Method and device for evaluating mobility of tight oil in shale

Abstract

The present application provides a shale tight oil mobility evaluation method and device, and relates to the technical field of unconventional oil and gas resource exploration, which comprises the following steps: pretreating core samples of a target layer section in shale, performing nuclear magnetic resonance experiment and grading centrifugation on the core samples, and determining the nuclear magnetic signal amount; converting the nuclear magnetic signal amount into a preset centrifugal force unit mass corresponding tight oil movable amount through a scale equation, and determining the mobility core index according to the tight oil movable amount and the centrifugal force; collecting logging data and pretreating the logging data; constructing a multiple linear regression model, wherein a quantitative relationship between the mobility core index and the logging data is established in the multiple linear regression model; inputting the logging data of the target well section into the multiple linear regression model, point by point calculating the mobility core index, forming a mobility continuous curve, continuously evaluating the tight oil mobility of the whole well section according to the mobility continuous curve, and obtaining the mobility evaluation result.

Description

Technical Field

[0001] This invention relates to the field of unconventional oil and gas resource exploration technology, and more particularly to a method and apparatus for evaluating the mobility of tight oil in mudstone and shale. Background Technology

[0002] This section is intended to provide background or context for embodiments of the present invention. The description herein is not intended to imply that it is prior art simply because it is included in this section.

[0003] With the increasing global demand for energy, tight oil, as an important unconventional oil and gas resource, has attracted widespread attention. However, accurately assessing the mobility and distribution of tight oil in shale and mudstone is crucial for optimizing exploration and development strategies. As an important component of unconventional oil and gas resources, the mobility assessment of tight oil is a core aspect of exploration and development. Mobility directly determines the production capacity and economic development potential of shale and mudstone reservoirs.

[0004] Traditional evaluation methods suffer from significant limitations in accuracy, applicability, and efficiency due to the complex occurrence states (free and adsorbed states), microscopic pore structures, and organic-inorganic interactions. Traditional methods for evaluating the mobility of tight oil in shale often rely on single experiments (such as mercury intrusion porosimetry and low-temperature nitrogen adsorption), making it difficult to distinguish between adsorbed and free oil and failing to dynamically characterize changes in the amount of mobile oil under different displacement forces. While nuclear magnetic resonance (NMR) technology can identify pore fluid distribution, it requires combination with physical displacement methods to quantify mobility. Furthermore, traditional well logging interpretation only reflects static physical properties such as porosity and permeability, failing to directly characterize the dynamic mobility of tight oil.

[0005] In summary, there is an urgent need for a technical solution that can overcome the above-mentioned shortcomings and effectively evaluate and predict the distribution of the "sweet spots" in tight oil mobility in shale and mudstone. Summary of the Invention

[0006] To address the problems existing in current technologies, this invention proposes a method and apparatus for evaluating the mobility of tight oil in shale and mudstone. By integrating nuclear magnetic resonance (NMR) technology and well logging analysis, this invention presents a novel technical solution to accurately assess and predict the mobility of tight oil in shale and mudstone, and effectively identify favorable exploration zones. The overall solution not only improves the accuracy of tight oil resource assessment but also provides a scientific basis for subsequent tight oil exploration and development.

[0007] In a first aspect of the present invention, a method for evaluating the mobility of tight oil in shale is proposed, the method comprising:

[0008] Core samples from the target layer in the mudstone and shale were pretreated. After pretreatment, the core samples were subjected to nuclear magnetic resonance experiments and fractional centrifugation to determine the nuclear magnetic signal quantity.

[0009] The nuclear magnetic resonance signal quantity is converted into the tight oil mobility per unit mass corresponding to a preset centrifugal force through a calibrated equation, and the core mobility index is determined based on the tight oil mobility and centrifugal force.

[0010] Well logging data is collected and preprocessed, wherein the core experimental data and well logging data are matched one-to-one after the preprocessing.

[0011] A multiple linear regression model is constructed, wherein a quantitative relationship between the core mobility index and well logging data is established in the multiple linear regression model.

[0012] The logging data of the target well section is input into the multivariate linear regression model, and the core mobility index is calculated point by point to form a continuous mobility curve. The mobility of tight oil in the entire well section is continuously evaluated based on the continuous mobility curve to obtain the mobility evaluation result.

[0013] In a second aspect of the present invention, a device for evaluating the mobility of tight oil in shale is provided, the device comprising:

[0014] The core experiment system is used to preprocess core samples from target strata in mudstone and shale. After preprocessing, the core samples are subjected to nuclear magnetic resonance experiments and fractional centrifugation to determine the nuclear magnetic signal quantity.

[0015] The index analysis module is used to convert the nuclear magnetic resonance signal quantity into the tight oil mobility corresponding to a unit mass of centrifugal force through a calibrated equation, and to determine the core mobility index based on the tight oil mobility and centrifugal force.

[0016] The data acquisition and processing module is used to acquire well logging data and preprocess the well logging data, wherein the preprocessing process matches the core experimental data with the well logging data one by one.

[0017] The model building module is used to build a multiple linear regression model, wherein a quantitative relationship between the core mobility index and well logging data is established in the multiple linear regression model.

[0018] The mobility evaluation module is used to input the logging data of the target well section into the multiple linear regression model, calculate the core mobility index point by point, form a continuous mobility curve, and continuously evaluate the mobility of tight oil in the entire well section based on the continuous mobility curve to obtain the mobility evaluation result.

[0019] In a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for evaluating the mobility of tight oil in shale.

[0020] In a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for evaluating the mobility of tight oil in shale.

[0021] In a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements a method for evaluating the mobility of tight oil in shale.

[0022] This invention proposes a method for evaluating the mobility of tight oil in shale and mudstone. By establishing a multiple linear regression model between tight oil mobility parameters and well logging parameters, it achieves quantitative prediction of mobility, filling the gap in the evaluation technology for the distribution of "sweet spots" in tight oil mobility. The statistically validated model construction method ensures the reliability of the prediction results. The model generates continuous profiles, indicating the optimal selection of favorable layers for tight oil exploration and development. Furthermore, this invention requires only a small amount of calibrated core data. By correlating well logging data with mobility parameters obtained from NMR-centrifugation through the multiple linear regression model, it reduces experimental dependence and significantly lowers the cost and time required for evaluating the "sweet spots" of tight oil mobility. Based on well logging data, the model prediction of this invention can complete the mobility analysis of the entire well section within 1-2 days, greatly shortening the testing cycle. Through the continuity of well logging data and the dynamic predictive capability of the multiple linear regression model, continuous evaluation of tight oil mobility throughout the entire well section is achieved. The multiple linear regression model used in this invention has a clear mathematical expression and statistical significance, clearly reflecting the contribution of each well logging parameter to mobility, ensuring the reliability and interpretability of the prediction results. Based on the tight oil mobility classification standard, this invention accurately identifies favorable stratigraphic intervals for tight oil exploration and development in mudstone and shale, providing strong technical support for unconventional oil and gas resource exploration. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a method for evaluating the mobility of tight oil in shale according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the process for preprocessing core samples from a target layer in shale according to an embodiment of the present invention.

[0026] Figure 3This is a graph showing the signal amplitude and signal accumulation under combined graded centrifugation in a nuclear magnetic resonance experiment according to the first embodiment of the present invention.

[0027] Figure 4 This is a graph showing the signal amplitude and signal accumulation under combined graded centrifugation in a nuclear magnetic resonance experiment according to the second embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram of a process for continuously evaluating the mobility of tight oil throughout the well section, according to an embodiment of the present invention.

[0029] Figure 6 This is a schematic diagram of the logical relationship of the steps in evaluating the mobility of tight oil in shale according to a specific embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram of the calibration relationship of the calibrated equations according to a specific embodiment of the present invention.

[0031] Figure 8 This is a diagram showing the distribution of mobility of dense oil per unit mass under different centrifugal forces according to a specific embodiment of the present invention.

[0032] Figure 9 This is a schematic diagram illustrating the quantitative relationship between the amount of free oil and the median centrifugal force in a specific embodiment of the present invention.

[0033] Figure 10 This is a schematic diagram showing the relationship between nuclear magnetic resonance experimental parameters and a single well logging curve in the first specific embodiment of the present invention.

[0034] Figure 11 This is a schematic diagram showing the relationship between nuclear magnetic resonance experimental parameters and a single well logging curve in the second specific embodiment of the present invention.

[0035] Figure 12 This is a schematic diagram showing the relationship between nuclear magnetic resonance experimental parameters and a single well logging curve in the third specific embodiment of the present invention.

[0036] Figure 13 This is the actual calculation of Q in a specific embodiment of the present invention. f / ΔP L Fitting Q to the multiple linear regression model f / ΔP L Related image.

[0037] Figure 14 This is a specific embodiment of the present invention that fits Q using a multiple linear regression model. f / ΔP L Create a favorable layer division diagram.

[0038] Figure 15 This is a schematic diagram of the architecture of a device for evaluating the mobility of tight oil in shale according to an embodiment of the present invention.

[0039] Figure 16 This is a schematic diagram of a computer device structure according to an embodiment of the present invention. Detailed Implementation

[0040] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0041] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0042] According to an embodiment of the present invention, a method and apparatus for evaluating the mobility of tight oil in shale are proposed, which relates to the field of unconventional oil and gas resource exploration technology.

[0043] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0044] Figure 1 This is a schematic flowchart of a method for evaluating the mobility of tight oil in shale according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0045] S101, pre-process the core sample of the target layer in the mudstone and shale, and after pre-processing, perform nuclear magnetic resonance experiments and graded centrifugation on the core sample to determine the nuclear magnetic signal quantity.

[0046] S102, the nuclear magnetic resonance signal quantity is converted into the tight oil mobility quantity corresponding to a unit mass of centrifugal force through the calibrated equation, and the core mobility index is determined based on the tight oil mobility quantity and centrifugal force.

[0047] S103, collect logging data and preprocess the logging data, wherein the core test data and logging data are matched one-to-one after preprocessing;

[0048] S104, Construct a multiple linear regression model, wherein a quantitative relationship between the core mobility index and logging data is established in the multiple linear regression model;

[0049] S105, input the logging data of the target well section into the multivariate linear regression model, calculate the core mobility index point by point, form a continuous mobility curve, and continuously evaluate the mobility of tight oil in the entire well section according to the continuous mobility curve to obtain the mobility evaluation result.

[0050] This invention proposes a method for evaluating the mobility of tight oil in shale and mudstone. By establishing a multiple linear regression model between tight oil mobility parameters and well logging parameters, it achieves quantitative prediction of mobility, filling the gap in the evaluation technology for the distribution of "sweet spots" in tight oil mobility. The statistically validated model construction method ensures the reliability of the prediction results. The model generates continuous profiles, indicating the optimal selection of favorable layers for tight oil exploration and development. Traditional methods require extensive core experiments for each well, while this invention requires only a smaller amount of calibrated core data. By using a multiple linear regression model to correlate well logging data with mobility parameters obtained from NMR-centrifugation, it reduces experimental dependence and significantly lowers the cost and time required for evaluating the "sweet spots" of tight oil mobility. Based on well logging data, this invention's model prediction can complete the mobility analysis of the entire well section within 1-2 days (compared to 2-3 months for traditional single-well evaluation methods), greatly shortening the testing cycle. Through the continuity of well logging data and the dynamic predictive capability of the multiple linear regression model, continuous evaluation of tight oil mobility across the entire well section is achieved. The multiple linear regression model in this method has a clear mathematical expression and statistical significance, which can clearly reflect the contribution of each logging parameter to mobility, ensuring the reliability and interpretability of the prediction results. This invention accurately identifies favorable stratigraphic intervals for tight oil exploration and development in shale and mudstone based on the tight oil mobility grading standard.

[0051] To provide a clearer explanation of the above-mentioned method for evaluating the mobility of tight oil in shale and mudstone, each step will be explained in detail below.

[0052] In one embodiment, S101, core samples from the target layer of shale are pretreated. After pretreatment, nuclear magnetic resonance (NMR) experiments and fractional centrifugation are performed on the core samples to determine the NMR signal intensity, with reference to... Figure 2 The specific process includes:

[0053] S101-1, take a core sample from the target layer of mudstone and shale, and use wire cutting to cut the core sample into a core column of a preset size.

[0054] Specifically, core samples from the target layer are taken and cut into core columns with a diameter of about 2.5 cm and a length of 30-50 mm using wire cutting.

[0055] S101-2, The core column is subjected to oil washing treatment; wherein, the core column is placed in an oil washing instrument and the oil is washed using a mixed solution of dichloromethane and acetone under a first preset temperature and pressure condition to remove residual hydrocarbons from the core column.

[0056] Specifically, the core column is placed in an oil washing apparatus and washed with a mixed solution of dichloromethane and acetone (volume ratio of approximately 3:1) at 0.3 MPa and 90°C for at least 7 days to remove residual hydrocarbons from the core column.

[0057] S101-3, the washed core column is placed in a drying oven and dried under the second preset temperature and pressure conditions to remove residual water from the core column. The dried core column is weighed and recorded, and a one-dimensional nuclear magnetic resonance experiment is carried out.

[0058] Specifically, the washed core column was placed in a drying oven and dried at 110℃ and -0.1MPa for 48 hours to remove residual water from the core column. The dried core column was weighed and recorded, and a one-dimensional nuclear magnetic resonance experiment was carried out.

[0059] S101-4. The dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator. A vacuum pump was used to evacuate the core column. The core column was saturated with n-dodecane under the third preset temperature and pressure conditions. After saturation, the core column was left to stand in the n-dodecane solution. The oil on the surface of the core column was wiped dry, weighed and recorded. The one-dimensional nuclear magnetic resonance experiment was performed on the saturated oil sample.

[0060] Specifically, the dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator and evacuated for 24 hours using a vacuum pump. Then, the core column was saturated with n-dodecane under temperature and pressure conditions of 20 MPa and 20 °C for 7 days. After saturation, the core column was left to stand in the n-dodecane solution for 24 hours. The oil on the surface of the core column was wiped dry, weighed and recorded. One-dimensional nuclear magnetic resonance experiments were then performed on the saturated oil sample.

[0061] S101-5, after the one-dimensional nuclear magnetic resonance experiment, the saturated oil core column sample was centrifuged under the fourth temperature and pressure conditions. The sample was weighed and recorded after each centrifugation under different rotation speeds, and the one-dimensional nuclear magnetic resonance test was repeated. The residual oil signal quantity under different centrifugal forces was determined by nuclear magnetic resonance T2 spectrum, the signal quantity decay curve was recorded, and the movable oil signal quantity under each centrifugation state was cumulatively determined.

[0062] Specifically, after the one-dimensional nuclear magnetic resonance experiment, the saturated oil core sample was centrifuged at 20°C and normal pressure. The rotation speed was set to 5000 r / min and 10000 r / min, and centrifuged for 4 hours at each rotation speed. The sample weight was recorded after each centrifugation, and the one-dimensional nuclear magnetic resonance test was repeated.

[0063] The residual oil signal quantity under different centrifugal forces was determined by nuclear magnetic resonance T2 spectroscopy, and the signal decay curves were recorded. The movable oil signal quantity under each centrifugal state was accumulated for reference. Figure 3 and Figure 4 The figures shown are the signal amplitude and signal accumulation under the combined staged centrifugation of nuclear magnetic resonance experiments in the first and second embodiments, respectively.

[0064] In nuclear magnetic resonance (NMR) T2 spectroscopy, the T2 relaxation time describes the transverse relaxation process, i.e., the time process of proton group dephase. Hydrogen protons in different environments (such as those in different pore structures and fluid properties) have different T2 relaxation times. By detecting and analyzing the decay of the T2 relaxation signal over time, the NMR T2 spectrum can be obtained. NMR T2 spectroscopy is mainly used to analyze rock pore structure and fluid properties. Fluids (oil, gas, water) in pores of different sizes have different T2 relaxation times. T2 spectroscopy can help determine rock porosity and permeability, distinguish fluid types, and assess the content of mobile fluids, which is crucial for evaluating the mobility of unconventional oil and gas resources such as tight oil and shale gas.

[0065] S101-6, using n-dodecane as a standard, the linear relationship between mass and NMR signal quantity was determined to identify the NMR signal quantity.

[0066] Specifically, using n-dodecane as a standard, the linear relationship between mass and NMR signal intensity was determined, and a calibration equation was established:

[0067] S = k·m + b; (1)

[0068] In the formula, S is the signal quantity, m is the mass in mg / g, and k and b are the fitting coefficients.

[0069] In one embodiment, S102, the nuclear magnetic resonance signal is converted into the tight oil mobility per unit mass corresponding to a preset centrifugal force using a calibrated equation. Based on the tight oil mobility and centrifugal force, core mobility indicators are determined, including:

[0070] Specifically, based on adsorption isotherm equations (e.g., the Langmuir equation), the mobility (Q) of tight oil per unit mass under different centrifugal forces is constructed. m The fitting relationship between the oil and centrifugal force (ΔP) is used to obtain the theoretical maximum oil mobility (Q) through linear regression. f ) and centrifugal median pressure (ΔP) L ), and calculate the ratio of the theoretical maximum tight oil mobility to the centrifugal median pressure (Q). f / ΔP L (), as the core indicator of mobility.

[0071] The method for calculating the mobility of the tight oil is as follows:

[0072]

[0073]

[0074] Equation (3) is a variation of equation (2).

[0075] In the formula, Q m The amount of dense oil mobility corresponding to a unit mass of preset centrifugal force, in mg / g;

[0076] Q f The mass of dense oil under infinite centrifugal force, expressed in mg / g.

[0077] ΔP is the centrifugal force, converted according to the centrifuge speed, and the unit is MPa;

[0078] ΔP L The median centrifugal force is given by the value of Q, where Q is the movable oil volume. f Centrifugal force at half the value, in MPa.

[0079] In one embodiment, S103, well logging data is collected and preprocessed, wherein the core experimental data and well logging data are matched one-to-one after preprocessing.

[0080] Specifically, for the well logging data, through data standardization and depth correction, the core experimental data and well logging data are matched one-to-one.

[0081] The collected well logging data includes at least porosity. Permeability (K), resistivity (RT), acoustic transit time (AC), density (DEN), neutron porosity (CNL), natural gamma (GR).

[0082] In one embodiment, S104, a multiple linear regression model is constructed, wherein a quantitative relationship between the mobility core index and well logging data is established in the multiple linear regression model.

[0083] Specifically, in the multiple linear regression model, the formula for the quantitative relationship between the core mobility index and well logging data is as follows:

[0084]

[0085] In the formula, As a core indicator of mobility, it represents the mobility oil efficiency under unit centrifugal force;

[0086] β0 is the intercept term, representing the value when all logging parameters are 0. value;

[0087] ΔT norm SP norm GR norm These represent the standardized sonic transit time logging curve value, the standardized spontaneous potential logging curve value, and the standardized natural gamma ray logging curve value, respectively; ΔTnorm Reflects the acoustic properties and pore structure information of the formation; SP norm Related to the electrochemical properties of formation water and mud filtrate; GR norm Characterizes the content of radioactive elements in strata;

[0088] β1, β2, and β3 are regression coefficients, representing the acoustic time difference, spontaneous potential, and natural gamma pair, respectively. The degree of contribution;

[0089] ε is the error term, representing the random error that the model fails to explain.

[0090] In one embodiment, S105, the logging data of the target well section is input into the multiple linear regression model, the core mobility index is calculated point by point to form a continuous mobility curve, and the mobility of tight oil in the whole well section is continuously evaluated based on the continuous mobility curve to obtain the mobility evaluation result.

[0091] refer to Figure 5 The specific process is as follows:

[0092] S105-1, input the logging data of the target well section into the model, and calculate the core mobility index (Q) point by point (0.1m resolution). f / ΔP L (value), forming a continuous mobility curve.

[0093] S105-2, Construct a favorable segment division and grading standard.

[0094] Specifically, the grading criteria for favorable strata can be as follows:

[0095] Category I Dessert Layer: Q f / ΔP L ≥25 (High flexibility, priority development);

[0096] Category II Potential Level: 15≤Q f / ΔP L <25 (Medium mobility, requires fracturing modification);

[0097] Category III Non-Dessert Layer: Q f / ΔP L <15 (Low flexibility, development postponed).

[0098] S105-3, based on the favorable strata division grading criteria and mobility continuity curve, continuously evaluate the mobility of tight oil throughout the well section.

[0099] The above steps can be used to continuously evaluate the mobility of tight oil throughout the well section.

[0100] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0101] The following describes the method for evaluating the mobility of tight oil in shale according to the present invention with reference to a specific embodiment.

[0102] refer to Figure 6 This is a schematic diagram illustrating the logical relationship of steps in evaluating the mobility of tight oil in shale according to a specific embodiment of the present invention. Figure 6 As shown, taking mudstone and shale in a certain basin as an example, the mobility of tight oil in mudstone and shale is evaluated. The specific methods include:

[0103] S1, Sample preparation and NMR experiments:

[0104] 1) Take the corresponding core sample from the target layer and cut the core sample into core columns with a diameter of about 2.5 cm and a length of 30-50 mm using wire cutting.

[0105] 2) The cut core column is then washed with oil.

[0106] The core column was placed in an oil washing apparatus and washed for at least 7 days using a mixed solution of dichloromethane and acetone (volume ratio of approximately 3:1) at 0.3 MPa and 90°C to remove residual hydrocarbons from the core column.

[0107] 3) Place the washed core column in a drying oven and dry it for 48 hours at 110℃ and -0.1MPa to remove residual water from the core column. Weigh and record the dried core column and conduct one-dimensional nuclear magnetic resonance experiments.

[0108] 4) The dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator and vacuumed for 24 hours. Then, the core column was saturated with n-dodecane at 20 MPa and 20 °C for 7 days. After saturation, the core column was left to stand in the n-dodecane solution for 24 hours. The oil on the surface of the core column was wiped dry, weighed and recorded. The saturated oil sample was then subjected to a one-dimensional nuclear magnetic resonance experiment.

[0109] 5) After the one-dimensional nuclear magnetic resonance experiment, the saturated oil core sample was centrifuged at 20°C and normal pressure. The rotation speed was set to 5000 r / min and 10000 r / min. The sample was centrifuged for 4 hours at each rotation speed. The sample weight was recorded after each centrifugation, and the one-dimensional nuclear magnetic resonance test was repeated.

[0110] The residual oil signal quantity under different centrifugal forces was determined by nuclear magnetic resonance T2 spectroscopy. The signal decay curves were recorded, and the movable oil signal quantity under each centrifugation state was accumulated. The signal amplitude and signal accumulation under the combined nuclear magnetic resonance experiment and fractional centrifugation are shown in the figure below. Figure 3 and Figure 4 As shown.

[0111] 6) Using n-dodecane as a standard, determine the linear relationship between its mass and NMR signal intensity, and establish a calibration equation:

[0112] S = k·m + b; (1)

[0113] In equation (1), S is the signal quantity, m is the mass in mg / g, and k and b are the fitting coefficients.

[0114] refer to Figure 7 The diagram shown illustrates the calibration relationship of the calibrated equations according to a specific embodiment of the present invention. Figure 7 As shown, S = 11076.9494·m was obtained, and the coefficient of determination R² was 0.97.

[0115] S2, Determine the experimental parameters:

[0116] 1) Convert the nuclear magnetic resonance signal quantity into the mobility of a unit mass of tight oil under a certain centrifugal force through the calibrated equation.

[0117] refer to Figure 8 The figure shown is a distribution diagram of the mobility of dense oil per unit mass under different centrifugal forces according to a specific embodiment of the present invention.

[0118] 2) The specific formulas for calculating the mobility parameters of tight oil include:

[0119]

[0120] In equations (2) and (3), Q m The amount of dense oil mobility corresponding to a unit mass of preset centrifugal force, in mg / g;

[0121] Q f The mass of dense oil under infinite centrifugal force, expressed in mg / g.

[0122] ΔP is the centrifugal force, converted according to the centrifuge speed, and the unit is MPa;

[0123] ΔP L The median centrifugal force is given by the value of Q, where Q is the movable oil volume. f Centrifugal force at half the value, in MPa.

[0124] Based on the Langmuir equation, the mobility (Q) of tight oil per unit mass under different centrifugal forces was constructed. mThe fitting relationship between the oil and centrifugal force (ΔP) is used to obtain the theoretical maximum oil mobility (Q) through linear regression. f ) and centrifugal median pressure (ΔP) L ), and calculate Q. f / ΔP L ratio.

[0125] refer to Figure 9 The diagram shown illustrates the quantitative relationship between free oil volume and median centrifugal force in a specific embodiment of the present invention.

[0126] In a specific embodiment, the calculation results are shown in Table 1.

[0127] Table 1

[0128] serial number <![CDATA[1 / Q f ]]> <![CDATA[ΔP L / Q f ]]> <![CDATA[Movable oil amount Q f > <![CDATA[ΔP L ]]> B7 0.204 0.04 4.904 0.198 B11 0.277 0.072 3.61 0.261 B37 0.137 0.027 7.305 0.2 B47 0.129 0.042 7.735 0.325 B53 0.188 0.064 5.327 0.341 B54 0.192 0.088 5.198 0.4591 B55 0.104 0.089 9.64 0.857 B58 0.157 0.103 6.368 0.654 B60 0.195 0.076 5.13 0.391 B61 0.249 0.216 4.023 0.869 B71 0.22 0.048 4.543 0.219 B76 0.164 0.332 6.105 2.029 b20 0.055 0.061 18.149 1.107 b25 0.147 0.082 6.826 0.558 b38 0.029 0.046 34.014 1.558

[0129] S3, Well Logging Data Acquisition and Preprocessing:

[0130] Collect well logging curves at the corresponding depths, including porosity. Permeability (K), resistivity (RT), acoustic transit time (AC), density (DEN), neutron porosity (CNL), natural gamma (GR), etc., are matched one-to-one with core experimental data and well logging data through data standardization and depth correction.

[0131] refer to Figure 10 , Figure 11 , Figure 12 The figures shown are schematic diagrams relating nuclear magnetic resonance experimental parameters and single well logging curves to the first, second, and third specific embodiments of the present invention, respectively.

[0132] S4, Construction of the Multiple Linear Regression Model:

[0133] Multiple linear regression models are used to establish nuclear magnetic resonance experimental parameters (Q). f / ΔP L The quantitative relationship between the logging curve and the data is shown in the model formula below:

[0134]

[0135] In equation (4), As a core indicator of mobility, it represents the mobility oil efficiency under unit centrifugal force;

[0136] The coefficient of determination R² = 0.976 indicates a good correlation, and the evaluation model is effective for the target variable (Q). f / ΔP L It has good explanatory power.

[0137] refer to Figure 13This is the actual calculation of Q in a specific embodiment of the present invention. f / ΔP L Fitting Q to the multiple linear regression model f / ΔP L Related image.

[0138] S5, Prediction of tight oil mobility and identification of favorable strata:

[0139] 1) Input the logging data of the target well section into the model, and calculate Q point by point (0.1m resolution). f / ΔP L The value forms a continuous mobility curve.

[0140] 2) Construct a favorable stratification and grading standard:

[0141] Category I Dessert Layer: Q f / ΔP L ≥25 (High flexibility, priority development);

[0142] Category II Potential Level: 15≤Q f / ΔP L <25 (Medium mobility, requires fracturing modification);

[0143] Category III Non-Dessert Layer: Q f / ΔP L <15 (Low flexibility, development postponed).

[0144] 3) Based on the classification criteria for favorable layers and the continuous mobility curve, the mobility of tight oil in the entire well section is continuously evaluated.

[0145] refer to Figure 14 This is a specific embodiment of the present invention that fits Q using a multiple linear regression model. f / ΔP L Create a favorable layer division diagram.

[0146] The present invention proposes an evaluation method for the mobility and favorable strata of tight oil in shale and mudstone, and establishes a multiple linear regression model of tight oil mobility parameters and logging parameters to achieve quantitative prediction of mobility, filling the gap in the evaluation technology of the distribution of the "sweet spot" of tight oil mobility.

[0147] This invention utilizes a statistically validated model construction method to ensure reliable prediction results. It generates continuous profiles through the model, identifying favorable zones for tight oil exploration and development. Traditional methods require extensive core experiments for each well, while this invention requires only a smaller amount of calibrated core data. By using a multiple linear regression model to correlate well logging data with mobility parameters obtained from NMR-centrifugation, it reduces experimental dependence and significantly lowers the cost and time required for evaluating the "sweet spot" of tight oil mobility.

[0148] Traditional methods require 2-3 months for single-well evaluation, while this invention, based on well logging data and model prediction, can complete the mobility analysis of the entire well section within 1-2 days. By leveraging the continuity of well logging data and the dynamic predictive capabilities of the multiple linear regression model, continuous evaluation of tight oil mobility across the entire well section is achieved. The multiple linear regression model in this method has a clear mathematical expression and statistical significance, clearly reflecting the contribution of each well logging parameter to mobility, ensuring the reliability and interpretability of the prediction results. This invention accurately identifies favorable zones for tight oil exploration and development in shale and mudstone based on tight oil mobility grading standards.

[0149] After introducing the method of exemplary embodiments of the present invention, the following references are made. Figure 15 An exemplary embodiment of the present invention will be described, which is a device for evaluating the mobility of tight oil in shale.

[0150] The implementation of the device for evaluating the mobility of tight oil in shale and mudstone can refer to the implementation of the above-described method, and will not be repeated here. The term "module" or "unit" used below can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0151] Based on the same inventive concept, this invention also proposes a device for evaluating the mobility of tight oil in shale, such as... Figure 15 As shown, the device includes:

[0152] The core experiment system 1510 is used to preprocess core samples from target layers in mudstone and shale. After preprocessing, the core samples are subjected to nuclear magnetic resonance experiments and fractional centrifugation to determine the nuclear magnetic signal quantity.

[0153] The index analysis module 1520 is used to convert the nuclear magnetic resonance signal quantity into the tight oil mobility corresponding to a unit mass of centrifugal force through a calibrated equation, and to determine the core mobility index based on the tight oil mobility and centrifugal force.

[0154] The data acquisition and processing module 1530 is used to acquire well logging data and preprocess the well logging data, wherein the preprocessing process matches the core test data with the well logging data one by one.

[0155] The model building module 1540 is used to build a multiple linear regression model, wherein a quantitative relationship between the mobility core index and well logging data is established in the multiple linear regression model.

[0156] The mobility evaluation module 1550 is used to input the logging data of the target well section into the multiple linear regression model, calculate the core mobility index point by point, form a continuous mobility curve, and continuously evaluate the mobility of tight oil in the entire well section based on the continuous mobility curve to obtain the mobility evaluation result.

[0157] In one embodiment, the core testing system 1510 preprocesses core samples from the target layer in shale. After preprocessing, the core samples undergo nuclear magnetic resonance (NMR) experiments and fractional centrifugation to determine the NMR signal quantity. The specific procedure is as follows:

[0158] Core samples were taken from the target layer of mudstone and shale, and the core samples were cut into core columns of a predetermined size using wire cutting.

[0159] The core column is subjected to oil washing treatment; wherein, the core column is placed in an oil washing instrument and washed with a mixed solution of dichloromethane and acetone under a first preset temperature and pressure condition to remove residual hydrocarbons from the core column;

[0160] After washing the oil, the core column was placed in a drying oven and dried under the second preset temperature and pressure conditions to remove residual water from the core column. The dried core column was weighed and recorded, and a one-dimensional nuclear magnetic resonance experiment was carried out.

[0161] The dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator. A vacuum pump was used to evacuate the core column. The core column was saturated with n-dodecane under the third preset temperature and pressure conditions. After saturation, the core column was allowed to stand in the n-dodecane solution. The oil on the surface of the core column was wiped dry, weighed and recorded. The one-dimensional nuclear magnetic resonance experiment was performed on the saturated oil sample.

[0162] After completing the one-dimensional nuclear magnetic resonance (NMR) experiment, the saturated oil core sample was centrifuged under the fourth temperature and pressure conditions. The sample was weighed and recorded after each centrifugation under different rotation speeds, and the one-dimensional NMR test was repeated. The residual oil signal quantity under different centrifugal forces was determined by the NMR T2 spectrum, and the signal quantity decay curve was recorded to determine the movable oil signal quantity under each centrifugation state.

[0163] Using n-dodecane as a standard, the linear relationship between mass and NMR signal intensity was determined to identify the NMR signal intensity.

[0164] In one embodiment, the indicator analysis module 1520 is specifically used for:

[0165] Based on the adsorption isotherm equation, a fitting relationship between the mobility of tight oil per unit mass and the centrifugal force under different centrifugal forces is constructed. The theoretical maximum mobility of tight oil and the centrifugal median pressure are obtained through linear regression. The ratio of the theoretical maximum mobility of tight oil to the centrifugal median pressure is calculated as the core indicator of mobility.

[0166] In one embodiment, the method for calculating the mobility of the tight oil is as follows:

[0167]

[0168] In the formula, Q m The amount of dense oil mobility corresponding to a unit mass of preset centrifugal force, in mg / g;

[0169] Q f The mass of dense oil under infinite centrifugal force, expressed in mg / g.

[0170] ΔP is the centrifugal force, converted according to the centrifuge speed, and the unit is MPa;

[0171] ΔP L The median centrifugal force is given by the value of Q, where Q is the movable oil volume. f Centrifugal force at half the value, in MPa.

[0172] In one embodiment, the data acquisition and processing module 1530 is specifically used for:

[0173] For the well logging data, through data standardization and depth correction, the core experimental data and well logging data are matched one-to-one; wherein, the collected well logging data includes at least porosity, permeability, resistivity, sonic transit time, density, neutron porosity, and natural gamma.

[0174] In one embodiment, in the multiple linear regression model constructed by the model building module 1540, the formula for the quantitative relationship between the mobility core index and well logging data is as follows:

[0175]

[0176] In the formula, As a core indicator of mobility, it represents the mobility oil efficiency under unit centrifugal force;

[0177] β0 is the intercept term, representing the value when all logging parameters are 0. value;

[0178] ΔT norm SP norm GR norm These represent the standardized sonic transit time logging curve value, the standardized spontaneous potential logging curve value, and the standardized natural gamma ray logging curve value, respectively; ΔT norm Reflects the acoustic properties and pore structure information of the formation; SP norm Related to the electrochemical properties of formation water and mud filtrate; GR norm Characterizes the content of radioactive elements in strata;

[0179] β1, β2, and β3 are regression coefficients, representing the acoustic time difference, spontaneous potential, and natural gamma pair, respectively. The degree of contribution;

[0180] ε is the error term, representing the random error that the model fails to explain.

[0181] In one embodiment, the mobility evaluation module 1550 is specifically used for:

[0182] Construct a tiered standard for the division of advantageous strata;

[0183] Based on the classification criteria for favorable strata and the continuous mobility curve, the mobility of tight oil in the entire well section is continuously evaluated.

[0184] It should be noted that although several modules of the tight oil mobility evaluation device in shale have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0185] Based on the aforementioned inventive concept, such as Figure 16 As shown, the present invention also proposes a computer device 1600, including a memory 1610, a processor 1620, and a computer program 1630 stored in the memory 1610 and executable on the processor 1620. When the processor 1620 executes the computer program 1630, it implements the aforementioned method for evaluating the mobility of tight oil in shale.

[0186] Based on the aforementioned inventive concept, this invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for evaluating the mobility of tight oil in shale.

[0187] Based on the aforementioned inventive concept, this invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements a method for evaluating the mobility of tight oil in shale and mudstone.

[0188] This invention proposes a method for evaluating the mobility of tight oil in shale and mudstone. By establishing a multiple linear regression model between tight oil mobility parameters and well logging parameters, it achieves quantitative prediction of mobility, filling the gap in the evaluation technology for the distribution of "sweet spots" in tight oil mobility. The statistically validated model construction method ensures the reliability of the prediction results. The model generates continuous profiles, indicating the optimal selection of favorable layers for tight oil exploration and development. Traditional methods require extensive core experiments for each well, while this invention requires only a smaller amount of calibrated core data. By using a multiple linear regression model to correlate well logging data with mobility parameters obtained from NMR-centrifugation, it reduces experimental dependence and significantly lowers the cost and time required for evaluating the "sweet spots" of tight oil mobility. Based on well logging data, this invention's model prediction can complete the mobility analysis of the entire well section within 1-2 days (compared to 2-3 months for traditional single-well evaluation methods), greatly shortening the testing cycle. Through the continuity of well logging data and the dynamic predictive capability of the multiple linear regression model, continuous evaluation of tight oil mobility across the entire well section is achieved. The multiple linear regression model in this method has a clear mathematical expression and statistical significance, which can clearly reflect the contribution of each logging parameter to mobility, ensuring the reliability and interpretability of the prediction results. Based on the tight oil mobility classification standard, this invention accurately identifies favorable stratigraphic intervals for tight oil exploration and development in shale and mudstone, providing strong technical support for unconventional oil and gas resource exploration.

[0189] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0190] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] This invention is described with reference to flowchart illustrations and / or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0192] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0193] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0194] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the mobility of tight oil in shale and mudstone, characterized in that, The method includes: Core samples from the target layer in the mudstone and shale were pretreated. After pretreatment, the core samples were subjected to nuclear magnetic resonance experiments and fractional centrifugation to determine the nuclear magnetic signal quantity. The nuclear magnetic resonance signal quantity is converted into the tight oil mobility per unit mass corresponding to a preset centrifugal force through a calibrated equation, and the core mobility index is determined based on the tight oil mobility and centrifugal force. Well logging data is collected and preprocessed, wherein the core experimental data and well logging data are matched one-to-one after the preprocessing. A multiple linear regression model is constructed, wherein a quantitative relationship between the core mobility index and well logging data is established in the multiple linear regression model. The logging data of the target well section is input into the multivariate linear regression model, and the core mobility index is calculated point by point to form a continuous mobility curve. The mobility of tight oil in the entire well section is continuously evaluated based on the continuous mobility curve to obtain the mobility evaluation result.

2. The method for evaluating the mobility of tight oil in shale and mudstone according to claim 1, characterized in that, Core samples from the target strata of shale were pretreated, and then subjected to nuclear magnetic resonance (NMR) experiments and fractionated centrifugation to determine the NMR signal intensity, including: Core samples were taken from the target layer of mudstone and shale, and the core samples were cut into core columns of a predetermined size using wire cutting. The core column is subjected to oil washing treatment; wherein, the core column is placed in an oil washing instrument and washed with a mixed solution of dichloromethane and acetone under a first preset temperature and pressure condition to remove residual hydrocarbons from the core column; After washing the oil, the core column was placed in a drying oven and dried under the second preset temperature and pressure conditions to remove residual water from the core column. The dried core column was weighed and recorded, and a one-dimensional nuclear magnetic resonance experiment was carried out. The dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator. A vacuum pump was used to evacuate the core column. The core column was saturated with n-dodecane under the third preset temperature and pressure conditions. After saturation, the core column was allowed to stand in the n-dodecane solution. The oil on the surface of the core column was wiped dry, weighed and recorded. The one-dimensional nuclear magnetic resonance experiment was performed on the saturated oil sample. After completing the one-dimensional nuclear magnetic resonance (NMR) experiment, the saturated oil core sample was centrifuged under the fourth temperature and pressure conditions. The sample was weighed and recorded after each centrifugation under different rotation speeds, and the one-dimensional NMR test was repeated. The residual oil signal quantity under different centrifugal forces was determined by the NMR T2 spectrum, and the signal quantity decay curve was recorded to determine the movable oil signal quantity under each centrifugation state. Using n-dodecane as a standard, the linear relationship between mass and NMR signal intensity was determined to identify the NMR signal intensity.

3. The method for evaluating the mobility of tight oil in shale and mudstone according to claim 1, characterized in that, The nuclear magnetic resonance signal is converted into the tight oil mobility per unit mass corresponding to a preset centrifugal force using a calibrated equation. Based on the tight oil mobility and centrifugal force, core mobility indicators are determined, including: Based on the adsorption isotherm equation, a fitting relationship between the mobility of tight oil per unit mass and the centrifugal force under different centrifugal forces is constructed. The theoretical maximum mobility of tight oil and the centrifugal median pressure are obtained through linear regression. The ratio of the theoretical maximum mobility of tight oil to the centrifugal median pressure is calculated as the core indicator of mobility.

4. The method for evaluating the mobility of tight oil in shale according to claim 3, characterized in that, The method for calculating the mobility of the tight oil is as follows: In the formula, Q m The amount of dense oil mobility corresponding to a unit mass of preset centrifugal force, in mg / g; Q f The mass of dense oil under infinite centrifugal force, expressed in mg / g. ΔP is the centrifugal force, converted according to the centrifuge speed, and the unit is MPa; ΔP L The median centrifugal force is given by the value of Q, where Q is the movable oil volume. f Centrifugal force at half the value, in MPa.

5. The method for evaluating the mobility of tight oil in shale and mudstone according to claim 1, characterized in that, Acquire well logging data and preprocess the well logging data, wherein the preprocessing involves matching the core experimental data with the well logging data one-to-one, including: For the well logging data, through data standardization and depth correction, the core experimental data and well logging data are matched one-to-one; wherein, the collected well logging data includes at least porosity, permeability, resistivity, sonic transit time, density, neutron porosity, and natural gamma.

6. The method for evaluating the mobility of tight oil in shale according to claim 1, characterized in that, In the multiple linear regression model, the formula for the quantitative relationship between the core mobility index and well logging data is as follows: In the formula, As a core indicator of mobility, it represents the mobility oil efficiency under unit centrifugal force; β0 is the intercept term, representing the value when all logging parameters are 0. value; ΔT norm SP norm GR norm These represent the standardized sonic transit time logging curve value, the standardized spontaneous potential logging curve value, and the standardized natural gamma ray logging curve value, respectively; ΔT norm Reflects the acoustic properties and pore structure information of the formation; SP norm Related to the electrochemical properties of formation water and mud filtrate; GR norm Characterizes the content of radioactive elements in strata; β1, β2, and β3 are regression coefficients, representing the acoustic time difference, spontaneous potential, and natural gamma pair, respectively. The degree of contribution; ε is the error term, representing the random error that the model fails to explain.

7. The method for evaluating the mobility of tight oil in shale according to claim 1, characterized in that, The logging data of the target well section is input into the multiple linear regression model, and the core mobility index is calculated point by point to form a continuous mobility curve. Based on the continuous mobility curve, the mobility of tight oil in the entire well section is continuously evaluated to obtain the mobility evaluation results, including: Construct a tiered standard for the division of advantageous strata; Based on the classification criteria for favorable strata and the continuous mobility curve, the mobility of tight oil in the entire well section is continuously evaluated.

8. A device for evaluating the mobility of tight oil in mudstone and shale, characterized in that, The device includes: The core experiment system is used to preprocess core samples from target strata in mudstone and shale. After preprocessing, the core samples are subjected to nuclear magnetic resonance experiments and fractional centrifugation to determine the nuclear magnetic signal quantity. The index analysis module is used to convert the nuclear magnetic resonance signal quantity into the tight oil mobility corresponding to a unit mass of centrifugal force through a calibrated equation, and to determine the core mobility index based on the tight oil mobility and centrifugal force. The data acquisition and processing module is used to acquire well logging data and preprocess the well logging data, wherein the preprocessing process matches the core experimental data with the well logging data one by one. The model building module is used to build a multiple linear regression model, wherein a quantitative relationship between the core mobility index and well logging data is established in the multiple linear regression model. The mobility evaluation module is used to input the logging data of the target well section into the multiple linear regression model, calculate the core mobility index point by point, form a continuous mobility curve, and continuously evaluate the mobility of tight oil in the entire well section based on the continuous mobility curve to obtain the mobility evaluation result.

9. The device for evaluating the mobility of tight oil in shale according to claim 8, characterized in that, The core testing system preprocesses core samples from the target strata of shale. After preprocessing, the core samples undergo nuclear magnetic resonance (NMR) experiments and fractional centrifugation to determine the NMR signal intensity. The specific procedure is as follows: Core samples were taken from the target layer of mudstone and shale, and the core samples were cut into core columns of a predetermined size using wire cutting. The core column is subjected to oil washing treatment; wherein, the core column is placed in an oil washing instrument and washed with a mixed solution of dichloromethane and acetone under a first preset temperature and pressure condition to remove residual hydrocarbons from the core column; After washing the oil, the core column was placed in a drying oven and dried under the second preset temperature and pressure conditions to remove residual water from the core column. The dried core column was weighed and recorded, and a one-dimensional nuclear magnetic resonance experiment was carried out. The dried core column after the one-dimensional nuclear magnetic resonance experiment was placed in a vacuum pressure saturator. A vacuum pump was used to evacuate the core column. The core column was saturated with n-dodecane under the third preset temperature and pressure conditions. After saturation, the core column was allowed to stand in the n-dodecane solution. The oil on the surface of the core column was wiped dry, weighed and recorded. The one-dimensional nuclear magnetic resonance experiment was performed on the saturated oil sample. After completing the one-dimensional nuclear magnetic resonance (NMR) experiment, the saturated oil core sample was centrifuged under the fourth temperature and pressure conditions. The sample was weighed and recorded after each centrifugation under different rotation speeds, and the one-dimensional NMR test was repeated. The residual oil signal quantity under different centrifugal forces was determined by the NMR T2 spectrum, and the signal quantity decay curve was recorded to determine the movable oil signal quantity under each centrifugation state. Using n-dodecane as a standard, the linear relationship between mass and NMR signal intensity was determined to identify the NMR signal intensity.

10. The device for evaluating the mobility of tight oil in shale and mudstone according to claim 8, characterized in that, The indicator analysis module is specifically used for: Based on the adsorption isotherm equation, a fitting relationship between the mobility of tight oil per unit mass and the centrifugal force under different centrifugal forces is constructed. The theoretical maximum mobility of tight oil and the centrifugal median pressure are obtained through linear regression. The ratio of the theoretical maximum mobility of tight oil to the centrifugal median pressure is calculated as the core indicator of mobility.

11. The device for evaluating the mobility of tight oil in shale and mudstone according to claim 10, characterized in that, The method for calculating the mobility of the tight oil is as follows: In the formula, Q m The amount of dense oil mobility corresponding to a unit mass of preset centrifugal force, in mg / g; Q f The mass of dense oil under infinite centrifugal force, expressed in mg / g. ΔP is the centrifugal force, converted according to the centrifuge speed, and the unit is MPa; ΔP L The median centrifugal force is given by the value of Q, where Q is the movable oil volume. f Centrifugal force at half the value, in MPa.

12. The device for evaluating the mobility of tight oil in shale according to claim 8, characterized in that, The data acquisition and processing module is specifically used for: For the well logging data, through data standardization and depth correction, the core experimental data and well logging data are matched one-to-one; wherein, the collected well logging data includes at least porosity, permeability, resistivity, sonic transit time, density, neutron porosity, and natural gamma.

13. The device for evaluating the mobility of tight oil in shale and mudstone according to claim 8, characterized in that, In the multiple linear regression model constructed by the model building module, the formula for the quantitative relationship between the core mobility index and well logging data is as follows: In the formula, As a core indicator of mobility, it represents the mobility oil efficiency under unit centrifugal force; β0 is the intercept term, representing the value when all logging parameters are 0. value; ΔT norm SP norm GR norm These represent the standardized sonic transit time logging curve value, the standardized spontaneous potential logging curve value, and the standardized natural gamma ray logging curve value, respectively; ΔT norm Reflects the acoustic properties and pore structure information of the formation; SP norm Related to the electrochemical properties of formation water and mud filtrate; GR norm Characterizes the content of radioactive elements in strata; β1, β2, and β3 are regression coefficients, representing the acoustic time difference, spontaneous potential, and natural gamma pair, respectively. The degree of contribution; ε is the error term, representing the random error that the model fails to explain.

14. The device for evaluating the mobility of tight oil in shale and mudstone according to claim 8, characterized in that, The mobility evaluation module is specifically used for: Construct a tiered standard for the division of advantageous strata; Based on the classification criteria for favorable strata and the continuous mobility curve, the mobility of tight oil in the entire well section is continuously evaluated.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

17. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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