Experimental simulation and quantitative evaluation method for pore and fracture co-evolution in carbonate rock

Abstract

The present application relates to the technical field of petroleum geology experiment, and particularly relates to an experimental simulation and quantitative evaluation method suitable for carbonate pore and fracture co-evolution. The method comprises the following steps: obtaining the evolution paths of the paleo-temperature, paleo-circumferential pressure, paleo-axial pressure and paleo-pore pressure of the target reservoir through geological process inversion, and converting them into experimental loading parameters; preparing multiple parallel samples and performing initial state multi-scale characterization; implementing the same temperature and pressure co-loading path on the samples under different pore pressure conditions in a high temperature and high pressure triaxial experimental system; performing fine comparative analysis on the samples after the experiment; and finally establishing a reservoir quality evaluation chart through calculation of the unique parameters such as the anti-compaction coefficient, pore and fracture co-enhancement index and fracture connectivity efficiency. The present application solves the problems of disconnection between traditional experiments and geological processes and insufficient multi-field coupling simulation, and realizes accurate revelation and quantitative evaluation of the super-deep carbonate pore and fracture co-evolution mechanism.

Description

Technical Field

[0001] This invention relates to the field of petroleum geological experimental technology, and more specifically, to experimental simulation and quantitative evaluation methods applicable to the co-evolution of pores and fractures in carbonate rocks. Background Technology

[0002] Carbonate reservoirs are a crucial component of global oil and gas resources and a strategic core for deep and ultra-deep oil and gas exploration in my country. In recent years, large oil and gas fields have been discovered in the Ordovician, Cambrian, and Sinian marine carbonate strata in the Tarim Basin and Sichuan Basin, confirming the enormous reservoir potential in carbonate rocks at depths of 4500–6000 m and ultra-deep depths of >6000 m. However, these reservoirs have generally undergone long-term burial and complex diagenetic alteration under high-temperature, high-pressure, and fluid conditions. Their reservoir space is not independently controlled by a single pore or fracture, but rather is the result of the synergistic evolution, shaping, and maintenance of both the pore and fracture systems. Fractures improve matrix permeability, while pores provide the main reservoir; this synergistic mechanism is key to determining the distribution of the "sweet spot" in ultra-deep reservoirs.

[0003] The physicochemical environment of ultra-deep reservoirs is extreme and highly variable. Temperatures often exceed 160 °C, and formation pressure coefficients can reach 1.8–2.2. High temperatures alter mineral reaction rates and rock mechanical behavior, while high pressure (especially effective stress) dominates physical compaction and fracture closure. High pore pressure, supported by the effective stress law (σ' = σ − Pp), is the core factor in maintaining porosity and fracture aperture. Therefore, to accurately reconstruct the pore-fracture co-evolution process of reservoirs throughout geological history, it is essential to simultaneously control temperature, confining pressure, axial pressure, and pore pressure in experiments to achieve multi-field coupled simulation of real geological pathways.

[0004] While current research has made some progress, significant limitations remain. Existing triaxial high-temperature and high-pressure experiments can apply temperature and confining pressure, but most loading paths are simple linear or constant-load methods, disconnected from the multi-stage, nonlinear geomechanical processes actually experienced by reservoirs. Parameter settings are often limited by equipment capabilities or experience, making it difficult to correspond to the temperature and pressure history of specific geological stages. More importantly, existing mechanical experiments generally lack independent and precise control of pore pressure, failing to achieve coordinated loading of the four fields of "temperature-confining pressure-axial pressure-pore pressure," making it difficult to reveal the crucial role of high pore pressure in porosity preservation and fracture aperture maintenance.

[0005] In reservoir structure characterization, traditional methods such as thin sections, CT, and SEM typically analyze pores or fractures from a single scale and perspective, lacking a comprehensive, three-dimensional, and multi-scale quantitative description of the key evolutionary mechanism of "pore-fracture synergy." The data from different characterization methods are scattered and lack fusion, making it difficult to establish a synergistic evolution parameter system for the pore network-fracture system, and also difficult to explain key questions such as how fractures connect isolated pores and how pore structure responds to fracture activity with temperature and pressure evolution.

[0006] In summary, current technologies are unable to systematically and quantitatively simulate the pore-fracture co-evolution of ultra-deep carbonate rocks in actual geological history, and also lack experimental methods and evaluation systems that can reveal the dynamic changes of pore-fracture structures under high temperature, high pressure, and multi-field coupling conditions.

[0007] Given the shortcomings of the above-mentioned schemes, there is an urgent need for experimental simulation and quantitative evaluation methods applicable to the co-evolution of pores and fractures in carbonate rocks to solve these problems. Summary of the Invention

[0008] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide a quantitative evaluation method for the co-evolution of pores and fractures in ultra-deep carbonate rocks under high temperature, high pressure, and pore pressure, comprising:

[0009] S1 extracts geological process constraint parameters, integrates drilling core, logging, seismic and tectonic evolution data, reconstructs burial history, thermal history, pore pressure evolution history and diagenesis-pore evolution history, identifies key transformation stages that have a decisive impact on the formation of the current reservoir quality, and extracts the start and end temperatures, confining pressure, axial pressure and geological duration of each stage.

[0010] S2, based on the time-temperature-stress equivalence principle, converts geological-scale time into laboratory loading time, generates an experimental loading scheme table containing target values ​​of multi-stage temperature, confining pressure, axial pressure and pore pressure, loading rate and holding time, and sets five parallel constant pore pressure conditions, namely 0MPa, 20MPa, 50MPa, 60MPa and 70MPa.

[0011] S3, in a high-temperature and high-pressure triaxial servo system with independent control functions for four fields: temperature, confining pressure, axial pressure and pore pressure, multi-stage collaborative loading is implemented according to the experimental loading scheme table, and the differential stress is increased according to the preset path to simulate the structural fracture process.

[0012] S4. Multi-scale characterization was carried out before and after the experiment, including at least: thin section and scanning electron microscopy identification, porosity and permeability testing, nuclear magnetic resonance T2 spectrum testing, and microfocus CT scanning and three-dimensional reconstruction. Rigid registration and image difference were performed on the CT volume data before and after the experiment to obtain parameters such as the volume of newly formed cracks, the volume of effectively connected pores, the crack density increment, the volume of closed pores, and the change in coordination number.

[0013] S5. Based on multi-source data before and after the experiment, three indicators were calculated: pore pressure retention coefficient, pore-fracture synergy index, and fracture connectivity efficiency. Curves showing the variation of each indicator with pore pressure were plotted. A two-dimensional reservoir quality evaluation chart was constructed with the pore pressure retention coefficient as the abscissa and the pore-fracture synergy index as the ordinate, which was used to interpret the pore-fracture synergistic evolution mechanism and identify high-quality reservoirs.

[0014] As a preferred technical solution of the present invention: the experimental loading described in S3 is divided into a simulated deep burial compaction stage, a steady-state maintenance stage and a structural fracture stage: in the first two stages, the temperature and confining pressure are increased and maintained at a constant or segmented constant rate according to the scheme table; in the structural fracture stage, the axial pressure is gradually increased under constant temperature and confining pressure conditions to increase the differential stress; the five parallel samples are only different in the constant value of pore pressure, and the rest of the loading paths are the same.

[0015] As a preferred technical solution of the present invention: the effective stress is monitored in real time throughout the experiment, and the confining pressure is always kept at least 20 MPa higher than the pore pressure to prevent hydraulic fracturing and ensure the stability of the four independent control fields; when a sudden drop in axial pressure or other safety thresholds are detected, the sample is unloaded and recovered in an orderly manner in the order of "reducing axial pressure - reducing confining pressure and pore pressure - cooling".

[0016] As a preferred technical solution of the present invention: after rigid registration, the CT three-dimensional data of S4 is subjected to image difference, and the differential voxels are extracted by threshold segmentation. The total volume of newly formed cracks, crack density increment, closed pore volume, newly formed or connected pore volume and coordination number change are quantified. The above results are then analyzed in correspondence with pore pressure preservation coefficient, pore-crack synergistic effect index and crack connectivity efficiency to reveal the spatial characteristics of pore compression, crack propagation and pore-crack interconnection.

[0017] As a preferred technical solution of the present invention: based on the multi-source characterization data obtained before and after the experiment in step S4 of the method, a quantitative relationship between pore pressure and three evaluation indicators is constructed. The relationship includes the correspondence between pore pressure preservation coefficient and pore pressure, the correspondence between pore-fracture synergistic efficiency index and pore pressure, and the correspondence between fracture connectivity efficiency and pore pressure. This is used to reveal the control law of pore pressure on the pore-fracture synergistic evolution process and to identify the optimal pore pressure window.

[0018] As a preferred technical solution of the present invention: Based on the pore pressure preservation coefficient and pore-fracture synergy index calculated in step S5 of the method, a two-dimensional reservoir quality evaluation chart is established, with the pore pressure preservation coefficient as the abscissa and the pore-fracture synergy index as the ordinate. According to the distribution characteristics of the samples in the chart, the reservoirs are divided into fracture-enhanced type, potential risk type, compaction failure type, and pore-fracture stable type. The chart, combined with pore pressure and lithological information annotation, is used for high-quality reservoir identification, sweet spot evaluation, and predictive analysis.

[0019] As a preferred technical solution of the present invention: Based on the nuclear magnetic resonance and CT differential analysis results obtained in step S4 of the method, the reservoir evolution mechanism is determined by combining the trend of nuclear magnetic resonance T2 spectrum change with the change result of pore-fracture synergy index: when the pore-fracture synergy index is significantly high and the T2 spectrum shifts to the right, it is determined to be fracture-dominated synergy; when the index is close to neutral and the T2 spectrum changes uniformly, it is determined to be homogeneous compaction or equilibrium evolution type; when the index is low and the T2 spectrum shifts to the left, it indicates that the pore throat is closed or the number of ineffective fractures increases, and the effectiveness of seepage channels is further analyzed in combination with the change of fracture connectivity efficiency.

[0020] As a preferred technical solution of the present invention: the pore pressure preservation coefficient mentioned in S5 is used to characterize the rock's resistance to compaction under different combinations of pore pressure and confining pressure. The lower the value, the easier it is for the pores to be preserved for a long time under high pore pressure. The input of the coefficient includes the porosity before and after the experiment, as well as the confining pressure and pore pressure at the beginning and end of the experiment.

[0021] As a preferred technical solution of the present invention: the pore-crack synergistic enhancement index mentioned in S5 is used to measure the ratio between the relative improvement of permeability and the relative change of porosity, so as to determine the relative contribution of cracks to the improvement of seepage capacity; when the index is significantly higher than the threshold, it indicates that the seepage channel is mainly controlled by crack enhancement.

[0022] As a preferred technical solution of the present invention: the effective pore volume that can be communicated or newly generated per unit crack volume described in S5 reflects the efficiency of crack activation and connectivity of matrix pores, and the input includes at least the total volume of newly generated cracks and the volume of effective pores that are communicated.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] By using the paleotemperature-pressure path derived from basin simulation inversion as a constraint condition for experimental loading, this invention overcomes the problem of blind setting of traditional experimental parameters. This geological process-driven experimental design ensures that the simulated environment closely matches the actual underground geological history, and the experimental results more realistically reflect the actual evolution path of reservoir porosity and fractures throughout geological history. This directly leads to a qualitative leap in prediction accuracy, providing geologically sound and highly reliable experimental data support for oilfield target area selection and sweet spot prediction, and is expected to significantly reduce exploration risks.

[0025] This invention, for the first time, systematically and precisely controls the four fields of temperature, confining pressure, axial pressure, and pore pressure in experiments. In particular, by setting parallel experiments with different pore pressure gradients, it is possible to accurately isolate and quantify the independent contribution of pore pressure, a key geological factor, to pore evolution. This multiphysics coupling simulation capability makes it possible to realistically reproduce the deep-ultra-deep "high temperature-high pressure-high pore pressure" environment in the laboratory, greatly deepening the understanding of reservoir retention mechanisms under overpressure conditions and providing a key technical means for advancing the theory of ultra-deep reservoir formation.

[0026] By integrating multi-scale characterization techniques such as rock thin sections, CT scans, nuclear magnetic resonance, and porosity-permeability testing, and comparing and analyzing results before and after experiments, this invention achieves three-dimensional, visualized, and quantitative monitoring of the entire process of pore-fracture synergistic evolution. In particular, the invention innovatively proposes quantitative indicators such as the pore pressure-to-pore retention coefficient (Kc), the pore-fracture synergistic enhancement index (SEI), and the fracture connectivity efficiency (FCE), constructing a novel quantitative reservoir evaluation system. This system transforms the previously vague concept of "pore-fracture synergy" into precise and comparable numerical indicators, enabling accurate and quantitative evaluation of reservoir quality and its controlling factors, significantly improving the scientific rigor of reservoir research and its relevance to practical application.

[0027] The method described in this invention has clear steps, forming a logically rigorous and interconnected complete technical chain from geological analysis to experimental simulation and mechanism analysis. Although it involves multiple disciplines, each step is supported by mature technologies, and the overall solution has good repeatability and operability, which is conducive to the promotion and application of the method and provides a practical solution for promoting technological progress in the industry.

[0028] In summary, this invention, through methodological innovation, has produced significant beneficial effects in terms of the relevance of experiments, the realism of simulations, the accuracy of characterization, and the scientific nature of evaluation. It has important theoretical value and practical significance for promoting the exploration and development of ultra-deep oil and gas. Attached Figure Description

[0029] Figure 1 The flowchart of the experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks proposed in this invention;

[0030] Figure 2 This diagram illustrates the specific steps of the S1 stage in the experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks, as proposed in this invention.

[0031] Figure 3 A diagram showing the burial history and stratigraphic temperature evolution of the Xiaoerbulak Formation in Tazhong.

[0032] Figure 4 Diagram showing the diagenetic sequence and pore evolution history of the Xiaoerbulak Formation dolomite reservoir in the central Tarim Basin;

[0033] Figure 5 A diagram showing the specific steps for determining the S2 stage;

[0034] Figure 6 This is a characterization diagram of the initial state of the sample;

[0035] Figure 7 A diagram showing the specific steps for determining the S3 stage;

[0036] Figure 8 This is a characterization diagram of the final state of the sample;

[0037] Figure 9 For differential voxel recognition;

[0038] Figure 10 A diagram showing the specific steps for determining the S4 stage;

[0039] Figure 11 A diagram showing the specific steps for determining the S5 stage;

[0040] Figure 12 This is a graph showing the "Kc-Pp" relationship.

[0041] Figure 13 This is a graph showing the "SEI-Pp" relationship.

[0042] Figure 14 This is a graph showing the "FCE-Pp" relationship.

[0043] Figure 15 This is a reservoir quality evaluation chart for "Kc-SEI". Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with embodiments and appendices. Figure 1-15 The present invention will be further described in detail below. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0045] This invention provides an experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks. The following describes the invention in detail with reference to the implementation steps, but the scope of protection of this invention is not limited thereto.

[0046] S1: Extraction of geological process constraint parameters, which specifically includes:

[0047] 1.1: Basic geological data acquisition and geological framework establishment, including:

[0048] Data source integration: The system collects drilling cores, logging data (including acoustic, density, neutron, resistivity and imaging logging, etc.), seismic profiles, regional geological maps and tectonic evolution research reports of the target block.

[0049] Stratigraphic and tectonic framework modeling: Based on the above data, a detailed stratigraphic framework for the target stratum (such as the Yijianfang Formation) is established, clarifying its top and bottom interfaces, thickness distribution, and internal structure. Combined with the regional tectonic evolution history, the major tectonic movements experienced by the target area (such as the Middle Caledonian and Himalayan periods), their nature (compression, extension, strike-slip), and their intensity are determined.

[0050] 1.2: Quantitative reconstruction of key geological historical processes, including:

[0051] Burial history reconstruction: Using basin simulation software (such as Petromod and BasinMod), based on stratigraphic framework and erosion analysis, the burial depth-time curve of the target reservoir from deposition to the present is reconstructed. The average density curve of the overlying strata obtained from integrated density logging data is used to convert the burial depth into the evolution path of paleostatic pressure (paleoconfining pressure, σ3), calculated using the following formula: , where ρ(g) is the density of the strata, g is the gravitational acceleration, and h is the burial depth. This formula accumulates the weight of the overlying strata into static rock pressure, which is used to construct the evolution path of σ3 over geological time.

[0052] Thermal history reconstruction: utilizing temperature-sensitive paleotherm data (such as vitrinite reflectance (R0)). o The basin model was calibrated and constrained using parameters such as fluid inclusion homogenization temperature and CO2 cluster isotope (∆47). The calibrated thermal model was then run to extract the paleotemperature (T) evolution history of the target reservoir, particularly its maximum paleotemperature (T). max (and the time it takes to reach that temperature).

[0053] Paleopore pressure (Pp) recovery: Using the sonic-density logging cross-plot method (applying Bowers' formula) or the equivalent depth method, combined with formation test data (such as MDT, RFT pressure points) and fluid inclusion PVT simulation, the evolution history of formation paleopore pressure is recovered, and key overpressure development stages and their pressure coefficients are identified.

[0054] 1.3: Identification and experimental pathway design of key diagenetic-mechanical alteration stages, including:

[0055] Analysis of diagenetic sequence and porosity evolution history: Based on core, thin section, scanning electron microscopy, cathodoluminescence, and geochemical data, a detailed diagenetic sequence was established to determine the type, stage, intensity, and formation sequence of each major diagenetic process (such as compaction, cementation, dissolution, fracturing, and pressure solution). Combining the above diagenetic information with reservoir physical property data, the porosity evolution history was inverted to identify key geological periods that led to a significant increase in porosity (constructive diagenesis) or a decrease in porosity (destructive diagenesis).

[0056] Experimental Target Stage Identification and Parameter Extraction: Based on comprehensive burial history, thermal history, pressure history, stress history, and diagenetic-porosity evolution history, key alteration stages that decisively influence the current reservoir quality were identified, such as the "rapid deep burial-strong compaction stage," the "tectonic compression-fracture development stage," or the "overpressure formation-porosity preservation stage." From the aforementioned evolutionary history models, the initial and final parameters corresponding to these key stages were extracted, including: initial temperature (T). i ) and endpoint temperature (T) f ), initial confining pressure (σ) 3,i ) and the confining pressure at the endpoint (σ 3,f ), initial axial compression (σ1, i ) and the final axial pressure (σ1, f ), and the geological duration of this stage (t geo ).

[0057] Quantitative transformation of laboratory loading paths: applying the time-temperature-strain rate equivalence principle (based on the rock creep constitutive equation, Power-Law or Dislocation Creep model for conversion), to transform geological processes on a million-year scale (t geo Compression into laboratory hour scale (t) lab The loading rate of ( ). The initial temperature.

[0058] Among them, temperature loading rate: ；

[0059] Confining pressure loading rate ；

[0060] The axial compression loading path is designed based on the evolution of differential stress. For example, during the construction phase, the axial compression loading rate is set to simulate a specific differential stress growth.

[0061] 1.4: Experimental Scheme Output (This scheme will serve as the direct input command for the experimental machine control in step S3).

[0062] Finally, a structured experimental loading scheme table is generated. This scheme clearly specifies the initial and final values ​​of temperature, confining pressure, axial pressure, and pore pressure, loading rate, holding time, and the sequence of constant pore pressure values ​​(0, 20, 50, 60, 70 MPa) required for parallel experiments in each experimental stage (such as burial, steady state, and construction).

[0063] S2: Sample preparation and multi-scale characterization of the initial state (this step aims to obtain the "initial state fingerprint" of the experimental sample, providing a benchmark for subsequent evolution analysis), specifically as follows:

[0064] 2.1: Sample screening and precision preparation, including: Representative sample screening: Core samples were selected from the target stratigraphic level, and high-porosity carbonate rock samples that could represent the main reservoir space in the area were selected based on petrographic analysis, such as residual oolitic dolomite, granular porous dolomite, or fracture-cavity limestone. Initial screening was performed using industrial CT scanning to ensure that the lithology, physical properties, and structural characteristics of the five parallel samples were basically consistent and that there were no obvious macroscopic defects.

[0065] Precision machining of standard specimens: A diamond core drilling machine is used to drill core columns perpendicular to the bedding direction. A high-precision rock cutting machine is used to cut the core columns into blanks with a length of about 52mm. The two ends of the specimen are ground flat on an end face grinder, and finally processed into standard cylindrical specimens with a diameter of 25mm±0.1mm and a height of 50mm±0.2mm. A dial indicator is used to check the non-parallelism between the two ends of the specimen to ensure that it is less than 0.02mm. A vernier caliper is used to check the diameter deviation to ensure that it is less than 0.1mm.

[0066] 2.2: Multi-scale initial characterization technology system, including:

[0067] Petrological and mineralogical identification: Small plungers are drilled from adjacent parts of the sample column to prepare cast thin sections; mineral composition identification, structural description, and diagenetic sequence analysis are performed using a polarizing microscope; generational relationships of cement are observed using a cathodoluminescence microscope; micro-area morphology and elemental distribution are observed using a scanning electron microscope (equipped with an EDS spectrometer); and parameters such as mineral composition content, porosity, microcrack density / length / aperture, and grain contact relationships are quantitatively analyzed.

[0068] Precise testing of rock physical parameters: In accordance with the standard SY / T6385-2016, the initial porosity (Φ0) was determined using a helium porosimeter; the initial gas permeability (K0) was determined using a pulse attenuation permeability meter; each sample was measured 3 times and the average value was taken to ensure that the measurement error was ±0.5% for porosity and ±5% for permeability.

[0069] Nuclear magnetic resonance (NMR) pore structure analysis: The sample was dried in a 100°C oven for 24 hours to constant weight; a vacuum pressure saturation device was used to simulate formation water and 100% saturation of the sample. A low-field NMR analyzer was used for testing, and the T2 relaxation time spectrum was obtained through inversion fitting. The pore size distribution curve, bound fluid saturation (Swi), and movable fluid saturation (Smf) were calculated. The T2 cutoff value was determined by centrifugation (centrifugal force 300 psi, duration 2 hours).

[0070] Microfocus CT 3D Scanning and Digital Reconstruction: A microfocus CT system was used, with scanning parameters set to 140kV voltage, 200μA current, and 5μm resolution. The sample was rotated 360°, and 2000 projection images were acquired. 3D reconstruction was performed using the Feldkamp algorithm to obtain 3D grayscale images. The following processing and analysis were performed using Avizo software:

[0071] Image segmentation: Global thresholding and watershed algorithm are used to distinguish rock matrix, pores and cracks.

[0072] Parameter calculation: Three-dimensional porosity (Φ 30 ), crack density (D) fracture (mm² / mm³), average crack aperture (μm), coordination number, and pore throat radius distribution.

[0073] 2.3: Data integration and quality control, including:

[0074] Data fusion and cross-validation: Establish a unified data form to integrate all characterization data; compare the porosity of thin sections with the three-dimensional porosity of CT to verify the consistency of characterization results; check the correlation between NMR mobile fluid saturation and CT connected pore volume; analyze the quantitative relationship between permeability and pore throat parameters extracted by CT.

[0075] 2.3.2 Sample homogeneity assessment: The porosity and permeability variation coefficients of 5 parallel samples were statistically analyzed to ensure that they were less than 15%; the CT three-dimensional structural parameters of each sample were compared to confirm that their structural characteristics were basically consistent.

[0076] S3: High-temperature and high-pressure triaxial hole-slot evolution simulation experiment, the specific details of which are:

[0077] 3.1 Experimental system preparation and sample installation, including:

[0078] Experimental system verification: A high-temperature, high-pressure three-axis servo experimental system with four independent control functions was adopted. Before use, the following subsystems need to be calibrated and verified:

[0079] Temperature control system: Confirm that its operating range (room temperature to 300℃) and control accuracy (±1℃) meet the design requirements.

[0080] Pressure control system: Confirm the range (0-200MPa) and accuracy (±0.1MPa) of the confining pressure and axial pressure servo mechanisms.

[0081] Pore ​​pressure system: It has been confirmed that it can provide a stable pore pressure of 0-100MPa with fluctuations of less than ±0.5MPa.

[0082] Data acquisition system: Check the zero point and signal stability of axial and radial strain sensors (LVDT or strain gauges), temperature sensors, and pressure sensors.

[0083] Sample mounting and sealing: The pre-prepared and characterized dried standard cylindrical sample (Φ25mm×H50mm) is tightly wrapped with polytetrafluoroethylene (PTFE) heat shrink tubing. This step is crucial to prevent the confining pressure medium (hydraulic oil) from entering the sample pores during the experiment, thereby ensuring the independence and accuracy of pore pressure control.

[0084] Porous gaskets are placed at both ends of the sample to ensure that the pore pressure medium (usually deionized water or inert gas) can act uniformly on the sample end face.

[0085] Carefully place the assembled sample into the triaxial pressure chamber, ensuring that the sample is aligned with the upper and lower pressure heads;

[0086] Connect the pore pressure line and the axial / radial strain sensor lines in sequence. After confirming that all connections are correct, seal the pressure chamber.

[0087] 3.2 Experimental parameter initialization and loading path setting, including:

[0088] Initial stress and temperature field establishment: Inject confining pressure medium into the pressure chamber and slowly apply initial confining pressure to 5 MPa to fix the sample and provide an initial stress environment.

[0089] The pore pressure system is activated, applying initial pore pressure to the sample according to preset operating conditions. For example, for P... p Under operating conditions of 50MPa, the pore pressure is slowly increased to 50MPa.

[0090] Start the axial compression system and apply axial stress slightly higher than the confining pressure (e.g., σ1 = σ3 + 2 MPa) to eliminate system clearance.

[0091] Turn on the temperature control system and set the initial temperature to room temperature (e.g., 25°C).

[0092] Loading Path Programming and Input: Accurately input the experimental loading scheme table output from step S1 into the experimental machine control system. This scheme table defines the complete loading path, typically containing multiple stages, for example:

[0093] Phase 1 (Simulated Deep Burial Compaction): The command system synchronously increases the temperature and target confining pressure at a constant rate (e.g., heating rate 2℃ / min, confining pressure loading rate 0.8MPa / min). The axial pressure adopts a follow-up control mode, always maintaining σ1=σ3+10MPa.

[0094] Phase Two (Simulated Geological Steady State): When the temperature reaches the target paleogeothermal temperature (e.g., 220℃) and the confining pressure reaches the target value (e.g., 120MPa), the constant temperature and pressure maintenance phase begins, and the duration (e.g., 60 minutes) is calculated from the geological time equivalent.

[0095] Phase 3 (Simulated structural fracture): Keep the temperature and confining pressure constant, switch the control system to differential stress control mode, and gradually increase the axial pressure at a quasi-static rate (e.g., 0.4 MPa / min) so that the differential stress (Δσ=σ1-σ3) increases from the initial value (e.g., 10 MPa) to the preset maximum value (e.g., 70 MPa).

[0096] 3.3 Execution and process monitoring of parallel tests under multiple operating conditions, including:

[0097] Parallel experimental execution: The above experiments were performed sequentially on 5 parallel samples. Each sample was subjected to a constant pore pressure condition (0 MPa, 20 MPa, 50 MPa, 60 MPa, 70 MPa), but all samples were subjected to the exact same temperature-confining pressure-axial pressure co-loading path. This design is key to quantifying the independent effect of pore pressure.

[0098] Real-time process monitoring and data acquisition: During the experiment, the control system automatically adjusts each physical field according to the preset path.

[0099] The data acquisition system records time-series data of the following key parameters at high frequency: control parameters: temperature (T), confining pressure (σ3), axial pressure (σ1), pore pressure (P). p Closely monitor changes in effective stress to ensure that, under high pore pressure conditions, the confining pressure always satisfies σ3≥P. p A safety condition of +20MPa is provided to prevent hydraulic fracturing of the sample.

[0100] 3.4 Experiment termination and sample recovery, including:

[0101] Termination Criteria: The experiment terminates when any of the following conditions are met:

[0102] Successfully completing all preset loading stages and monitoring a sudden drop in axial pressure (such as a drop exceeding 10% of the peak intensity) indicates that the sample has undergone macroscopic damage and the relevant experimental conditions have reached the preset safety limit.

[0103] Orderly unloading and recovery: After the experiment is terminated, unload strictly in the following order to avoid secondary damage to the sample: First, unload the axial pressure at a controlled rate to slightly higher than the confining pressure; second, simultaneously and slowly reduce the confining pressure and pore pressure; third, start the system cooling program to reduce the temperature to a safe range (such as below 60°C); finally, remove all pressure, open the pressure chamber, and carefully remove the sample after the experiment.

[0104] The extracted samples should be immediately labeled and photographed, and subsequent characterization in step S4, especially CT scans, should be performed as soon as possible to capture the most accurate crack condition.

[0105] S4: Detailed comparative analysis of samples after the experiment;

[0106] 4.1 Standardized recovery and macroscopic morphology recording of samples after the experiment, including:

[0107] High-definition image acquisition: Immediately after sample removal, use a high-resolution digital SLR camera equipped with a macro lens to take multi-angle (at least frontal, top, side, and two end-face) photos of the sample under fixed lighting conditions. Ensure a scale is placed next to the sample as a reference.

[0108] Macroscopic crack logging: On the photographs or in the corresponding logbook, mark and describe in detail the following characteristics of all newly formed macroscopic cracks:

[0109] Spatial distribution: Record the location and shape of the cracks on the sample surface.

[0110] Geometric parameters: Measure and record the length (mm) and visible aperture a (μm) of the crack, and describe its morphology (e.g., straight, tortuous, branched, etc.).

[0111] Relationship with the original structure: Preliminary observation of whether the crack extends along the original bedding, mineral bands, or pre-existing microcracks.

[0112] 4.2 Post-experiment sample processing and multi-scale characterization retesting, including:

[0113] Sample stabilization treatment: To prevent loose particles from falling off or cracks from changing shape, the sample is wrapped and fixed using flexible heat shrink tubing or a special sheath. At this stage, vacuum pressure saturation treatment is performed in preparation for subsequent fluid saturation NMR testing.

[0114] Repeat the initial characterization process: For post-experimental samples, completely repeat all characterization tests in step S2 to obtain comparable end-point data.

[0115] (1) Preparation and observation of rock thin sections: The sample was re-cut along the axial direction or a specific direction (such as parallel to the main fracture) and cast thin sections were prepared. The sections were compared with the thin sections before the experiment under a microscope, and the following key features were identified:

[0116] Newly formed microcracks: their density, length, and aperture are statistically analyzed, and their propagation path (transgranular or intergranular) is observed.

[0117] Changes in pore structure: Compare the compression, deformation, or enlargement of intergranular pores and intercrystalline pores;

[0118] Diagenetic phenomena: Observe whether there is evidence of new pressure-dissolution sutures, microfractures, or mineral phase transformations.

[0119] (2) Gas method porosity-permeability retest: Under the same test standards and conditions as before the experiment, measure the porosity (Φ1) and permeability (K1) after the experiment. Calculate their absolute changes (ΔΦ=Φ1-Φ0, ΔK=K1-K0) and relative change rates.

[0120] (3) Re-measurement of NMR T2 spectrum: Under the same saturation and instrument parameters, the NMR T2 spectrum of the sample was collected after the experiment. The T2 spectra before and after the experiment were superimposed and compared for analysis:

[0121] Changes in T2 spectrum morphology: Is the overall T2 spectrum "shifted to the left" (increased number of small pores) or "shifted to the right" (increased number of large pores or cracks)?

[0122] Pore ​​size distribution evolution: Through inversion calculation, the pore volume change in different pore size ranges is quantitatively analyzed to identify the pore size range with dominant changes;

[0123] Changes in movable fluid saturation: Calculate the movable fluid saturation (Smf1) after the experiment and compare it with the initial value (Smf0) to assess the improvement or deterioration of fluid mobility.

[0124] (4) High-resolution CT scan retest: Under the same scanning parameters (voltage, current, resolution, rotation angle, etc.) as before the experiment, a second CT scan was performed on the sample after the experiment.

[0125] 4.3 Precise registration and digitization difference analysis of 3D data, including:

[0126] 3D image registration: The two sets of CT 3D data obtained before and after the experiment were imported into professional image processing software (such as Avizo or Dragonfly). A rigid registration algorithm based on grayscale information was used to accurately align the post-experiment data with the pre-experiment data in 3D space, eliminating errors caused by differences in sample placement.

[0127] Voxel identification and quantitative calculation: Image subtraction is performed on the two registered sets of 3D image data to generate a single "voxel" dataset. Within this dataset:

[0128] Voxel regions with significantly increased gray values ​​represent areas of newly formed cracks or enlarged pores;

[0129] Voxel regions with significantly reduced gray values ​​represent areas of pore compression, collapse, or mineral infilling.

[0130] By setting a reasonable grayscale threshold, the "differential bodies" are segmented, thereby quantitatively calculating the following key parameters:

[0131] Changes in the fracture system: Total volume of newly formed fractures (ΔV) fracture ), crack density increment (ΔD) fracture ).

[0132] Changes in porous systems: Volume of closed pores (ΔV) pore close ), newly formed pore volume (ΔV) pore open ).

[0133] Changes in pore network connectivity: Extract the pore network model before and after the experiment, calculate the change in the average coordination number (ΔCN), and directly quantify the change in pore connectivity.

[0134] S5: Analysis and Quantitative Evaluation of the Hole-Slit Co-evolution Mechanism (This step is crucial for extracting knowledge and forming evaluation indicators);

[0135] Multi-source data fusion and originality evaluation parameter calculation: Constructing a comprehensive database that integrates all experimental conditions (temperature T, confining pressure σ3, axial pressure σ1, pore pressure P) p ) and response data (φ, K, CT parameters, NMR parameters).

[0136] Calculate the core quantitative evaluation index proposed in this invention:

[0137] (1) Pore pressure retention coefficient (K) c ):

[0138] Kc = |(φ1-φ0) / [(σ3,1-Pp,1)-(σ3,0-Pp,0)]|, unit: % / MPa

[0139] Wherein, φ0,φ1: rock porosity (%) measured before and after the experiment, respectively; σ3,0,σ3,1: initial and final confining pressure values ​​(MPa) of the experiment, respectively; Pp,0,Pp,1: initial and final pore pressure values ​​(MPa) of the experiment, respectively. This coefficient quantifies the rock's ability to resist stress compression due to pore pressure: a low Kc value (e.g., <0.175% / MPa): indicates that the pore pressure leads to strong resistance to compaction of the rock. Under deep high-pressure conditions, due to the presence of pore pressure, the porosity loss is very small, which is more conducive to the long-term preservation of pores; a high Kc value (e.g., >0.175% / MPa): indicates that the rock has weak resistance to compaction and is very sensitive to effective stress. Such rocks are difficult to preserve pores in deep burial environments.

[0140] (2) Pore-Fraction Synergistic Enhancement Index (SEI): SEI=|((K1 / K0)-1) / ((φ1 / φ0)-1)|, dimensionless index; where K0,K1: rock permeability (mD) measured before and after the experiment, respectively; φ0,φ1: rock porosity (%) measured before and after the experiment, respectively; SEI represents the ratio of the relative improvement of permeability to the relative change of porosity, which reveals the synergistic or decoupling relationship between porosity change and permeability change.

[0141] SEI>>50 (significantly greater than 50): This is a clear indication of “crack-dominated synergistic effect”, meaning that the increase in permeability (or the slowing of its decline) is much greater than the change in porosity. This indicates that new seepage channels (cracks) are formed or existing cracks are widened during the experiment, which greatly improves the fluid transport capacity, while its contribution to the total volume of pore space is not significant.

[0142] SEI≈50: This indicates that the changes in porosity and permeability are homogeneous and synchronous. The changes in permeability are mainly caused by the uniform compression or dissolution of pores, and the contribution of cracks is not significant.

[0143] SEI<50: This indicates a deterioration in reservoir quality, which may mean that pore throats are blocked or newly added pores / fractures are isolated and ineffective, causing permeability improvement to lag behind changes in porosity, or even permeability to decline faster.

[0144] (3) Fracturing efficiency (FCE): FCE = ΔV pore open / ΔV fracture， Dimensionless ratio, where ΔV fracture： The total volume (mm³) of the newly formed fracture system was identified and calculated from 3D CT scan images before and after the experiment, ΔV. pore effective：The change (mm³) in effective pore volume (connected or created) by the newly formed fracture system, identified by CT scans and / or NMR T2 spectral analysis before and after the experiment. This includes isolated pores connected by fractures, pores generated by the dissolution and expansion of fracture walls, etc. FCE stands for "effective pore volume that a unit volume of fracture can connect or create";

[0145] It quantifies the "efficiency" of fractures in improving reservoir space and connectivity. A high FCE value indicates that the fractures have high connectivity efficiency, not only forming seepage channels but also greatly activating the surrounding matrix pore system, forming an efficient "fracture-cavity" network. A low FCE value indicates that the fractures have low connectivity efficiency, with the fractures mainly relying on their own reservoir space and having limited ability to communicate with and modify the surrounding pore system, resulting in limited reservoir space.

[0146] 5.2 Multi-source data fusion and pattern curve plotting, including:

[0147] Construct a comprehensive data matrix: integrate all experimental data (including initial lithology, experimental temperature and pressure paths, post-experiment Kc, SEI, FCE values, CT fracture parameters, NMR pore size distribution changes, etc.) under different pore pressure (Pp) conditions into a unified data table.

[0148] Plot the pore pressure effect curve:

[0149] (1) Plot the “Kc-Pp” relationship curve: with pore pressure (Pp) as the horizontal axis and pore pressure retention coefficient (Kc) as the vertical axis; this curve will intuitively show how the rock’s resistance to compaction changes as pore pressure increases.

[0150] (2) Plot the “SEI-Pp” relationship curve: with pore pressure (Pp) as the horizontal axis and pore-crack synergy index (SEI) as the vertical axis; this curve is used to analyze the effect of pore pressure on crack synergy. The curve shape can reveal whether there is an optimal pore pressure window that allows the crack communication efficiency (SEI) to reach its peak.

[0151] (3) Plot the “FCE-Pp” relationship curve: with pore pressure (Pp) as the horizontal axis and fracture connectivity efficiency (FCE) as the vertical axis; this curve is used to evaluate the difference in the efficiency of fracture connectivity under different pore pressure environments.

[0152] 5.3 Diagnosis of causal mechanisms combining micro and macroscopic perspectives, including:

[0153] CT-Thin Section Linkage Analysis: The spatial distribution of newly formed fractures extracted from the post-experiment CT 3D model is superimposed and compared with the post-experiment thin sections in digital images; the focus is on analyzing the relationship between the fracture initiation point and propagation path and the rock texture.

[0154] If the crack extends strictly along the boundary between different minerals, it is diagnosed as a diagenetic interface fracture, indicating that the heterogeneity of the rock controls the fracture process.

[0155] If the crack cuts through quartz grains or coarse dolomite crystals, it is diagnosed as a crack controlled by mineral mechanical properties, reflecting the brittle strength of the rock.

[0156] If the fracture network preferentially connects isolated dissolution pores, these "key connection points" are marked on the CT model to confirm the effective activation of isolated reservoir spaces by fractures.

[0157] NMR and porosity data corroborate this: the evolution of NMR T2 spectra (such as the T2 peak shifting to the right, indicating an increase in macropore components) is correlated with changes in the SEI index, explaining the microscopic reasons for permeability changes from the perspective of pore structure changes: combined with porosity data, it is confirmed that for samples with SEI>50, the increase in permeability is due to qualitative changes (cracks) in the fluid channels, rather than quantitative changes (porosity).

[0158] 5.4 Establishment and application of quantitative evaluation charts for reservoir quality, including:

[0159] A "Kc-SEI" reservoir quality evaluation chart was constructed: the pore pressure retention coefficient (Kc) was used as the abscissa (representing the rock's resistance to damage), and the pore-fracture synergy index (SEI) was used as the ordinate (representing the fracture's ability to improve seepage). Based on the distribution of (Kc, SEI) data points of all parallel samples on the chart, four quadrants with clear geological significance were divided:

[0160] Quadrant I (low Kc, high SEI): Fracture-enhanced type, which is the optimal reservoir type. It has both strong resistance to compaction to preserve porosity and a highly efficient fracture network to improve seepage. It is the preferred "sweet spot" for exploration and development.

[0161] Quadrant II (high Kc, high SEI): Potential risk type. Although cracks are developed (high SEI), the rock itself has weak compaction resistance (high Kc). The pore space may be unstable during burial or production, and there is a risk of crack closure due to later compaction.

[0162] Quadrant III (high Kc, low SEI): Compaction failure type, worst reservoir type, easily compacted and with undeveloped or ineffective fractures;

[0163] Quadrant IV (low Kc, low SEI): Pore-stable type, the rock is dense and hard, pores and fractures are not well developed, but existing pores are well preserved, and it can be used as a secondary exploration target.

[0164] Engineering calibration and prediction of the chart: The chart further uses different colors or symbols to mark the pore pressure (Pp) and lithology corresponding to each data point. This chart can be directly extrapolated for drilling and field prediction: By conducting basic tests on core samples from unknown areas to obtain their Kc and SEI values, these values ​​can be plotted on the chart to quickly determine the reservoir quality type and main controlling factors, thereby guiding the selection of exploration target areas and well location deployment.

[0165] Example 2: The present invention will be further described in detail below with reference to specific embodiments; the analytical and testing methods appearing in the following embodiments are conventional methods in the art unless otherwise specified; the professional names appearing in the following embodiments, such as "microbial dolomite", "crystalline dolomite", "granular dolomite", "mixed sedimentary rock", etc., are all professional terms in the art and will not be described one by one.

[0166] The sample in this embodiment is a Cambrian Xiaoerbulak Formation dolomite sample from a well in the Tazhong area of ​​the Tarim Basin, buried at a depth of 6000–8000 meters. Due to its old age and deep burial, influenced by the original sedimentary environment and multiple phases of diagenetic fluid alteration, its pore structure is complex, making it a typical representative for studying the synergistic evolution of pores and fractures in carbonate rocks under ultra-deep, high-temperature, and high-pressure conditions.

[0167] According to step S1.1, collect drilling cores, logging data (including sonic, density, neutron, resistivity and imaging logging, etc.), seismic profiles, regional geological maps and tectonic evolution research reports of the Cambrian Xiaoerbulake Formation in the central Tarim Basin to model the stratigraphic and structural framework.

[0168] According to step S1.2, basin simulation software is used to reconstruct the burial history of the Cambrian Xiaoerbulak Formation dolomite reservoir in the Tazhong area of ​​the Tarim Basin by restoring the burial depth-time curve from deposition to the present. Temperature-sensitive paleotherm data is used to calibrate and constrain the basin model to reconstruct the thermal history. The evolution history of paleopore pressure (Pp) is restored by using the sonic-density logging cross-plot method (applying Bowers' formula) or the equivalent depth method, combined with stratigraphic testing data. Figure 3 , Figure 4 .

[0169] Based on core, thin section, scanning electron microscopy, cathodoluminescence, and geochemical data from step S1.3, a detailed diagenetic sequence of the Cambrian Xiaoerbulak Formation dolomite reservoir in the Tazhong area of ​​the Tarim Basin was established. The type, stage, intensity, and formation sequence of each major diagenetic process (such as compaction, cementation, dissolution, fracturing, and pressure solution) were determined. By integrating burial history, thermal history, pressure history, stress history, and diagenetic-pore evolution history, the key alteration stage that decisively influences the current reservoir quality was identified, and the initial temperature (T) was obtained. i ) and endpoint temperature (T) f ), initial confining pressure (σ3,i ) and the confining pressure at the endpoint (σ3, f ), initial axial compression (σ1, i ) and the final axial pressure (σ1, f ), and the geological duration of this stage (t geo ),like Figure 3 .

[0170] Based on the experimental scheme for the Cambrian Xiaoerbulak Formation dolomite reservoir in the Tazhong area of ​​the Tarim Basin, as output in step S1.4, the required constant pore pressure sequence (0, 20, 50, 60, 70 MPa) for parallel experiments is specified. The loading time is 16 h, and the loading rate is R... T =25℃ / h, R σ3 =25MPa / h, as shown in Table 1.

[0171] Table 1. Experimental scheme for the co-evolution of pores and fractures in the Cambrian Xiaoerbulake Formation dolomite reservoir under high temperature and high pressure in the central Tarim Basin.

[0172] ① Rapid burial during the quasi-syngenetic to early diagenetic period 20 120 0 80 0 100 70 ② Brief uplift during the middle diagenetic period 120 95 80 60 100 75 20 ③ Stable burial during the intermediate diagenetic period 95 120 60 80 75 100 60 ④ Uplift during the middle and late diagenetic stages 120 95 80 60 100 75 20 ⑤ Continuous burial during the late diagenetic period 95 175 60 120 75 155 340

[0173] Core samples were selected from the Cambrian Xiaoerbulak Formation in the Tazhong area of ​​the Tarim Basin according to step S2.1. Based on petrographic analysis, high-porosity carbonate rock samples representing the main reservoir space in this area were selected. Initial screening was performed using industrial CT scanning to ensure that the lithology, physical properties, and structural characteristics of the five parallel samples were basically consistent and that there were no obvious macroscopic defects. These samples were then processed into standard cylindrical specimens with a diameter of 25 mm ± 0.1 mm and a height of 50 mm ± 0.2 mm.

[0174] The initial porosity (Φ0) and initial permeability (K0) of Cambrian Xiaoerbulak Formation dolomite samples from the Tazhong area of ​​the Tarim Basin were obtained according to step S2.2. The T2 relaxation time spectrum was obtained through nuclear magnetic resonance inversion fitting, and the pore size distribution curve was calculated. The projection images obtained from CT scans were imported into Avizo software to obtain the rock matrix, pores, and fractures, and the three-dimensional porosity (Φ0) was calculated. 30 ), average crack aperture, coordination number, pore throat radius distribution; integrated data images such as Figure 6 .

[0175] Integrate all characterization data according to step S2.3; calculate the coefficient of variation of porosity and permeability of 5 parallel samples to ensure that it is less than 15%; compare the CT three-dimensional structural parameters of each sample to confirm that their structural characteristics are basically consistent.

[0176] According to step S3.1, the temperature control system, pressure control system, pore pressure system and data acquisition system of the high temperature and high pressure triaxial experimental system are calibrated and confirmed. The pre-prepared and characterized dried standard cylindrical sample (Φ25mm×H50mm) is tightly wrapped with polytetrafluoroethylene (PTFE) heat shrink tubing and carefully placed into the triaxial pressure chamber. Ensure that the sample is aligned with the upper and lower pressure heads. Connect the pore pressure pipeline and the axial / radial strain sensor line in sequence. After confirming that all connections are correct, the pressure chamber is sealed.

[0177] According to step S3.2, inject the confining pressure medium into the pressure chamber and slowly apply the initial confining pressure to 5 MPa to fix the sample and provide the initial stress environment. Start the pore pressure system and apply the initial pore pressure to the sample according to the preset working conditions. Start the axial pressure system and apply axial stress to make it slightly higher than the confining pressure by 2 MPa to eliminate system gaps. Start the temperature control system and set the initial temperature to 20°C.

[0178] The experimental loading scheme table output from step S1.4 should be accurately input into the experimental machine control system.

[0179] The above experiments were performed sequentially on five parallel samples according to step S3.3. Each sample corresponded to a constant pore pressure condition (0MPa, 20MPa, 50MPa, 60MPa, 70MPa), and the time-series data of the following key parameters were recorded at high frequency: control parameters: temperature (T), confining pressure (σ3), axial pressure (σ1), pore pressure (P). p Closely monitor changes in effective stress to ensure that, under high pore pressure conditions, the confining pressure always satisfies σ3≥P. p A safety condition of +20MPa is provided to prevent hydraulic fracturing of the sample.

[0180] Terminate the experiment according to step S3.4 and unload and recover the samples in an orderly manner.

[0181] Following step S4.1, immediately after sample removal, a high-resolution digital SLR camera is used to photograph the sample from multiple angles under fixed light source conditions. The rock exhibits one or more high-angle cracks under low pore pressure, resulting in an overall reduction in porosity after the experiment.

[0182] Following step S4.2, the sample is wrapped and secured using flexible heat-shrink tubing or a special sheath, and vacuum pressure saturation is performed at this stage in preparation for subsequent fluid-saturated NMR testing. The initial characterization procedure is repeated, and the data are integrated as follows: Figure 8 .

[0183] According to step S4.3, the two sets of CT 3D data obtained before and after the experiment are imported into professional image processing software. A rigid registration algorithm based on grayscale information is used to precisely align the post-experiment data with the pre-experiment data in 3D space, eliminating errors caused by differences in sample placement. Figure 9 Image subtraction was performed on the two registered sets of 3D image data to generate a "difference volume" data, as shown in Tables 2, 3, 4, and 5.

[0184] Table 2. Data on "differences" in Sample A (microbial dolomite) before and after the experiment.

[0185] <![CDATA[φ0(%)]]> 5.034 5.086 5.112 5.079 4.984 <![CDATA[φ1(%)]]> 2.06394 3.20418 3.62952 3.65688 3.63832 Δφ(%) -2.9701 -1.8818 -1.4825 -1.4221 -1.3457 a(mm) 0.01 0.012 0.009 0.01 0.011 <![CDATA[ΔV pore close (mm³)]]> 970.6517 718.0242 577.2193 476.2550 392.6240 <![CDATA[ΔV pore open (mm³)]]> 241.6894 256.1580 213.3655 127.2157 62.3458 <![CDATA[ΔD fracture (mm² / mm³)]]> 321.4693 339.3654 315.2548 215.3651 156.3215 <![CDATA[ΔV fracture (mm³)]]> 321.4693 339.3654 315.2548 215.3651 156.3215 CN 3.5 3.6 3.1 3.3 3.4 ΔCN 16.8951 17.8548 16.3954 11.1724 8.0240

[0186] Table 3. Data on "differences" in Sample B (crystalline dolomite) before and after the experiment.

[0187] <![CDATA[φ0(%)]]> 5.316 4.994 5.275 4.863 4.946 <![CDATA[φ1(%)]]> 3.1896 3.84538 4.27275 3.866085 3.9568 Δφ(%) -2.1264 -1.1486 -1.0023 -0.9969 -0.9892 a(mm) 0.018 0.018 0.019 0.011 0.019 <![CDATA[ΔV pore close (mm³)]]> 676.2260 405.5711 328.1369 300.0491 265.9407 <![CDATA[ΔV pore open (mm³)]]> 154.3290 123.6574 82.1478 55.3694 23.1545 <![CDATA[ΔD fracture (mm² / mm³)]]> 0.3969 0.3517 0.2259 0.3197 0.0865 <![CDATA[ΔV fracture (mm³)]]> 175.3548 155.3652 105.3215 86.3215 40.3251 CN 2.5 2.8 2.9 2.4 2.6 ΔCN 0.3747 0.3566 0.2321 0.1496 0.0638

[0188] Table 4. Data on "differences" in Sample C (granular dolomite) before and after the experiment.

[0189] <![CDATA[φ0(%)]]> 4.289 4.356 4.489 4.125 4.165 <![CDATA[φ1(%)]]> 1.07225 2.26512 2.78318 2.51625 2.62395 Δφ(%) -3.2168 -2.0909 -1.7058 -1.6088 -1.5411 a(mm) 0.008 0.007 0.006 0.009 0.01 <![CDATA[ΔV pore close (mm³)]]> 1113.7576 789.3945 636.0244 535.1717 458.6301 <![CDATA[ΔV pore open (mm³)]]> 324.2485 276.2154 217.3532 140.3251 80.3996 <![CDATA[ΔD fracture (mm² / mm³)]]> 2.3395 2.3879 2.5727 1.3763 0.9822 <![CDATA[ΔV fracture (mm³)]]> 459.3687 410.2565 378.8621 304.0251 241.0579 CN 4.2 3.8 3.9 4.1 4.3 ΔCN 1.6694 1.2975 1.0519 0.7507 0.4549

[0190] Table 5. Data on "differences" in Sample D (mixed sedimentary rock) before and after the experiment.

[0191] <![CDATA[φ0(%)]]> 4.316 3.994 4.275 3.963 3.946 <![CDATA[φ1(%)]]> 0.90636 0.9985 1.41075 1.129455 1.1838 Δφ(%) -3.4096 -2.9955 -2.8643 -2.8335 -2.7622 a(mm) 0.007 0.008 0.009 0.011 0.012 <![CDATA[ΔV pore close (mm³)]]> 1017.0035 886.2302 810.3536 771.7765 713.2468 <![CDATA[ΔV pore open (mm³)]]> 180.152 151.024 107.361 76.32 35.301 <![CDATA[ΔD fracture (mm² / mm³)]]> 2.1376 1.6053 1.0925 0.7680 0.4822 <![CDATA[ΔV fracture (mm³)]]> 367.246 315.204 241.331 207.354 142.005 CN 3.1 3.3 3.2 3.1 2.8 ΔCN 0.7395 0.6904 0.4475 0.3303 0.1466

[0192] Calculate the core quantitative evaluation indicators according to step S5.1:

[0193] Substituting the parameters measured before and after the experiment of five parallel samples of three different lithologies into the above formula, we obtain Kc, SEI and FCE, which are shown in Tables 6, 7, 8 and 9.

[0194] Table 6 Core quantitative evaluation indicators for Sample A (microbial dolomite) before and after the experiment

[0195] <![CDATA[φ0(%)]]> 5.034 5.086 5.112 5.079 4.984 <![CDATA[φ1(%)]]> 2.06394 3.20418 3.62952 3.65688 3.63832 <![CDATA[σ3,0(MPa)]]> 0 0 0 0 0 <![CDATA[σ3,1(MPa)]]> 120 120 120 120 120 <![CDATA[Pp,0(MPa)]]> 0 20 50 60 70 <![CDATA[Pp,1(MPa)]]> 0 20 50 60 70 <![CDATA[K0(mD)]]> 2.759 2.764 2.812 2.715 2.687 <![CDATA[K1(mD)]]> 4.9662 5.2516 7.8736 40.725 2325.11484 <![CDATA[ΔV pore open (mm³)]]> 241.6894 256.1580 213.3655 127.2157 62.3458 <![CDATA[ΔV fracture (mm³)]]> 321.4693 339.3654 315.2548 215.3651 156.3215 Kc(% / MPa) 0.0247505 0.015681833 0.012354 0.011851 0.011214 SEI 1.355932203 2.432432432 6.206896552 50 3201.185185 FCE 0.782934482 0.754814722 0.676803335 0.590697843 0.398830615

[0196] Table 7 Core quantitative evaluation indicators for sample B (crystalline dolomite) before and after the experiment.

[0197] <![CDATA[φ0(%)]]> 5.316 4.994 5.275 4.863 4.946 <![CDATA[φ1(%)]]> 3.1896 3.84538 4.27275 3.866085 3.9568 <![CDATA[σ3,0(MPa)]]> 0 0 0 0 0 <![CDATA[σ3,1(MPa)]]> 120 120 120 120 120 <![CDATA[Pp,0(MPa)]]> 0 20 50 60 70 <![CDATA[Pp,1(MPa)]]> 0 20 50 60 70 <![CDATA[K0(mD)]]> 2.134 2.158 2.113 2.162 2.187 <![CDATA[K1(mD)]]> 3.8412 4.316 6.339 64.86 2089.0224 <![CDATA[ΔV pore open (mm³)]]> 154.3290 123.6574 82.1478 55.3694 23.1545 <![CDATA[ΔV fracture (mm³)]]> 175.3548 155.3652 105.3215 86.3215 40.3251 Kc(% / MPa) 0.01772 0.009571833 0.008352083 0.008307625 0.008243333 SEI 2 4.347826087 10.52631579 141.4634146 4771 FCE 0.908144812 0.860278878 0.808456013 0.757278314 0.574195724

[0198] Table 8 Core quantitative evaluation indicators for sample C (granular dolomite) before and after the experiment

[0199] <![CDATA[φ0(%)]]> 4.289 4.356 4.489 4.125 4.165 <![CDATA[φ1(%)]]> 1.07225 2.26512 2.78318 2.51625 2.62395 <![CDATA[σ3,0(MPa)]]> 0 0 0 0 0 <![CDATA[σ3,1(MPa)]]> 120 120 120 120 120 <![CDATA[Pp,0(MPa)]]> 0 20 50 60 70 <![CDATA[Pp,1(MPa)]]> 0 20 50 60 70 <![CDATA[K0(mD)]]> 1.579 1.482 1.624 1.517 1.611 <![CDATA[K1(mD)]]> 0.9474 0.5928 3.7352 21.238 676.62 <![CDATA[ΔV pore open (mm³)]]> 324.2485 276.2154 217.3532 140.3251 80.3996 <![CDATA[ΔV fracture (mm³)]]> 459.3687 410.2565 378.8621 304.0251 241.0579 Kc(% / MPa) 0.02680625 0.017424 0.014215167 0.01340625 0.012842083 SEI 0.533333333 1.25 3.421052632 33.33333333 1132.432432 FCE 0.705856755 0.67327489 0.573700035 0.461557615 0.292044359

[0200] Table 9 Core quantitative evaluation indicators for sample D (mixed sedimentary rock) before and after the experiment.

[0201] <![CDATA[φ0(%)]]> 4.316 3.994 4.275 3.963 3.946 <![CDATA[φ1(%)]]> 0.90636 0.9985 1.41075 1.129455 1.1838 <![CDATA[σ3,0(MPa)]]> 0 0 0 0 0 <![CDATA[σ3,1(MPa)]]> 120 120 120 120 120 <![CDATA[Pp,0(MPa)]]> 0 20 50 60 70 <![CDATA[Pp,1(MPa)]]> 0 20 50 60 70 <![CDATA[K0(mD)]]> 1.254 1.209 1.334 1.13 1.211 <![CDATA[K1(mD)]]> 1.0032 0.8463 2.2678 3.39 12.11 <![CDATA[ΔV pore open (mm³)]]> 180.152 151.024 107.361 76.32 35.301 <![CDATA[ΔV fracture (mm³)]]> 367.246 315.204 241.331 207.354 142.005 Kc(% / MPa) 0.028413667 0.0249625 0.02386875 0.023612875 0.023018333 SEI 0.253164557 0.4 1.044776119 2.797202797 12.85714286 FCE 0.49054857 0.479130975 0.444870323 0.368066206 0.248589838

[0202] Plot the "Kc-Pp" relationship curve according to step S5.2 as follows: Figure 12The higher the Pp, the lower the Kc value, demonstrating the supporting effect of pore pressure on the rock skeleton and the pore protection effect. At the same Pp, the Kc values ​​of the four lithologies, from smallest to largest, are: crystalline dolomite, microbial dolomite, grain-like dolomite, and mixed sedimentary rock. This indicates that the sensitivity of the four rocks to effective stress increases sequentially, while their compaction resistance decreases sequentially. Crystalline dolomite has stronger compaction resistance; pore pressure effectively supports the skeleton, which is beneficial for the long-term preservation of pores. Microbial dolomite is next, followed by grain-like dolomite, and mixed sedimentary rock has the weakest compaction resistance. A "SEI-Pp" relationship curve is plotted, as shown below. Figure 13 The SEI values ​​of the four rock types were far less than 50 under low pore pressure. This indicates that newly formed fractures significantly increased permeability, but simultaneously, the rock matrix was compressed, leading to a decrease in total porosity. The flow effect of fractures far outweighed their contribution to pore space. The sharp rebound in SEI values ​​during the medium-to-high pressure stage (>60 MPa) indicates that as pore pressure increased, fractures not only began to provide channels but also effectively communicated and expanded the surrounding matrix porosity, thus curbing and reversing the decreasing porosity trend, ultimately achieving a balanced state of simultaneous improvement in porosity and permeability. The "FCE-Pp" relationship curve was plotted, as shown below. Figure 14 The highest FCE value was found in crystalline dolomite, indicating that its fractures, compared to the other three lithologies, more effectively connected isolated pores, induced dissolution and expansion, and activated the surrounding matrix pore system, forming a highly efficient "fracture-cavity" network. Microbial dolomite consistently had a lower FCE value than crystalline dolomite, suggesting a relatively weaker ability of its fractures to connect isolated pores and create new pores. Granular dolomite and mixed sedimentary rocks had the lowest and most similar FCE values. This indicates that even when fractures formed, their ability to connect with and modify the surrounding dense matrix was very limited, resulting in a limited total reservoir space.

[0203] According to step S5.3, the spatial distribution of newly formed fractures extracted from the post-experiment CT 3D model was overlaid and compared with the post-experiment thin sections under digital images. Analysis of the relationship between the fracture initiation point and propagation path and the rock fabric revealed that the fractures generated in parallel samples under different pore pressures of the three lithologies strictly extended along the boundaries between different minerals, thus diagenetic interface-controlled fracture, indicating that the heterogeneity of the rock controlled the fracture process. Combining porosity and permeability data with the evolution of NMR T2 spectra, it was confirmed that the increased permeability of samples with SEI>50 was due to qualitative changes in the fluid channels (fractures), rather than quantitative changes (porosity).

[0204] According to step S5.4, a "Kc-SEI" reservoir quality evaluation chart is constructed: the pore pressure retention coefficient (Kc) is used as the abscissa (representing the rock's resistance to damage), and the pore-fracture synergy index (SEI) is used as the ordinate (representing the fracture's ability to improve seepage). Based on the distribution of (Kc, SEI) data points of all parallel samples on the chart, four quadrants with clear geological significance are divided, such as... Figure 15By constructing a reservoir quality evaluation chart based on the "pore pressure retention coefficient (Kc) - pore-fracture synergistic enhancement index (SEI)," the reservoir performance of four lithologies—microbial dolomite, crystalline dolomite, grainy dolomite, and mixed sedimentary rocks—was systematically evaluated. The results show that crystalline dolomite and microbial dolomite generally exhibit the best reservoir quality. Their sample points, especially under higher pore pressure (50-70 MPa) conditions, are concentrated in quadrant I (low Kc, high SEI), and are defined as "fracture-enhanced" reservoirs.

[0205] Specifically, crystalline dolomite achieved the best parameters at a pore pressure of 70 MPa, indicating that this type of rock not only possesses extremely strong anti-compaction capabilities and can effectively protect the original pore space from damage, but also has a highly efficient fracture network that greatly improves the fluid permeability, making it the preferred "sweet spot" target for exploration and development. In comparison, grainy dolomite performed moderately poorly, with most of its samples distributed in the transition zone between quadrants I and II / III. The higher Kc value reveals that its rock mechanical strength is relatively weak, and there is a risk of pore stability during burial or production. Mixed sedimentary rocks had the worst reservoir performance, with all sample points consistently falling into quadrant III (high Kc, low SEI), belonging to the "compaction-damaged" reservoir type. Its high Kc value and extremely low SEI value together indicate that the rock is easily compacted and the fracture system is extremely underdeveloped or ineffective, lacking good reservoir and permeability conditions. Furthermore, with the increase of pore pressure, all lithological samples showed a trend of decreasing Kc value and a sharp increase in SEI value, which confirms that higher formation pore pressure plays a crucial and constructive role in maintaining pore structure stability and activating fracture network.

[0206] In summary, the "Kc-SEI" evaluation chart established in this invention successfully combines rock mechanical parameters with reservoir properties and permeability, providing a strong theoretical basis and quantitative evaluation tool for the fine classification of complex reservoirs, prediction of sweet spots, and risk avoidance in exploration and development.

[0207] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An experimental simulation and quantitative evaluation method applicable to the co-evolution of pores and fractures in carbonate rocks, characterized in that, include: S1 extracts geological process constraint parameters, integrates drilling core, logging, seismic and tectonic evolution data, reconstructs burial history, thermal history, pore pressure evolution history and diagenesis-pore evolution history, identifies key transformation stages that have a decisive impact on the formation of the current reservoir quality, and extracts the start and end temperatures, confining pressure, axial pressure and geological duration of each stage. S2, based on the time-temperature-stress equivalence principle, converts geological-scale time into laboratory loading time, generates an experimental loading scheme table containing target values ​​of multi-stage temperature, confining pressure, axial pressure and pore pressure, loading rate and holding time, and sets five parallel constant pore pressure conditions, namely 0MPa, 20MPa, 50MPa, 60MPa and 70MPa. S3, in a high-temperature and high-pressure triaxial servo system with independent control functions for four fields: temperature, confining pressure, axial pressure and pore pressure, multi-stage collaborative loading is implemented according to the experimental loading scheme table. The same temperature-confining pressure-axial pressure collaborative loading path is implemented for samples under different pore pressure conditions. The differential stress is increased according to the preset path to simulate the structural fracture process. S4. Multi-scale characterization was carried out before and after the experiment, including at least: thin section and scanning electron microscopy identification, porosity and permeability testing, nuclear magnetic resonance T2 spectrum testing, and microfocus CT scanning and three-dimensional reconstruction. Rigid registration and image difference were performed on the CT volume data before and after the experiment to obtain parameters such as the volume of newly formed cracks, the volume of effectively connected pores, the crack density increment, the volume of closed pores, and the change in coordination number. S5. Based on multi-source data before and after the experiment, three indicators were calculated: pore pressure retention coefficient, pore-fracture synergy index and fracture connectivity efficiency. Curves showing the variation of each indicator with pore pressure were plotted. A two-dimensional reservoir quality evaluation chart with pore pressure retention coefficient as the abscissa and pore-fracture synergy index as the ordinate was constructed for the analysis and quantitative evaluation of the pore-fracture synergy evolution mechanism. The pore pressure retention coefficient described in S5 is used to characterize the rock's resistance to compaction under different combinations of pore pressure and confining pressure. The lower the value, the easier it is for pores to be preserved for a long time under high pore pressure. The input of the coefficient includes the porosity before and after the experiment, as well as the confining pressure and pore pressure at the beginning and end of the experiment. The pore-fracture synergistic enhancement index described in S5 is used to measure the ratio between the relative improvement in permeability and the relative change in porosity, in order to determine the relative contribution of fractures to the improvement of seepage capacity. When the index is significantly higher than the threshold, it indicates that the seepage channels are controlled by fracture enhancement. The fracture connectivity efficiency described in S5 is used to characterize the effective pore volume that can be connected or newly generated per unit fracture volume, reflecting the efficiency of fractures in activating and connecting matrix pores. The input of the fracture connectivity efficiency includes at least the newly generated fracture volume and the connected effective pore volume obtained from S4. Based on the pore pressure preservation coefficient and pore-fracture synergy index calculated in step S5 of the method, a two-dimensional reservoir quality evaluation chart is established, with the pore pressure preservation coefficient as the abscissa and the pore-fracture synergy index as the ordinate. According to the distribution characteristics of the samples in the chart, the reservoirs are divided into fracture-enhanced type, potential risk type, compaction failure type, and pore-fracture stable type. The chart, combined with pore pressure and lithological information annotation, is used for high-quality reservoir identification, sweet spot evaluation, and predictive analysis.

2. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 1, characterized in that: The experimental loading described in S1 is divided into three stages: a simulated deep-buried compaction stage, a steady-state maintenance stage, and a tectonic fracturing stage. The burial history and thermal history of the target reservoir are restored based on basin simulation software. The paleocontainment pressure evolution path is calculated using integrated density logging data. The thermal history model is calibrated using vitrinite reflectivity and fluid inclusion homogenization temperature data. The paleopore pressure evolution history is restored using the sonic-density logging cross-plot method. Key alteration stages are identified based on diagenetic sequence analysis.

3. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 1, characterized in that: The experimental loading described in S3 is divided into a simulated deep-buried compaction stage, a steady-state maintenance stage, and a structural fracture stage. In the first two stages, the temperature and confining pressure are increased and maintained at a constant or segmented constant rate according to the scheme table. In the structural fracture stage, the axial pressure is gradually increased under constant temperature and confining pressure conditions to increase the differential stress. The five parallel samples differ only in the constant value of pore pressure, while the other loading paths are the same.

4. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 2, characterized in that: The effective stress was monitored in real time throughout the experiment, and the confining pressure was kept at least 20 MPa higher than the pore pressure to prevent hydraulic fracturing and ensure the stability of the four independent control fields. When a sudden drop in axial pressure or other safety thresholds were detected, the samples were unloaded and recovered in an orderly manner in the order of "reducing axial pressure - reducing confining pressure and pore pressure - cooling".

5. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 1, characterized in that: After rigid registration, the CT 3D data of S4 were subjected to image difference analysis. The differential voxels were extracted using threshold segmentation to quantify the total volume of newly formed cracks, crack density increment, closed pore volume, newly formed or connected pore volume, and coordination number change. The above results were then correlated with the pore pressure preservation coefficient, pore-crack synergy index, and crack connectivity efficiency to reveal the spatial characteristics of pore compression, crack propagation, and pore-crack interconnection.

6. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 1, characterized in that: Based on the multi-source characterization data obtained before and after the experiment in step S4 of the method, a quantitative relationship between pore pressure and three evaluation indicators is constructed. The relationship includes the correspondence between pore pressure preservation coefficient and pore pressure, the correspondence between pore-fracture synergistic efficiency index and pore pressure, and the correspondence between fracture connectivity efficiency and pore pressure. This is used to reveal the control law of pore pressure on the pore-fracture synergistic evolution process and to identify the optimal pore pressure window.

7. The experimental simulation and quantitative evaluation method for the co-evolution of pores and fractures in carbonate rocks according to claim 1, characterized in that: Based on the nuclear magnetic resonance and CT differential analysis results obtained in step S4 of the method, the reservoir evolution mechanism is determined by combining the trend of nuclear magnetic resonance T2 spectrum changes with the changes in the pore-fracture synergy index: when the pore-fracture synergy index is significantly high and the T2 spectrum shifts to the right, it is determined to be a fracture-dominated synergy; when the index is close to neutral and the T2 spectrum changes uniformly, it is determined to be a homogeneous compaction or equilibrium evolution type; when the index is low and the T2 spectrum shifts to the left, it indicates that the pore throat is closed or the number of ineffective fractures has increased, and the effectiveness of the seepage channels is further analyzed in combination with the changes in fracture connectivity efficiency.

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