A prediction method for the minimum horizontal principal stress of horizontal wells with multi-information fusion

Abstract

A method for predicting the minimum horizontal principal stress of horizontal wells with multi-information fusion, which relates to the field of shale gas development. The method for predicting the minimum horizontal principal stress of horizontal wells with multi-information fusion is to collect logging data of horizontal wells, horizontal well trajectory data, surface elevation data and the fracturing construction pressure curves of each fracturing stage; calculate the bottom-hole fracture closure pressure of each fracturing stage; calculate the average value of the logging curves of each fracturing stage of the horizontal well using the logging data of the horizontal well, and calculate the average buried depth of each fracturing stage of the horizontal well using the well trajectory curve and the surface elevation data; establish a model to calculate the low-frequency and high-frequency components of the predicted minimum horizontal principal stress; add the low-frequency component of the predicted minimum horizontal principal stress to the high-frequency component of the predicted minimum horizontal principal stress to obtain the minimum horizontal principal stress of the horizontal well. The method for predicting the minimum horizontal principal stress of horizontal wells with multi-information fusion can use logging data and fracturing data to predict the minimum horizontal principal stress of horizontal wells.

Description

Technical Field

[0001] The present application relates to the field of shale gas exploitation, and more particularly, to a method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion. Background Art

[0002] In the process of unconventional shale gas development, the prediction and calculation of the minimum horizontal principal stress of a shale horizontal well are very important, which directly affects the design of the fracturing stage plan and the fracturing process parameters of the shale horizontal well. At present, the existing prediction and calculation of the minimum horizontal principal stress of a shale horizontal well mainly calculate the stress through dipole acoustic logging data. In the calculation process, data such as Young's modulus and Poisson's ratio need to be calculated through the longitudinal and transverse wave velocities and density, and the deformation coefficient is calibrated by the measured stress value or the regional empirical deformation coefficient is adopted.

[0003] However, the existing stress calculation methods for shale gas horizontal wells have the following defects: First, dipole acoustic logging data are required. Due to cost reasons, there is not much dipole acoustic logging data collected at present, and more are conventional logging data, resulting in difficult calculation; Second, correction of the longitudinal and transverse wave logging data of the horizontal well is required, and accurate correction is difficult; Third, there are challenges in obtaining the deformation coefficient, lacking calibration of the measured stress value, or the regional empirical deformation coefficient is difficult to reflect the local changes of the local deformation coefficient. Summary of the Invention

[0004] The purpose of the present application is to provide a method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion, which can predict the minimum horizontal principal stress of a horizontal well by using the conventional data of the horizontal well, avoid the problems of lack of dipole shear wave data, obtaining the deformation coefficient, and correction of the longitudinal and transverse wave logging data of the horizontal well during the stress calculation process, and effectively improve the prediction accuracy.

[0005] The embodiments of the present application are implemented as follows:

[0006] The embodiments of the present application provide a method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion, including the following steps:

[0007] Collect data; collect the logging data of the horizontal well, the horizontal well trajectory data, the surface elevation data of the work area, and the fracturing construction pressure curves of each fracturing stage of the horizontal well;

[0008] Calculate the bottom-hole fracture closure pressure of each fracturing stage of the horizontal well by using the collected data;

[0009] Calculate the average value of the logging curves of each fracturing stage of the horizontal well by using the logging data of the horizontal well, and calculate the average buried depth of each fracturing stage by using the well trajectory curve and the surface elevation data of the work area;

[0010] A model is established to calculate the mid-low frequency components of the predicted minimum horizontal principal stress, and the high-frequency components of the predicted minimum horizontal principal stress are calculated;

[0011] The mid-low frequency components of the predicted minimum horizontal principal stress and the high-frequency components of the predicted minimum horizontal principal stress are combined to obtain the minimum horizontal principal stress of the horizontal well.

[0012] In some alternative embodiments, when calculating the closure pressure of each fracturing stage of a horizontal well using the collected data, first, a pressure drop curve analysis is performed on the fracturing construction pressure curve of each fracturing stage, and the G-function method is used to obtain the closure pressure of each fracturing stage to get the wellhead closure pressure. The average vertical depth of each fracturing stage is obtained by using the minimum curvature method with the horizontal well trajectory data, and the hydrostatic pressure is calculated through the average vertical depth. The wellhead closure pressure and the corresponding hydrostatic pressure are added together to obtain the bottom-hole fracture closure pressure.

[0013] In some alternative embodiments, when calculating the average formation depth of each fracturing stage of a horizontal well using the well trajectory curve and the surface elevation data of the work area, the average formation depth of each fracturing stage is calculated by adding the average vertical depth of each fracturing stage to the difference between the surface elevation at each fracturing stage of the horizontal well and the surface elevation at the wellhead.

[0014] In some alternative embodiments, when establishing a model to predict the mid-low frequency components of the minimum horizontal principal stress, taking the average value of the logging curves of each fracturing stage of the horizontal well and the average formation depth of each fracturing stage as variables, and the bottom-hole fracture closure pressure of each fracturing stage as the target value, a training and learning data set is established to carry out the minimum horizontal principal stress prediction learning, and the Butterworth low-pass filtering method is used to filter and obtain the mid-low frequency components of the predicted minimum horizontal principal stress.

[0015] In some alternative embodiments, the acoustic travel-time curve in the horizontal well logging data is filtered to obtain the high-frequency components of the predicted minimum horizontal principal stress.

[0016] In some alternative embodiments, the collected horizontal well logging data includes acoustic travel-time, resistivity, natural gamma ray GR, and compensated neutron CNL; the horizontal well trajectory data includes the wellhead coordinates, kelly bushing elevation, and the depth, azimuth, and inclination of the well deviation data.

[0017] In some alternative embodiments, when calculating the average value of the resistivity logging curves of each fracturing stage of a horizontal well using the horizontal well logging data, the resistivity is taken logarithmically and then the average value of each fracturing stage is calculated.

[0018] In some alternative embodiments, when calculating the closure pressure of each fracturing stage, when the fracturing construction of a fracturing stage is abnormal and the calculated closure pressure value is abnormal, the abnormal closure pressure value of the abnormal fracturing stage is filtered out.

[0019] The beneficial effects of the present application are as follows: The method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion provided by the present application includes the following steps: collecting logging data of the horizontal well, horizontal well trajectory data, surface elevation data of the work area, and fracture construction pressure curves of each fracturing section of the horizontal well; calculating the bottom-hole fracture closure pressure of each fracturing section of the horizontal well using the collected data; calculating the average value of the logging curves of each fracturing section of the horizontal well using the horizontal well logging data, and calculating the average formation burial depth using the horizontal well trajectory data and surface elevation data of the work area; establishing a model to calculate the low- and medium-frequency components of the predicted minimum horizontal principal stress, and calculating the high-frequency components of the predicted minimum horizontal principal stress; obtaining the minimum horizontal principal stress of the horizontal well by combining the low- and medium-frequency components of the predicted minimum horizontal principal stress with the high-frequency components of the predicted minimum horizontal principal stress. The method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion provided by the present application can predict the minimum horizontal principal stress of a horizontal well using conventional horizontal well data, effectively improving the prediction accuracy. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 Schematic diagram for calculating the minimum horizontal principal stress of a horizontal well using the method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion provided by the embodiments of the present application; Detailed Embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application to be protected, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0024] The features and performance of the method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion of the present application will be further described in detail below in conjunction with the embodiments.

[0025] As Figure 1 shown, an embodiment of the present application provides a method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion, including the following steps:

[0026] Collect data; collect logging data of horizontal wells, horizontal well trajectory data, surface elevation data of the work area, and fracturing construction pressure curves of each fracturing section of the horizontal well; collect logging data of horizontal wells including acoustic travel time, resistivity, natural gamma ray GR, and compensated neutron CNL; horizontal well trajectory data includes wellhead coordinates, kelly bushing elevation, and depth, azimuth, and inclination of well deviation data.

[0027] Use the collected data to calculate the bottom-hole fracture closure pressure of each fracturing section of the horizontal well; first, perform a pressure drop curve analysis on the fracturing construction pressure curve of each fracturing section, use the G function method to obtain the closure pressure of each fracturing section to get the wellhead closure pressure, use the minimum curvature method through the horizontal well trajectory data to obtain the average vertical depth of each fracturing section, calculate the hydrostatic pressure through the average vertical depth, and add the wellhead closure pressure and the corresponding hydrostatic pressure to obtain the bottom-hole fracture closure pressure; when calculating the closure pressure of each fracturing section, when the fracturing construction of a fracturing section is abnormal and the calculated closure pressure value is abnormal, filter out the abnormal closure pressure value of the abnormal fracturing section.

[0028] Use the logging data of the horizontal well to calculate the average value of the logging curves of each fracturing section of the horizontal well, use the logging data of the horizontal well to calculate the average value of the logging curves of acoustic travel time, resistivity, natural gamma ray, and compensated neutron of each fracturing section of the horizontal well. Among them, when calculating the average value of the logging curves of resistivity of each fracturing section of the horizontal well, take the logarithm of the resistivity and then calculate to obtain the average value of the logging curves of resistivity of each fracturing section; among them, the average value of the logging curves of natural gamma ray and compensated neutron is as shown in the second column of Figure 1 ; use the well trajectory curve and surface elevation data to calculate the average buried depth of each fracturing section of the horizontal well, and calculate the average formation buried depth of each fracturing section by adding the average vertical depth of each fracturing section to the difference between the surface elevation at each fracturing section of the horizontal well and the surface elevation at the wellhead.

[0029] Establish a model to calculate the low- and medium-frequency components of the predicted minimum horizontal principal stress. Using the average value of the logging curves of each fracturing section of the horizontal well and the average formation buried depth of each fracturing section as variables, and the bottom-hole fracture closure pressure of each fracturing section as the target value, establish a training and learning data set to carry out minimum horizontal principal stress prediction learning, and use the Butterworth low-pass filtering method to filter and obtain the low- and medium-frequency components of the predicted minimum horizontal principal stress as shown in the third column of Figure 1 ; calculate the high-frequency components of the predicted minimum horizontal principal stress as shown in the fourth column of Figure 1 ; filter the acoustic travel time curve in the logging data of the horizontal well to obtain the high-frequency components of the predicted minimum horizontal principal stress.

[0030] The minimum horizontal principal stress of the horizontal well is obtained by combining the medium and low frequency components of the predicted minimum horizontal principal stress with the high frequency components of the predicted minimum horizontal principal stress. Figure 1 As shown in the fifth column.

[0031] The multi-information fusion method for predicting the minimum horizontal principal stress of a horizontal well provided in the embodiment of the present application is to collect the horizontal well logging data, the horizontal well trajectory data and the fracturing construction pressure curve of each fracturing section, and calculate the bottom hole fracture closure pressure of each fracturing section. The horizontal well logging data is used to calculate the average value of the logging curve of each fracturing section of the horizontal well, the well trajectory curve and the surface elevation data are used to calculate the average value of the formation burial depth, and a model is established to calculate the medium and low frequency components of the predicted minimum horizontal principal stress, and the high frequency components of the predicted minimum horizontal principal stress are calculated, and finally the medium and low frequency components of the predicted minimum horizontal principal stress are added to the high frequency components of the predicted minimum horizontal principal stress to obtain the minimum horizontal principal stress of the horizontal well. The multi-information fusion method for predicting the minimum horizontal principal stress of horizontal wells provided in the present application can avoid the problems of lack of dipole shear wave data and deformation coefficient calculation and correction of longitudinal and shear wave logging data of horizontal wells in the stress calculation process, and use limited data to predict the minimum horizontal principal stress of horizontal wells and effectively improve the prediction accuracy. It plays an important role in the design of horizontal well fracturing segmentation schemes and fracturing process parameter design, has certain reference for shale gas exploration and development, and has broad promotion prospects.

[0032] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A prediction method for the minimum horizontal principal stress of horizontal wells with multi-information fusion, characterized in that Including the following steps: Collect data; collect horizontal well logging data, horizontal well trajectory data, surface elevation data of the work area, and the fracturing construction pressure curves of each fracturing stage of the horizontal well; Use the collected data to calculate the bottom-hole fracture closure pressure of each fracturing stage of the horizontal well; Use the horizontal well logging data to calculate the average value of the logging curves of each fracturing stage of the horizontal well, and use the horizontal well trajectory data and the surface elevation data of the work area to calculate the average buried depth of each fracturing stage of the horizontal well; Establish a model to calculate the low- and medium-frequency components of the predicted minimum horizontal principal stress, and calculate the high-frequency components of the predicted minimum horizontal principal stress; when establishing a model to predict the low- and medium-frequency components of the minimum horizontal principal stress, use the average value of the logging curves of each fracturing stage of the horizontal well and the average buried depth of each fracturing stage of the formation as variables, and use the bottom-hole fracture closure pressure of each fracturing stage as the target value to establish a training and learning data set to carry out minimum horizontal principal stress prediction learning, and use the Butterworth low-pass filter method to filter and obtain the low- and medium-frequency components of the predicted minimum horizontal principal stress; filter the acoustic travel-time curve in the horizontal well logging data to obtain the high-frequency components of the predicted minimum horizontal principal stress; Obtain the minimum horizontal principal stress of the horizontal well by combining the low- and medium-frequency components of the predicted minimum horizontal principal stress and the high-frequency components of the predicted minimum horizontal principal stress.

2. The minimum horizontal principal stress prediction method for horizontal wells with multi-information fusion according to claim 1, characterized in that When using the collected data to calculate the closure pressure of each fracturing stage of the horizontal well, first conduct a pressure-drop curve analysis on the fracturing construction pressure curve of each fracturing stage, use the G function to obtain the closure pressure of each fracturing stage to get the wellhead closure pressure, use the minimum curvature method through the horizontal well trajectory data to obtain the average vertical depth of each fracturing stage, calculate the hydrostatic pressure through the average vertical depth, and add the wellhead closure pressure and the corresponding hydrostatic pressure to obtain the bottom-hole fracture closure pressure.

3. The method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion according to claim 1, wherein, When using the horizontal well trajectory data and the surface elevation data of the work area to calculate the average buried depth of each fracturing stage of the horizontal well, use the average vertical depth of each fracturing stage plus the difference between the surface elevation at each fracturing stage of the horizontal well and the surface elevation at the wellhead to calculate the average buried depth of each fracturing stage of the formation.

4. The method for predicting the minimum horizontal principal stress of a horizontal well with multi-information fusion according to claim 1, characterized in that, Collecting horizontal well logging data includes acoustic travel-time, resistivity, natural gamma ray GR, and compensated neutron CNL; horizontal well trajectory data includes wellhead coordinates, kelly bushing elevation, and depth, azimuth, and inclination of well deviation data.

5. The minimum horizontal principal stress prediction method for horizontal wells with multi-information fusion according to claim 1, characterized in that, When using the horizontal well logging data to calculate the average value of the logging curves of each fracturing stage of the horizontal well, take the logarithm of the resistivity and then calculate to obtain the average value of each fracturing stage.

6. The minimum horizontal principal stress prediction method for horizontal wells with multi-information fusion according to claim 1, characterized in that When calculating the bottom-hole fracture closure pressure of each fracturing stage, when the fracturing construction of a fracturing stage is abnormal and the calculated closure pressure value is abnormal, filter out the abnormal closure pressure value of the abnormal fracturing stage.

Images