Shale oil highly-deviated well stress and rock mechanics calculation method and system

Abstract

The invention discloses a shale oil highly-deviated well stress and rock mechanics calculation method and system. According to the method, a conventional logging data set along a shaft is obtained, the conventional logging data set generally comprises data columns such as interval transit time data, resistivity, density data, natural gamma data and neutron porosity data, and the data columns are selected according to corresponding characteristics of regional logging; selecting the same reservoir, and establishing a relationship between natural gamma data and sound wave data; selecting the same reservoir, and establishing a relationship between neutron porosity data and sound wave data; and through the GRAC and the CNLAC obtained in the step 3d, mechanical parameters of a certain depth are obtained through an indoor rock mechanical test, and a weight coefficient is obtained. Through the field test fracturing test, the actually measured minimum horizontal principal stress at the perforation can be obtained, and the weight coefficient can be obtained. Therefore, an alternative formula verified by measured data is obtained, the stress and mechanical parameter profile of the deep fractured highly-deviated well can be obtained more reliably, and the pertinence of fracturing design is improved.

Description

Technical Field

[0001] This invention belongs to the field of oil production engineering technology, specifically a method and system for calculating stress and rock mechanics in highly deviated shale oil wells. Background Technology

[0002] Shale oil has become a new frontier for increasing oil and gas reserves and production in my country, playing a significant role in ensuring national energy security. Among them, the deep fractured shale oil in the Mabei region is characterized by poor matrix properties, well-developed bedding, and abundant natural fractures. Conventional vertical and horizontal well development with fracture control is highly limited, and the effectiveness of such development is questionable. Currently, exploratory tests using highly deviated wells have yielded some positive results. The stress and mechanical property profile along the wellbore is the foundation for fracturing design, enabling further sweet spot evaluation and section cluster division. The calculation of conventional stress and mechanical property profiles requires well logging data, with key control parameters including sonic transit time and resistivity.

[0003] However, due to the influence of well inclination, bedding, and fractures, the acoustic transit time and resistivity data of the Mabei shale oil wells with high deviation have different paths at different well inclinations. This results in a lack of comparability of acoustic transit time data at different depths along the wellbore, causing distortion of stress and mechanical properties along the wellbore. It is impossible to evaluate sweet spots and divide segments. At present, there are no relevant correction methods at home and abroad, which restricts the accurate fracturing design of deep shale oil wells with high deviation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for calculating stress and rock mechanics in highly deviated shale oil wells in order to solve the problems mentioned above.

[0005] The technical solution adopted in this invention is as follows: a method for calculating stress and rock mechanics in highly deviated shale oil wells, the method comprising the following steps:

[0006] S1: Obtain the conventional logging dataset along the wellbore. The conventional logging dataset typically includes data columns such as sonic transit time data, resistivity data, density data, natural gamma data, and neutron porosity data.

[0007] S2: Select data columns. Based on the corresponding characteristics of regional logging, select data columns from the conventional logging dataset.

[0008] S3: Select the same reservoir (i.e., the layer with similar logging characteristics), establish the relationship between natural gamma data and sonic data, and replace sonic data with natural gamma data in the deviated well section to form gamma-sonic transit time data GR_AC;

[0009] S4: Select the same reservoir (i.e., the layer with similar logging characteristics), establish the relationship between neutron porosity data and acoustic data, and replace acoustic data with neutron porosity data in the deviated well section to form pore wave time difference data CNL_AC;

[0010] S5: Using the gamma-ray acoustic transit data GR_AC and pore wave transit data CNL_AC obtained in steps 3d and 4 respectively, mechanical and rock mechanics parameters are calculated according to conventional methods.

[0011] S6: Obtain the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtain the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC;

[0012] S7: By conducting on-site fracturing tests, the measured minimum horizontal principal stress SHmin at the perforation point can be obtained, and the weighting coefficient v in SHmin=v*GR_AC+(1-v)*CNL_AC can be obtained.

[0013] In a preferred embodiment, the data acquisition method in step S1 includes:

[0014] Logging instruments include sonic logging tools, resistivity logging tools, density logging tools, natural gamma logging tools, and neutron logging tools. These instruments can measure along the wellbore to obtain logging data at different depths.

[0015] Well logging curves: Well logging data are represented as curves, with depth on the horizontal axis and the numerical values ​​of logging parameters on the vertical axis. These include sonic transit time curves, resistivity curves, and density curves.

[0016] Well logging software: Well logging data can be read, analyzed, and processed using specialized well logging software.

[0017] Well logging data types include:

[0018] Acoustic transit time (AC): This is one of the most commonly used data for calculating rock mechanical parameters. It reflects the speed at which sound waves propagate through the rock, and can be used to infer parameters such as the rock's elastic modulus and Poisson's ratio.

[0019] Resistivity: Resistivity data can reflect information such as the porosity, permeability and oil saturation of rocks, which is of great significance for evaluating reservoir properties and predicting oil and gas production capacity.

[0020] Density: Density data can reflect information such as lithology, porosity and mineral composition of rocks, and plays an important role in identifying rock strata and calculating rock mechanical parameters.

[0021] Natural gamma data: Natural gamma data can reflect the content of radioactive elements in rocks, which is of great significance for identifying lithology and determining stratigraphic age.

[0022] Neutron porosity data: Neutron porosity data can reflect the porosity of rocks and is of great significance for evaluating reservoir properties and predicting oil and gas production capacity.

[0023] Data quality requirements include:

[0024] Completeness: The well logging dataset should contain all necessary well logging parameters and be large enough to allow for statistical analysis.

[0025] Accuracy: Well logging data should accurately reflect the true properties of the rock and avoid errors caused by instrument malfunction, operational mistakes, etc.

[0026] Consistency: Well logging data should be kept in consistent units and undergo necessary normalization processing to facilitate subsequent calculations and analysis.

[0027] Data acquisition methods include:

[0028] Oilfield companies: You can obtain logging data from oilfield companies, including raw data and processed data.

[0029] Logging service companies: You can obtain logging data from logging service companies, including raw data and processed data.

[0030] Public databases: Well logging data can be obtained from publicly available well logging databases, such as the well logging database of the United States Geological Survey (USGS).

[0031] In a preferred embodiment, step S2, selecting data columns mainly includes eliminating data columns whose response characteristics are affected by well deviation, bedding, and fractures, based on the well logging response characteristics (which can be obtained by those skilled in the art through conventional means). Natural gamma ray curves and neutron porosity curves are selected. Preferably, in step S301, to ensure the consistency of the dimensions of the selected data, the data is normalized to ensure that the data in each column is within the range of 0 to 1, thereby eliminating the influence of dimensions. The relationship between the two is obtained through fitting.

[0032] In a preferred embodiment, step S3 specifically includes:

[0033] First, based on the shape, numerical range, and correlation of the logging curves, the wellbore is divided into multiple reservoirs. The wellbore can be divided into mudstone, sandstone, and shale layers based on the shape of the natural gamma ray curve.

[0034] Next, for each reservoir, a linear regression method was used to establish a relationship model between natural gamma data and acoustic data. For sandstone layers, the following relationship was obtained:

[0035] GR_AC = 0.31 * AC + 0.41

[0036] Wherein, GR_AC is the acoustic time difference data obtained by replacing the acoustic data with natural gamma data, and AC is the original acoustic time difference data.

[0037] Then, the reserved well logging data is used for model validation. This includes using well logging data from a portion of the sandstone layers for model validation, calculating the error between the model's predicted values ​​and the actual values, to ensure the accuracy and reliability of the relational model.

[0038] Finally, the natural gamma data is substituted into the relational model to calculate the corresponding sonic transit time data, which is named GR_AC. For sandstone layers, GR_AC can be calculated using the following formula:

[0039] GR_AC = 0.31 * GR + 0.41

[0040] GR represents natural gamma data.

[0041] By merging GR_AC with the natural gamma data to form a new data column, we can obtain the GR_AC data for subsequent calculations.

[0042] In a preferred embodiment, step S3, which involves using reserved sandstone layer logging data for model verification, specifically includes:

[0043] First, a portion of the logging data from the sandstone formation is selected as reserved data, which is then cleaned and formatted to ensure data integrity and accuracy. Next, the natural gamma ray data from the reserved data is substituted into the model to calculate the corresponding predicted sonic transit time. This predicted value is then compared with the actual sonic transit time data from the reserved data to calculate the error. Error indices such as mean square error (MSE) and mean absolute error (MAE) are calculated to evaluate the model's prediction accuracy. If the model's prediction accuracy meets the requirements, it can be applied to actual logging data to calculate the corresponding predicted sonic transit time, and further analysis and application can be performed. If the model's prediction accuracy does not meet the requirements, the model parameters need to be adjusted or other methods used to establish a relational model, and then re-validated. This approach ensures the accuracy and reliability of the relationship model between natural gamma ray data and sonic data, thereby enabling more accurate calculation of rock mechanics parameters and providing a reliable data foundation for fracturing design.

[0044] In a preferred embodiment, in step S4, to ensure the consistency of the dimensions of the screened data, the data is normalized to ensure that the data in each column ranges between 0 and 1, thereby eliminating the influence of dimensions. The relationship between the two is obtained through fitting, specifically including:

[0045] 1. Data Preparation: Data Selection: Select natural gamma ray and sonic transit time data from sandstone formation logging data. Data Cleaning: Clean the data, removing outliers and erroneous data. Data Normalization: Normalize the natural gamma ray and sonic transit time data, scaling the data range to between 0 and 1. The following formula can be used for normalization:

[0046] X_normalized=(X-X_min) / (X_max-X_min)

[0047] Where X represents the original data, X_min represents the minimum value of the data column, X_max represents the maximum value of the data column, and X_normalized represents the normalized data.

[0048] 2. Linear Regression:

[0049] Model Establishment: A linear regression method was used to establish a relationship model between the normalized values ​​of natural gamma data and the normalized values ​​of acoustic transit time data. The following relationship can be obtained:

[0050] Y_normalized=a*X_normalized+b

[0051] Where Y_normalized is the normalized value of the acoustic time difference data, X_normalized is the normalized value of the natural gamma data, and a and b are regression coefficients.

[0052] 3. Model parameter estimation:

[0053] Calculate the regression coefficients: Use methods such as least squares to calculate the regression coefficients a and b.

[0054] Model evaluation: Evaluating the goodness of fit of the model, for example, calculating the coefficient of determination (R²). 2 ).

[0055] 4. Data denormalization:

[0056] Predicted data: The model is used to predict the normalized values ​​of the acoustic time difference data.

[0057] Data denormalization: The predicted acoustic time difference data is denormalized to the original data range to obtain the predicted acoustic time difference data.

[0058] 5. Results Analysis:

[0059] Comparing predicted and actual values: The predicted acoustic time difference data is compared with the actual acoustic time difference data to analyze the prediction accuracy of the model.

[0060] Model application: The model is applied to actual well logging data to calculate the corresponding predicted values ​​of sonic transit time data.

[0061] Data normalization eliminates the influence of data dimensions, allowing for a more accurate model of the relationship between natural gamma data and acoustic wave data. Normalized data falls between 0 and 1, facilitating statistical analysis methods such as linear regression. Ultimately, this results in a more accurate and reliable model for predicting acoustic wave transit time data and further calculating rock mechanics parameters.

[0062] In a preferred embodiment, in step S5, mechanical and rock mechanics parameters are calculated using Young's modulus. The static Young's modulus measured in the laboratory is YS = 42000 MPa. YS = w*GR_AC + (1-w)*CNL_AC, 42530 = w*44516 + (1-w)*40654, so the weighting coefficient w = -0.35 can be obtained. Thus, YS = 0.485*GR_AC + 0.514*CNL_AC. Similarly, rock mechanics data at various points along the wellbore can be obtained.

[0063] In a preferred embodiment, in step S6, taking Young's modulus as an example, the static Young's modulus measured in indoor rock mechanics is YS, YS=w*GR_AC+(1-w)*CNL_AC, from which the weighting coefficient w can be obtained. Rock mechanics parameters at various points along the wellbore can then be obtained.

[0064] In a preferred embodiment, in step S6, the measured minimum horizontal principal stress at the perforation is obtained as SHmin = 110 MPa, SHmin = v * GR_AC + (1 - v) * CNL_AC, that is, 110 = v * 123 + (1 - v) * 105, so v = 0.277;

[0065] Therefore, SHmin = 0.277 * GR_AC + 0.723 * CNL_AC. Similarly, the stress data at each point along the wellbore can be obtained.

[0066] In a preferred embodiment, in step S7,

[0067] To obtain the weighting coefficient v, field fracturing tests are required, and the measured minimum horizontal principal stress SHmin at the perforation point must be obtained. The specific steps are as follows:

[0068] 1. Field fracturing test:

[0069] Design a fracturing scheme: Based on the geological conditions and development goals, design a fracturing scheme, including perforation parameters, fracturing fluid type, and discharge rate.

[0070] Implement fracturing: Carry out fracturing operations according to the design plan and record parameters such as pressure and displacement during the fracturing process.

[0071] Data collection: Collect data during the fracturing process, including fracturing curves, pressure data, etc.

[0072] 2. Measured minimum horizontal principal stress SHmin at the perforation point:

[0073] Data extraction: Extract pressure data at the perforation point from the fracturing curve.

[0074] Stress calculation: Based on the fracturing curve and fracturing fluid properties, the measured minimum horizontal principal stress SHmin at the perforation point is calculated.

[0075] Data recording: Record the measured minimum horizontal principal stress SHmin at the perforation point for subsequent calculations.

[0076] 3. Calculation of weighting coefficient v:

[0077] Establish the relationship: Establish the relationship between the measured minimum horizontal principal stress SHmin at the perforation point and CNL_AC and GR_AC:

[0078] SHmin = v * CNL_AC + (1 - v) * GR_AC

[0079] Substitute the data: Substitute the CNL_AC and GR_AC data at the perforation point into the above relational expression.

[0080] Calculate the weight coefficient v: The weight coefficient v can be obtained by solving the above relationship.

[0081] 4. Results Analysis:

[0082] Verify the weighting coefficient v: Use the weighting coefficient v to calculate the minimum horizontal principal stress at each point along the wellbore, and compare it with the actual measured data to verify the accuracy of the weighting coefficient v.

[0083] Model application: The weighting coefficient v is applied to the actual logging data to calculate the minimum horizontal principal stress at each point along the wellbore.

[0084] 5. Examples:

[0085] Assuming CNL_AC at the perforation point is 0.4, GR_AC is 0.6, and the measured minimum horizontal principal stress SHmin is 100 MPa, substituting these values ​​into the above equation, we can obtain:

[0086] 100MPa = v * 0.4 + (1 - v) * 0.6

[0087] Solving for v, we get v = 0.5.

[0088] This means that when calculating the minimum horizontal principal stress at each point along the wellbore, the weights of CNL_AC and GR_AC are 0.5, respectively.

[0089] By conducting field fracturing tests, the measured minimum horizontal principal stress SHmin at the perforation point can be obtained, and the weighting coefficient v can be calculated. Applying the weighting coefficient v to actual logging data can more accurately calculate the minimum horizontal principal stress at various points along the wellbore, providing a reliable data basis for fracturing design.

[0090] In another embodiment of the present invention, a shale oil high-angle well stress and rock mechanics calculation system includes a data acquisition module, a data preprocessing module, a gamma data processing module, a porosity data processing module, a mechanical parameter calculation module, and a weighting coefficient calculation module.

[0091] The data acquisition module acquires conventional logging datasets along the wellbore. The conventional logging datasets include data columns of sonic transit time, resistivity, density, natural gamma ray, and neutron porosity.

[0092] The data preprocessing module selects data columns based on the corresponding characteristics of regional well logging.

[0093] The gamma data processing module establishes the relationship between natural gamma data and acoustic data in the same reservoir, and replaces acoustic data with natural gamma data in the deviated well section to form GR_AC;

[0094] The porosity data processing module establishes a relationship between neutron porosity data and acoustic data in the same reservoir, and replaces acoustic data with neutron porosity data in the deviated well section to form CNL_AC;

[0095] The mechanical parameter calculation module obtains GR_AC and CNL_AC and performs rock mechanical parameter calculations.

[0096] The weighting coefficient calculation module obtains the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtains the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC; through field fracturing tests, it can obtain the measured minimum horizontal principal stress SHmin at the perforation, and obtain the weighting coefficient v in v*GR_AC+(1-v)*CNL_AC.

[0097] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0098] This invention utilizes natural gamma ray and neutron porosity data to replace acoustic transit time data in deviated well sections, and combines this with measured data obtained from laboratory rock mechanics tests and field fracturing tests. This effectively eliminates the influence of well deviation, bedding, and fractures on acoustic data, thereby more accurately calculating rock mechanics parameters and providing a reliable data foundation for fracturing design. This helps optimize fracturing schemes, improve fracturing effects, and ultimately increase oil and gas production. Furthermore, this method does not require additional logging projects; it only needs to use conventional logging data for calculation, thus reducing logging data acquisition costs. Simultaneously, this method has a fast calculation speed, quickly obtaining stress and rock mechanics parameters, thereby shortening the fracturing design cycle and improving fracturing design efficiency.

[0099] 2. In this invention, more accurate stress and rock mechanics parameters help assess fracturing risks and take corresponding preventative measures, thereby reducing fracturing risks and improving fracturing success rates. Therefore, this calculation method not only improves the accuracy of stress and rock mechanics parameter calculations and reduces fracturing design costs, but also increases efficiency, providing important technical support for the development of highly deviated shale oil wells. It has significant economic and social benefits, and can more realistically reflect the stress and mechanical profiles of deeply fractured shale oil wells. Measured data are obtained through both indoor and field testing, and weights are derived from the measured data, resulting in more reliable optimized data. Attached Figure Description

[0100] Figure 1 Optimize the flowchart for the calculation method;

[0101] Figure 2 The image shows a comparison of the calculation results before and after optimization in this invention. The left image shows the result after correction, and the right image shows the result using the conventional method. Detailed Implementation

[0102] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0103] Reference Figure 1-2 ,

[0104] This invention provides a method for calculating stress and rock mechanics in highly deviated shale oil wells, the method comprising the following steps:

[0105] S1: Obtain the conventional logging dataset along the wellbore. The conventional logging dataset typically includes data columns such as sonic transit time data, resistivity data, density data, natural gamma data, and neutron porosity data.

[0106] S2: Select data columns based on the corresponding characteristics of regional well logging;

[0107] S3: Select the same reservoir (i.e., the layer with similar logging characteristics), establish the relationship between natural gamma data and sonic data, and replace sonic data with natural gamma data in the deviated section to form GR_AC;

[0108] S4: Select the same reservoir (i.e., the layer with similar logging characteristics), establish the relationship between neutron porosity data and acoustic data, and replace the acoustic data with neutron porosity data in the deviated well section to form CNL_AC;

[0109] S5: Calculate the mechanical and rock mechanics parameters of GR_AC and CNL_AC obtained in steps 3d and 4 respectively, according to conventional methods.

[0110] S6: Obtain the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtain the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC;

[0111] S7: By conducting on-site fracturing tests, the measured minimum horizontal principal stress SHmin at the perforation point can be obtained, and the weighting coefficient v in v*GR_AC+(1-v)*CNL_AC can be obtained.

[0112] In step S1, the data acquisition methods include:

[0113] Logging instruments include sonic logging tools, resistivity logging tools, density logging tools, natural gamma logging tools, and neutron logging tools. These instruments can measure along the wellbore to obtain logging data at different depths.

[0114] Well logging curves: Well logging data are represented as curves, with depth on the horizontal axis and the numerical values ​​of logging parameters on the vertical axis. These include sonic transit time curves, resistivity curves, and density curves.

[0115] Well logging software: Well logging data can be read, analyzed, and processed using specialized well logging software.

[0116] Well logging data types include:

[0117] Acoustic transit time (AC): This is one of the most commonly used data for calculating rock mechanical parameters. It reflects the speed at which sound waves propagate through the rock, and can be used to infer parameters such as the rock's elastic modulus and Poisson's ratio.

[0118] Resistivity: Resistivity data can reflect information such as the porosity, permeability and oil saturation of rocks, which is of great significance for evaluating reservoir properties and predicting oil and gas production capacity.

[0119] Density: Density data can reflect information such as lithology, porosity and mineral composition of rocks, and plays an important role in identifying rock strata and calculating rock mechanical parameters.

[0120] Natural gamma data: Natural gamma data can reflect the content of radioactive elements in rocks, which is of great significance for identifying lithology and determining stratigraphic age.

[0121] Neutron porosity data: Neutron porosity data can reflect the porosity of rocks and is of great significance for evaluating reservoir properties and predicting oil and gas production capacity.

[0122] Data quality requirements include:

[0123] Completeness: The well logging dataset should contain all necessary well logging parameters and be large enough to allow for statistical analysis.

[0124] Accuracy: Well logging data should accurately reflect the true properties of the rock and avoid errors caused by instrument malfunction, operational mistakes, etc.

[0125] Consistency: Well logging data should be kept in consistent units and undergo necessary normalization processing to facilitate subsequent calculations and analysis.

[0126] Data acquisition methods include:

[0127] Oilfield companies: You can obtain logging data from oilfield companies, including raw data and processed data.

[0128] Logging service companies: You can obtain logging data from logging service companies, including raw data and processed data.

[0129] Public databases: Well logging data can be obtained from publicly available well logging databases, such as the well logging database of the United States Geological Survey (USGS).

[0130] In step S2, the selection of data columns mainly includes eliminating data columns whose response characteristics are affected by well deviation, bedding, and fractures, based on the well logging response characteristics (which can be obtained by those skilled in the art through conventional means). Natural gamma ray curves and neutron porosity curves are selected. Preferably, in step S301, to ensure the consistency of the dimensions of the selected data, the data is normalized to ensure that the data in each column ranges between 0 and 1, thereby eliminating the influence of dimensions. The relationship between the two is obtained through fitting.

[0131] Step S2 specifically includes:

[0132] Feature Analysis: First, a detailed feature analysis is performed on the logging data within the region. This includes identifying the logging response characteristics of different lithologies, fluid types, and geological structures (such as bedding, fractures, faults, etc.). These feature analyses will help technicians determine which data series are most critical for the calculation of rock mechanics parameters.

[0133] Data removal: When removing data columns affected by well deviation, bedding, or fractures, specific operations may include:

[0134] Well inclination data is used to determine the reliability of parameters such as acoustic transit time, and data measured when the well inclination is large are discarded.

[0135] Analyze abnormal changes in the natural gamma curve to eliminate data bias caused by radioactive element enrichment due to bedding or cracks.

[0136] Examine the abnormal fluctuations in the neutron porosity curve to rule out data anomalies caused by cracks or wellbore irregularities.

[0137] Data correction: For data columns that are less affected, perform necessary corrections, such as:

[0138] Environmental corrections were applied to the acoustic transit time data to eliminate the effects of factors such as mud soaking and wellbore irregularities.

[0139] Temperature and mud filtrate corrections were applied to the resistivity data to improve their accuracy.

[0140] Data filtering: In the filtering process, in addition to natural gamma curves and neutron porosity curves, the following data series can also be considered:

[0141] Density curve: used to assess the density and porosity of rocks, but density anomalies caused by wellbore collapse or cracks must be excluded.

[0142] Sonic amplitude curve: It may be used to identify fracture zones, but care should be taken to eliminate artifacts caused by well wall reflections during screening.

[0143] Data normalization: After data removal and correction, the remaining data columns are normalized to ensure that all data are within the same dimension, which facilitates subsequent model building and calculation.

[0144] Quality control: Finally, quality control is performed on the screened and preprocessed data, including cross-validation and repeated measures validation, to ensure the accuracy and reliability of the data.

[0145] With these supplementary details, the data selection in step S2 will be more comprehensive and accurate, laying a solid foundation for subsequent calculations of rock mechanics parameters.

[0146] Step S3 specifically includes:

[0147] First, based on the shape, numerical range, and correlation of the logging curves, the wellbore is divided into multiple reservoirs. The wellbore can be divided into mudstone, sandstone, and shale layers based on the shape of the natural gamma ray curve.

[0148] Next, for each reservoir, a linear regression method was used to establish a relationship model between natural gamma data and acoustic data. For sandstone layers, the following relationship was obtained:

[0149] GR_AC = 0.31 * AC + 0.41

[0150] Wherein, GR_AC is the acoustic time difference data obtained by replacing the acoustic data with natural gamma data, and AC is the original acoustic time difference data.

[0151] Then, the reserved well logging data is used for model validation. This includes using well logging data from a portion of the sandstone layers for model validation, calculating the error between the model's predicted values ​​and the actual values, to ensure the accuracy and reliability of the relational model.

[0152] Finally, the natural gamma data is substituted into the relational model to calculate the corresponding sonic transit time data, which is named GR_AC. For sandstone layers, GR_AC can be calculated using the following formula:

[0153] GR_AC = 0.31 * GR + 0.41

[0154] GR represents natural gamma data.

[0155] By merging GR_AC with the natural gamma data to form a new data column, we can obtain the GR_AC data for subsequent calculations.

[0156] Step S3, which uses reserved sandstone layer logging data for model verification, specifically includes:

[0157] First, a portion of the logging data from the sandstone formation is selected as reserved data, which is then cleaned and formatted to ensure data integrity and accuracy. Next, the natural gamma ray data from the reserved data is substituted into the model to calculate the corresponding predicted sonic transit time. This predicted value is then compared with the actual sonic transit time data from the reserved data to calculate the error. Error indices such as mean square error (MSE) and mean absolute error (MAE) are calculated to evaluate the model's prediction accuracy. If the model's prediction accuracy meets the requirements, it can be applied to actual logging data to calculate the corresponding predicted sonic transit time, and further analysis and application can be performed. If the model's prediction accuracy does not meet the requirements, the model parameters need to be adjusted or other methods used to establish a relational model, and then re-validated. This approach ensures the accuracy and reliability of the relationship model between natural gamma ray data and sonic data, thereby enabling more accurate calculation of rock mechanics parameters and providing a reliable data foundation for fracturing design.

[0158] In step S4, to ensure the consistency of the dimensions of the selected data, the data is normalized to ensure that the data in each column ranges between 0 and 1, thus eliminating the influence of dimensions. The relationship between the two is obtained through fitting, specifically including:

[0159] 1. Data Preparation: Data Selection: Select natural gamma ray and sonic transit time data from sandstone formation logging data. Data Cleaning: Clean the data, removing outliers and erroneous data. Data Normalization: Normalize the natural gamma ray and sonic transit time data, scaling the data range to between 0 and 1. The following formula can be used for normalization:

[0160] X_normalized=(X-X_min) / (X_max-X_min)

[0161] Where X represents the original data, X_min represents the minimum value of the data column, X_max represents the maximum value of the data column, and X_normalized represents the normalized data.

[0162] 2. Linear Regression:

[0163] Model Establishment: A linear regression method was used to establish a relationship model between the normalized values ​​of natural gamma data and the normalized values ​​of acoustic transit time data. The following relationship can be obtained:

[0164] Y_normalized=a*X_normalized+b

[0165] Where Y_normalized is the normalized value of the acoustic time difference data, X_normalized is the normalized value of the natural gamma data, and a and b are regression coefficients.

[0166] 3. Model parameter estimation:

[0167] Calculate the regression coefficients: Use methods such as least squares to calculate the regression coefficients a and b.

[0168] Model evaluation: Evaluating the goodness of fit of the model, for example, calculating the coefficient of determination (R²). 2 ).

[0169] 4. Data denormalization:

[0170] Predicted data: The model is used to predict the normalized values ​​of the acoustic time difference data.

[0171] Data denormalization: The predicted acoustic time difference data is denormalized to the original data range to obtain the predicted acoustic time difference data.

[0172] 5. Results Analysis:

[0173] Comparing predicted and actual values: The predicted acoustic time difference data is compared with the actual acoustic time difference data to analyze the prediction accuracy of the model.

[0174] Model application: The model is applied to actual well logging data to calculate the corresponding predicted values ​​of sonic transit time data.

[0175] Data normalization eliminates the influence of data dimensions, allowing for a more accurate model of the relationship between natural gamma data and acoustic wave data. Normalized data falls between 0 and 1, facilitating statistical analysis methods such as linear regression. Ultimately, this results in a more accurate and reliable model for predicting acoustic wave transit time data and further calculating rock mechanics parameters.

[0176] In step S5, mechanical and rock mechanics parameters are calculated using Young's modulus. The static Young's modulus measured in the laboratory is YS = 42000 MPa. YS = w*GR_AC + (1-w)*CNL_AC, 42530 = w*44516 + (1-w)*40654. The weighting coefficient w = -0.35 can be obtained, and YS = 0.485*GR_AC + 0.514*CNL_AC can be obtained. Similarly, rock mechanics data at various points along the wellbore can be obtained.

[0177] In step S6, taking Young's modulus as an example, the static Young's modulus measured in the laboratory rock mechanics is YS, YS=w*GR_AC+(1-w)*CNL_AC, from which the weighting coefficient w can be obtained. Rock mechanics parameters at each point along the wellbore can then be obtained.

[0178] In step S6, the measured minimum horizontal principal stress at the perforation point is obtained as SHmin = 110 MPa. SHmin = v * GR_AC + (1 - v) * CNL_AC, that is, 110 = v * 123 + (1 - v) * 105, so v = 0.277.

[0179] Therefore, SHmin = 0.277 * GR_AC + 0.723 * CNL_AC. Similarly, the stress data at each point along the wellbore can be obtained.

[0180] In step S7,

[0181] To obtain the weighting coefficient v, field fracturing tests are required, and the measured minimum horizontal principal stress SHmin at the perforation point must be obtained. The specific steps are as follows:

[0182] 1. Field fracturing test:

[0183] Design a fracturing scheme: Based on the geological conditions and development goals, design a fracturing scheme, including perforation parameters, fracturing fluid type, and discharge rate.

[0184] Implement fracturing: Carry out fracturing operations according to the design plan and record parameters such as pressure and displacement during the fracturing process.

[0185] Data collection: Collect data during the fracturing process, including fracturing curves, pressure data, etc.

[0186] 2. Measured minimum horizontal principal stress SHmin at the perforation point:

[0187] Data extraction: Extract pressure data at the perforation point from the fracturing curve.

[0188] Stress calculation: Based on the fracturing curve and fracturing fluid properties, the measured minimum horizontal principal stress SHmin at the perforation point is calculated.

[0189] Data recording: Record the measured minimum horizontal principal stress SHmin at the perforation point for subsequent calculations.

[0190] 3. Calculation of weighting coefficient v:

[0191] Establish the relationship: Establish the relationship between the measured minimum horizontal principal stress SHmin at the perforation point and CNL_AC and GR_AC:

[0192] SHmin = v * CNL_AC + (1 - v) * GR_AC

[0193] Substitute the data: Substitute the CNL_AC and GR_AC data at the perforation point into the above relational expression.

[0194] Calculate the weight coefficient v: The weight coefficient v can be obtained by solving the above relationship.

[0195] 4. Results Analysis:

[0196] Verify the weighting coefficient v: Use the weighting coefficient v to calculate the minimum horizontal principal stress at each point along the wellbore, and compare it with the actual measured data to verify the accuracy of the weighting coefficient v.

[0197] Model application: The weighting coefficient v is applied to the actual logging data to calculate the minimum horizontal principal stress at each point along the wellbore.

[0198] 5. Examples:

[0199] Assuming CNL_AC at the perforation point is 0.4, GR_AC is 0.6, and the measured minimum horizontal principal stress SHmin is 100 MPa, substituting these values ​​into the above equation, we can obtain:

[0200] 100MPa = v * 0.4 + (1 - v) * 0.6

[0201] Solving for v, we get v = 0.5.

[0202] This means that when calculating the minimum horizontal principal stress at each point along the wellbore, the weights of CNL_AC and GR_AC are 0.5, respectively.

[0203] By conducting field fracturing tests, the measured minimum horizontal principal stress SHmin at the perforation point can be obtained, and the weighting coefficient v can be calculated. Applying the weighting coefficient v to actual logging data can more accurately calculate the minimum horizontal principal stress at various points along the wellbore, providing a reliable data basis for fracturing design.

[0204] In this application, the weighting coefficient v is a parameter used to adjust the degree to which natural gamma data (GR) and neutron porosity data (CNL) substitute for acoustic transit time data (AC) when AC is affected by factors such as well deviation. This coefficient reflects the relative importance of GR_AC and CNL_AC in the synthesized acoustic transit time data;

[0205] Correlation with Stress in Deviated Wells: In deviated wells, due to variations in the wellbore trajectory, sonic transit time data may be affected by various factors, thus failing to accurately reflect the actual rock conditions. By adjusting the weighting coefficient v, sonic transit time under deviated well conditions can be estimated more accurately, and subsequently used to calculate the stress state of the rock. The measured minimum horizontal principal stress SHmin is a key parameter in fracturing design, helping to determine the stress field of the rock. Determining the weighting coefficient v helps improve the accuracy of sonic transit time data, thereby enabling more accurate calculation of the stress field.

[0206] Relevance to Rock Mechanics: The calculation of rock mechanics parameters relies on accurate sonic transit time data. By optimizing the weighting coefficient v, more reliable sonic transit time data can be obtained, which can then be used to calculate rock mechanics parameters such as elastic modulus and compressive strength. These parameters are crucial for evaluating rock fracture pressure, fracturing effectiveness, and wellbore stability.

[0207] This invention utilizes natural gamma ray and neutron porosity data to replace acoustic transit time data in deviated well sections, and combines this with measured data obtained from laboratory rock mechanics tests and field fracturing tests. This effectively eliminates the influence of well deviation, bedding, and fractures on acoustic data, thereby more accurately calculating rock mechanics parameters and providing a reliable data foundation for fracturing design. This helps optimize fracturing schemes, improve fracturing effects, and ultimately increase oil and gas production. Furthermore, this method does not require additional logging projects; it only needs to use conventional logging data for calculation, thus reducing logging data acquisition costs. Simultaneously, this method has a fast calculation speed, quickly obtaining stress and rock mechanics parameters, thereby shortening the fracturing design cycle and improving fracturing design efficiency.

[0208] In this invention, more accurate stress and rock mechanics parameters help assess fracturing risks and take corresponding preventative measures, thereby reducing fracturing risks and improving fracturing success rates. Therefore, this calculation method not only improves the accuracy of stress and rock mechanics parameter calculations and reduces fracturing design costs, but also increases efficiency, providing crucial technical support for the development of highly deviated shale oil wells. It has significant economic and social benefits, and can more realistically reflect the stress and mechanical profiles of deeply fractured, highly deviated shale oil wells. Measured data are obtained through both indoor and field testing, and weights are derived from the measured data to optimize the data, making the resulting data more reliable.

[0209] This application addresses the technical challenge that existing methods cannot fully resolve the issue of sonic transit time data from deep shale oil wells with high deviation, which is affected by bedding, fractures, and well deviation, and thus cannot reflect the mechanical properties along the wellbore. It enables the evaluation of stress and mechanical properties along the wellbore in deep fractured, high-deviation wells, providing a data foundation for fracturing design and improved fracturing efficiency.

[0210] In another embodiment of the present invention, a stress and rock mechanics calculation system for highly deviated shale oil wells is provided, including a data acquisition module, a data preprocessing module, a gamma data processing module, a porosity data processing module, a mechanical parameter calculation module, and a weighting coefficient calculation module.

[0211] The data acquisition module acquires conventional logging datasets along the wellbore. The conventional logging datasets include data columns of sonic transit time, resistivity, density, natural gamma ray, and neutron porosity.

[0212] The data preprocessing module selects data columns based on the corresponding characteristics of regional well logging.

[0213] The gamma data processing module establishes the relationship between natural gamma data and acoustic data in the same reservoir, and replaces acoustic data with natural gamma data in the deviated well section to form GR_AC;

[0214] The porosity data processing module establishes a relationship between neutron porosity data and acoustic data in the same reservoir, and replaces acoustic data with neutron porosity data in the deviated well section to form CNL_AC;

[0215] The mechanical parameter calculation module obtains GR_AC and CNL_AC and performs rock mechanical parameter calculations.

[0216] The weighting coefficient calculation module obtains the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtains the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC; through field fracturing tests, it can obtain the measured minimum horizontal principal stress SHmin at the perforation, and obtain the weighting coefficient v in v*GR_AC+(1-v)*CNL_AC.

[0217] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0218] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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.

Claims

1. A method for calculating stress and rock mechanics in highly deviated shale oil wells, characterized in that: The method includes the following steps: S1: Obtain the conventional logging dataset along the wellbore. The conventional logging dataset includes data columns of sonic transit time data, resistivity data, density data, natural gamma data, and neutron porosity data. S2: Select data columns: Select data columns from the conventional logging dataset based on the corresponding characteristics of the regional logging; S3: Select the same reservoir, establish the relationship between natural gamma data and acoustic data, and replace acoustic data with natural gamma data in the deviated well section to form gamma-acoustic transit time data GR_AC; S4: Select the same reservoir, establish the relationship between neutron porosity data and acoustic data, and replace acoustic data with neutron porosity data in the deviated well section to form pore wave time difference data CNL_AC; S5: Calculate rock mechanical parameters using the gamma-ray sonic transit data GR_AC and pore wave transit data CNL_AC obtained from S3 and S4 respectively; S6: Obtain the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtain the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC; S7: The weighting coefficient v in the measured minimum horizontal principal stress SHmin = v*GR_AC + (1-v)*CNL_AC at the perforation point is obtained through field fracturing tests.

2. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: In step S1, the data acquisition methods include: Logging instruments are used to measure along the wellbore to obtain logging data at different depths; Well logging data is represented in the form of curves, with the horizontal axis representing depth and the vertical axis representing the values ​​of well logging parameters; including sonic transit time curves, resistivity curves, and density curves.

3. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: In step S2, the selected data series include, based on the logging response characteristics, eliminating data series whose response characteristics are affected by well deviation, bedding, and fractures; and selecting natural gamma curves and neutron porosity curves.

4. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: Step S3 specifically includes: Based on the shape, numerical range, and correlation characteristics of the logging curves, the wellbore is divided into multiple reservoirs; based on the shape of the natural gamma curve, the wellbore is divided into mudstone, sandstone, and shale layers. For each reservoir, a linear regression method was used to establish a relationship model between natural gamma data and acoustic data; for sandstone layers, the following relationship was obtained: GR_AC = 0.31 * AC + 0.41; Wherein, GR_AC is the acoustic time difference data obtained by replacing the acoustic data with natural gamma data, and AC is the original acoustic time difference data; Model validation is performed using reserved well logging data; this includes using well logging data from some sandstone layers to validate the model and calculate the error between the model's predicted values ​​and the actual values. Substituting natural gamma data into the relational model, the corresponding acoustic transit time data is calculated and named GR_AC; for sandstone layers, GR_AC is calculated using the following formula: GR_AC = 0.31 * GR + 0.41; Where GR represents natural gamma data; By merging GR_AC with the natural gamma data to form a new data column, we can obtain the GR_AC data for subsequent calculations.

5. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 4, characterized in that: In step S3, the model verification using reserved sandstone layer logging data specifically includes: A portion of the logging data from the sandstone formation is selected as reserved data, which is then cleaned and formatted to ensure data integrity and accuracy. Next, the natural gamma data from the reserved data is substituted into the model to calculate the corresponding predicted sonic transit time (SRT) data. This predicted value is then compared with the actual SRT data from the reserved data to calculate the error. The model's prediction accuracy is evaluated by calculating the mean square error and mean absolute error. If the model's prediction accuracy meets the requirements, it is applied to the actual logging data to calculate the corresponding predicted SRT data, and further analysis and application are performed. If the model's prediction accuracy does not meet the requirements, the model parameters need to be adjusted or other methods are used to establish a relational model, and then re-verification is required.

6. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: In step S4, to ensure the consistency of the dimensions of the screened data, the neutron porosity data and acoustic data are normalized to ensure that the data in each column are within the range of 0 to 1, so as to eliminate the influence of dimensions; the relationship between the neutron porosity data and the acoustic data is obtained by fitting.

7. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: In step S5, mechanical and rock mechanics parameters are calculated using Young's modulus. The static Young's modulus measured in the laboratory is YS = 42000MPa. YS = w*GR_AC + (1-w)*CNL_AC, 42530 = w*44516 + (1-w)*40654. The weighting coefficient w = -0.35 is obtained, and YS = 0.485*GR_AC + 0.514*CNL_AC.

8. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 1, characterized in that: In step S6, the static Young's modulus measured in the laboratory rock mechanics is YS, YS=w*GR_AC+(1-w)*CNL_AC, and the weighting coefficient w is obtained; the rock mechanics parameters at each point along the wellbore are calculated.

9. The method for calculating stress and rock mechanics in highly deviated shale oil wells as described in claim 8, characterized in that: In step S6, the measured minimum horizontal principal stress at the perforation point is obtained as SHmin = 110 MPa. SHmin = v * GR_AC + (1 - v) * CNL_AC, that is, 110 = v * 123 + (1 - v) * 105, so v = 0.

277. SHmin=0.277*GR_AC+0.723*CNL_AC.

10. A system for calculating stress and rock mechanics in highly deviated shale oil wells, characterized in that: It includes a data acquisition module, a data preprocessing module, a gamma data processing module, a porosity data processing module, a mechanical parameter calculation module, and a weighting coefficient calculation module; The data acquisition module acquires conventional logging datasets along the wellbore. The conventional logging datasets include data columns of sonic transit time, resistivity, density, natural gamma ray, and neutron porosity. The data preprocessing module selects data columns based on the corresponding characteristics of regional well logging. The gamma data processing module establishes the relationship between natural gamma data and acoustic data in the same reservoir, and replaces acoustic data with natural gamma data in the deviated well section to form GR_AC; The porosity data processing module establishes a relationship between neutron porosity data and acoustic data in the same reservoir, and replaces acoustic data with neutron porosity data in the deviated well section to form CNL_AC; The mechanical parameter calculation module obtains GR_AC and CNL_AC and performs rock mechanical parameter calculations. The weighting coefficient calculation module obtains the static Young's modulus and Poisson's ratio at a certain depth through indoor rock mechanics tests, and obtains the weighting coefficient w in w*GR_AC+(1-w)*CNL_AC; By conducting on-site fracturing tests, the measured minimum horizontal principal stress SHmin at the perforation point can be obtained, and the weighting coefficient v in v*GR_AC+(1-v)*CNL_AC can be obtained.

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