actual well logging curves for error comparison, and the wavelet phase, frequency, and stratigraphic interpretation scheme are corrected, iterating repeatedly until the preset convergence accuracy is reached. Based on the earthquake-geological correspondence model, earthquake tectonic interpretation was carried out, a time-stratigraphic framework was generated, and regional division was carried out in combination with geological information to obtain preliminary regional division results. S3. Obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation and solve the problem, calculate the oil production and heat production under different working conditions, and obtain the numerical simulation results; based on the numerical simulation results, use the sensitivity analysis method to calculate the influence factors of various geological and engineering parameters on oil production and heat production, and identify key sensitive factors. The method for obtaining the impact factor is as follows: Based on the numerical simulation results, a random forest regression model is constructed. Geological parameters and engineering parameters are used as input features, and oil production and heat recovery are used as target variables. The feature importance scores of each parameter are calculated through the model and used as the impact factor. Based on the preliminary regional division results, numerical simulation results, and key sensitive factors, the final development mode of the target oil well is determined. The development modes include separate oil production, separate heat production, or simultaneous oil and heat production.

2. The method for determining oil well production and heat recovery development modes according to claim 1, characterized in that, The specific steps for correcting and normalizing non-formation factor deviations in S1's full-field logging data are as follows: using histogram matching or trend surface analysis techniques, systematic deviations caused by non-formation factors are identified; using the standard distribution pattern of key wells as a benchmark, frequency distribution normalization or mean-variance correction models are employed to perform consistency transformation on all logging curves.

3. The method for determining oil well production and heat recovery development modes according to claim 1, characterized in that, The seismic tectonic interpretation in S2 includes: establishing a core profile network through wells and ensuring stratigraphic consistency through closure error verification; identifying complex fault zones and completing stratigraphic tracing across the entire area based on wave group characteristics to form a temporal stratigraphic framework.

4. The method for determining the oil well production and heat recovery development mode according to claim 1, characterized in that, The final development mode of the target oil well can be determined in S3 by comparing the preset multi-condition joint judgment threshold range with parameters such as oil saturation, oil-water ratio, reservoir temperature, thermal conductivity, well spacing, discharge rate and fracture aperture.

5. A system for identifying oil well production and thermal recovery modes, used to implement the method for identifying oil well production and thermal recovery modes as described in claim 1, characterized in that, include: The standardized logging dataset generation module is used to acquire multi-well logging data within the target oilfield area, select standard layers with stable distribution and homogeneous lithology, take key wells as references, statistically analyze the frequency distribution and characteristic values ​​of logging parameters in the standard layers, establish a standard distribution pattern of logging parameters, and perform non-formation factor deviation correction and normalization processing on the logging data of the entire oilfield to generate a standardized logging dataset. The preliminary regional division result generation module is used to establish a unified time-depth conversion relationship and seismic-geological correspondence model for the entire region by generating synthetic seismic traces and well-seismic calibration based on standardized well logging datasets and seismic data; based on the seismic-geological correspondence model, seismic tectonic interpretation is carried out, a time stratigraphic framework is generated, and regional division is performed in combination with geological information to obtain preliminary regional division results. The development model generation module is used to obtain the geological and engineering parameters of the target well and the target layer, establish a three-dimensional oil and heat production numerical model of the target well, set boundary conditions and initial values, perform numerical simulation and solve the problem, calculate the oil production and heat production under different working conditions, and obtain the numerical simulation results. Based on the numerical simulation results, the sensitivity analysis method is used to calculate the influence factors of various geological and engineering parameters on oil production and heat production, and identify key sensitive factors. Based on the preliminary regional division results, numerical simulation results, and key sensitive factors, the final development mode of the target oil well is determined. The development modes include separate oil production, separate heat production, or simultaneous oil and heat production.

6. A device for distinguishing oil well production and heat extraction development modes, characterized in that, It includes a processor and a memory, wherein when the processor executes a computer program stored in the memory, it implements the method for determining the oil well production and heat extraction development mode as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the method for determining the oil well production and heat extraction development mode as described in any one of claims 1-4.

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