CN116188706A - Shale reservoir three-dimensional modeling method based on big data - Google Patents
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Abstract
The invention discloses a shale reservoir three-dimensional modeling method based on big data, which comprises the following steps: s1, constructing a pre-drilling three-dimensional geological model of a target area by a Kriging interpolation method, and measuring downhole data of a drill bit during drilling; s2, transmitting downhole data to a software platform in real time, correcting a pre-drilling three-dimensional geological model by using the software platform, and obtaining a three-dimensional geological model; s3, displaying the underground stratum condition on a display terminal in real time by utilizing a three-dimensional geological model; s4, a worker observes the relation between the drill bit and the stratum through the display terminal and makes adaptive adjustment to ensure that the drill bit is kept at the target layer. According to the method, the three-dimensional geological model before drilling is built by utilizing the data while drilling, the model is corrected in real time according to the data while drilling, the accuracy of the model can be guaranteed, the functions of slicing, expanding, shrinking, rotating and the like are provided for the model, the correlation of the borehole track in the three-dimensional space is intuitively displayed, and the method is beneficial to reducing the collision risk of the shaft.
Description
Technical Field
The invention relates to the field of shale storage, in particular to a shale reservoir three-dimensional modeling method based on big data.
Background
At present, because the shale of the Loma stream has the characteristics of strong heterogeneity and thin target reservoir, the phenomenon that a drill bit deviates from the target reservoir easily occurs in the drilling and production process, and the geosteering is particularly important. At present, only two-dimensional display function geosteering software is in China, and is not visual to observe, and better basis can not be provided for adjustment of a drill bit, so that a three-dimensional display software platform capable of accurately displaying a subsurface stratum structure in real time is urgently needed.
Two-dimensional geosteering generally uses a single-port co-ordinated data as a reference for guided modeling, mainly taking into account the vertical distribution of formation properties, and the guiding process is relatively simple. The data mainly used is limited to geological and well logging data, so that the requirements of horizontal well geosteering can be met to a certain extent; the seismic data contains more potential stratum and oil and gas information, can reflect stratum morphology, stratum properties, oil and gas reservoir positions and the like, and is hardly or rarely applied in the current two-dimensional geosteering process. At present, the domestic method gas field generally adopts MD (with GR or GR+RT) or LWD (with GR and RT) to combine with comprehensive logging to carry out horizontal well geosteering.
Two-dimensional geosteering cannot accurately reflect the three-dimensional spatial distribution form of the well track, the formation structure change around the well limit track and the anisotropic change of the reservoir, faults and cracks cannot be accurately described, and the reservoir inversion result of seismic data cannot be utilized, so that certain uncertainty is brought to geosteering work.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a shale reservoir three-dimensional modeling method based on big data, which aims to overcome the technical problems existing in the prior related art.
For this purpose, the invention adopts the following specific technical scheme:
the shale reservoir three-dimensional modeling method based on big data comprises the following steps:
s1, constructing a pre-drilling three-dimensional geological model of a target area by a Kriging interpolation method, and measuring downhole data of a drill bit during drilling;
s2, transmitting downhole data to a software platform in real time, correcting a pre-drilling three-dimensional geological model by using the software platform, and obtaining a three-dimensional geological model;
s3, displaying the underground stratum condition on a display terminal in real time by utilizing a three-dimensional geological model;
s4, a worker observes the relation between the drill bit and the stratum through the display terminal and makes adaptive adjustment to ensure that the drill bit is kept at the target layer.
Further, the construction of the kriging interpolation method comprises the following steps:
constructing a variation function;
taking the known points with effective neighborhood search as input points, and calculating variation function values between any two input points and between the point to be interpolated and all the input points;
assigning a value to the K matrix and inverting the value to obtain an inverse matrix of the K matrix;
and multiplying the inverse matrix of the K matrix by the M matrix to obtain a weight coefficient, and weighting to obtain the attribute value of the point to be interpolated.
Further, the K matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variation function between the nth known point and the nth known point, and the value of n columns in the n rows before the K matrix is the value of the variation function between every two known points.
Further, the M matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variance function between the nth known point and the nth known point, and the value of the variance function between the point currently requiring evaluation at the first n behaviors of the M matrix and each known point.
Further, the method for constructing the pre-drilling three-dimensional geological model of the target area by the Kriging interpolation method and measuring the downhole data of the drill bit during drilling comprises the following steps:
s11, establishing a blank model;
s12, setting the lengths of the three directions of the three-dimensional model and the step length of each grid according to the size of the drilled block, and reading adjacent well data and seismic data;
s13, building a pre-drilling three-dimensional geological model through the attribute value of the point to be interpolated by the Kriging interpolation method, and measuring downhole data of the drill bit in real time in the drilling process.
Further, the transmitting the downhole data to the software platform in real time, correcting the pre-drilling three-dimensional geological model by the software platform, and obtaining the three-dimensional geological model comprises the following steps:
s21, receiving underground data transmitted in real time by a software platform, and modifying a pre-drilling three-dimensional geological model in real time according to the data while drilling;
s22, slicing, scaling and rotating the pre-drilling three-dimensional geological model, and continuously correcting the pre-drilling three-dimensional geological model;
s23, continuously correcting until the required three-dimensional geological model is obtained.
Further, the displaying the underground stratum condition on the display terminal in real time by using the three-dimensional geological model comprises the following steps:
s31, constructing a deposition microphase identification model;
s32, processing the three-dimensional geological model in the display terminal through a deposition microphase identification model, and identifying the underground stratum condition in the three-dimensional geological model;
s33, displaying the underground stratum condition in the three-dimensional geological model on a display terminal in a visual mode.
Further, the construction of the deposition microphase identification model comprises the following steps:
s311, inputting a stratum image of the underground to a GoogleNet input layer;
s312, extracting the image features of the underground stratum through convolution operation of a convolution layer and pooling treatment of a downsampling layer;
s313, inputting a full connection layer to judge the target category of the underground stratum image;
s314, converting the multi-classification output numerical value into a corresponding probability score through a softmax loss function, and outputting a classification result.
Further, the model formula of google net is:
wherein X is the dimension of an input signal, Y is the dimension of an output signal, W is a convolution kernel, b is a bias term, θ is a model weight parameter, and X is an input object.
Further, the softmax loss function is formulated as:
in the formula, softmax (z i ) For the probability score of the ith neuron, z i For the output value of the ith neuron, n is the number of neurons participating in classification, and e is the bottom of the natural logarithm.
The beneficial effects of the invention are as follows:
1. according to the method, the three-dimensional geological model before drilling is built by utilizing the data while drilling, the model is corrected in real time according to the data while drilling, the accuracy of the model can be guaranteed, meanwhile, the functions of slicing, scaling, rotation and the like are provided for the model, the correlation of the borehole track in the three-dimensional space is intuitively displayed, and the method is beneficial to reducing the collision risk of the shaft; and the two-dimensional and three-dimensional geologic model is combined to make real-time geologic reservoir decisions, so that the accurate simulation of abnormal and complex geologic conditions can be realized, and the drill bit can be ensured to be always in a target layer.
2. The invention can realize real-time accurate display of underground stratum conditions, simultaneously provide functions of rotary slicing and the like, facilitate ground staff to check and adjust the drill bit, and ensure that the drill bit drills in a target layer all the time.
3. The method has the advantages of providing the Kriging interpolation by comparing with other interpolation methods, has great advantages compared with other interpolation methods, ensures the interpolation accuracy, simultaneously correspondingly improves the complexity of the Kriging interpolation, greatly improves the interpolation efficiency and can be better applied to practical engineering.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a shale reservoir three-dimensional modeling method based on big data in accordance with an embodiment of the invention.
Detailed Description
For the purpose of further illustrating the various embodiments, the present invention provides the accompanying drawings, which are a part of the disclosure of the present invention, and which are mainly used to illustrate the embodiments and, together with the description, serve to explain the principles of the embodiments, and with reference to these descriptions, one skilled in the art will recognize other possible implementations and advantages of the present invention, wherein elements are not drawn to scale, and like reference numerals are generally used to designate like elements.
According to an embodiment of the invention, a shale reservoir three-dimensional modeling method based on big data is provided.
The invention will be further described with reference to the accompanying drawings and specific embodiments, as shown in fig. 1, a shale reservoir three-dimensional modeling method based on big data according to an embodiment of the invention, the shale reservoir three-dimensional modeling method includes the following steps:
s1, constructing a pre-drilling three-dimensional geological model of a target area by a Kriging interpolation method, and measuring downhole data of a drill bit during drilling;
in one embodiment, the construction of the kriging interpolation includes the steps of:
constructing a variation function;
taking the known points with effective neighborhood search as input points, and calculating variation function values between any two input points and between the point to be interpolated and all the input points;
assigning a value to the K matrix and inverting the value to obtain an inverse matrix of the K matrix;
and multiplying the inverse matrix of the K matrix by the M matrix to obtain a weight coefficient, and weighting to obtain the attribute value of the point to be interpolated.
In one embodiment, the K matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variation function between the nth known point and the nth known point, and the value of n columns in the n rows before the K matrix is the value of the variation function between every two known points.
In one embodiment, the M matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variance function between the nth known point and the nth known point, and the value of the variance function between the point currently requiring evaluation at the first n behaviors of the M matrix and each known point.
In one embodiment, the pre-drilling three-dimensional geological model of the target area is constructed by the Kriging interpolation method, and the measuring of the downhole data of the drill bit during drilling comprises the following steps:
s11, establishing a blank model;
s12, setting the lengths of the three directions of the three-dimensional model and the step length of each grid according to the size of the drilled block, and reading adjacent well data and seismic data;
s13, building a pre-drilling three-dimensional geological model through the attribute value of the point to be interpolated by the Kriging interpolation method, and measuring downhole data of the drill bit in real time in the drilling process.
Specifically, since the unbiased estimation value is to be obtained, the condition of unbiased is to be satisfied, that is, the weighting value of the weight coefficient is 1; meanwhile, the regional variable needs to meet a second-order balance assumption, and the weighting coefficient can be obtained by combining the conditions;
in addition, in recent years, scientific computing visualization technology has rapidly developed and has wide application in various engineering and computing fields, especially in geological three-dimensional model modeling, and in order to truly reflect a geological model, a large data set needs to be visualized. However, under the existing technical conditions and limited by many factors in practice, the data points that can be acquired are very small relative to the point set required for data visualization, so relying only on limited discrete data points is far from meeting the visualization requirements. To solve this problem, interpolation techniques have emerged therefrom;
the interpolation technology is to utilize some corresponding analysis research models based on known limited discrete data point information, build a mapping relation between the spatial domain and the attribute domain by researching the attribute information of the sample points, quantify the mapping relation, and finally estimate the value at the unknown point based on the built mapping relation. Due to the wide application of interpolation techniques, a number of interpolation methods have also emerged. The existing common interpolation methods include a distance inverse proportion method, a radial basis function method, a Thiessen polygon method, spline interpolation, a Kriging method and the like.
The basic idea of the inverse distance method is to calculate the distance between the known point and the unknown point, and then weight average the attribute value of the known point by the inverse ratio of the distance to obtain the unknown attribute value. The method is simple and easy to understand and easy to implement, but only takes the distance as the basis of interpolation, and does not consider the relationship between the known points, and the estimation accuracy is limited.
The Thiessen polygon method takes a known point as a center, divides an interpolation area into thousands of polygons, and takes the known point in a subarea as an input of a point to be interpolated in the polygon area to perform interpolation calculation. The method is visual, simple and easy to understand, but when the distribution of the sampling points is very uneven, singular polygons are easy to appear in the formed polygons, so that the interpolation precision is lower. Moreover, the Thiessen polygon method is only suitable for two-dimensional planes, and has great limitations in application.
As a spatial prediction method widely used in various fields, the kriging method was first proposed by the south african mining engineer Krige. The Kriging method is studied with the objective of a variation function that gives an estimate of the regionalized variable over a limited region, and this estimate is optimal and unbiased. The basic idea of the kriging interpolation is that firstly, the influence of a known point on an estimated point is judged, the influence is expressed by a weight coefficient, and then the weight coefficient is utilized to carry out weighted summation on the attribute value of the known point to obtain the attribute value of the point to be interpolated, so that the kriging interpolation is a linear interpolation method. Furthermore, it is said to be an optimal, unbiased estimation method because its mathematical model is built on the basis of analysis of the regionalized variables and the variational functions. As can be seen from the description of the above methods, the kriging method considers not only the distance factor, but also the spatial correlation 1221 between known points as an important theoretical premise for estimation, and quantifies this spatial correlation by studying the variational function. Therefore, the result error obtained by the Kriging interpolation method is small and more accurate, the complexity of the Kriging interpolation is correspondingly improved, the interpolation efficiency is greatly improved, and the method can be better applied to practical engineering.
Compared with other methods, the kriging method has great advantages, ensures the interpolation accuracy and correspondingly improves the complexity of the kriging interpolation.
S2, transmitting downhole data to a software platform in real time, correcting a pre-drilling three-dimensional geological model by using the software platform, and obtaining a three-dimensional geological model;
in one embodiment, the transmitting downhole data to the software platform in real time and correcting the pre-drilling three-dimensional geologic model with the software platform and obtaining the three-dimensional geologic model comprises the steps of:
s21, receiving underground data transmitted in real time by a software platform, and modifying a pre-drilling three-dimensional geological model in real time according to the data while drilling;
s22, slicing, scaling and rotating the pre-drilling three-dimensional geological model, and continuously correcting the pre-drilling three-dimensional geological model;
s23, continuously correcting until the required three-dimensional geological model is obtained.
S3, displaying the underground stratum condition on a display terminal in real time by utilizing a three-dimensional geological model;
in one embodiment, the displaying the condition of the subsurface stratum on the display terminal in real time by using the three-dimensional geological model comprises the following steps:
s31, constructing a deposition microphase identification model;
s32, processing the three-dimensional geological model in the display terminal through a deposition microphase identification model, and identifying the underground stratum condition in the three-dimensional geological model;
s33, displaying the underground stratum condition in the three-dimensional geological model on a display terminal in a visual mode.
In one embodiment, the building of the deposit microphase identification model includes the steps of:
s311, inputting a stratum image of the underground to a GoogleNet input layer;
s312, extracting the image features of the underground stratum through convolution operation of a convolution layer and pooling treatment of a downsampling layer;
s313, inputting a full connection layer to judge the target category of the underground stratum image;
s314, converting the multi-classification output numerical value into a corresponding probability score through a softmax loss function, and outputting a classification result.
In one embodiment, the model formula for GoogleNet is:
wherein X is the dimension of an input signal, Y is the dimension of an output signal, W is a convolution kernel, b is a bias term, θ is a model weight parameter, and X is an input object.
In one embodiment, the softmax loss function is formulated as:
in the formula, softmax (z i ) For the probability score of the ith neuron, z i For the output value of the ith neuron, n is the number of neurons participating in classification, and e is the bottom of the natural logarithm.
Specifically, the training process of the convolutional neural network is essentially an optimization updating process of the network weight, the GoogleNet parameters are adjusted according to the well logging image data set, and proper super parameters are selected to facilitate the retrieval of the well logging images.
S4, a worker observes the relation between the drill bit and the stratum through the display terminal and makes adaptive adjustment to ensure that the drill bit is kept at the target layer.
Specifically, the interrelation of the well track in the three-dimensional space is intuitively displayed, the well collision risk is reduced, the stable construction is ensured, and stable operation guarantee is provided for the construction.
In summary, by means of the technical scheme, the three-dimensional geological model before drilling is built by using the data while drilling, the model is corrected in real time according to the data while drilling, the accuracy of the model can be ensured, meanwhile, the functions of slicing, scaling, rotating and the like are provided for the model, the interrelation of the well track in the three-dimensional space is intuitively displayed, and the well collision risk is reduced; real-time geological oil reservoir decision is carried out by combining two-dimensional and three-dimensional geological models, so that the geological condition is extremely complex and can be accurately simulated, and the drill bit can be always in a target layer; the invention can realize real-time accurate display of underground stratum conditions, simultaneously provide functions of rotary slicing and the like, facilitate ground staff to check and adjust the drill bit, and ensure that the drill bit drills in a target layer all the time; the method has the advantages of providing the Kriging interpolation by comparing with other interpolation methods, has great advantages compared with other interpolation methods, ensures the interpolation accuracy, simultaneously correspondingly improves the complexity of the Kriging interpolation, greatly improves the interpolation efficiency and can be better applied to practical engineering.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The shale reservoir three-dimensional modeling method based on big data is characterized by comprising the following steps of:
s1, constructing a pre-drilling three-dimensional geological model of a target area by a Kriging interpolation method, and measuring downhole data of a drill bit during drilling;
s2, transmitting downhole data to a software platform in real time, correcting a pre-drilling three-dimensional geological model by using the software platform, and obtaining a three-dimensional geological model;
s3, displaying the underground stratum condition on a display terminal in real time by utilizing a three-dimensional geological model;
s4, a worker observes the relation between the drill bit and the stratum through the display terminal and makes adaptive adjustment to ensure that the drill bit is kept at the target layer.
2. The three-dimensional modeling method of shale reservoir based on big data according to claim 1, wherein the construction of the kriging interpolation method comprises the following steps:
constructing a variation function;
taking the known points with effective neighborhood search as input points, and calculating variation function values between any two input points and between the point to be interpolated and all the input points;
assigning a value to the K matrix and inverting the value to obtain an inverse matrix of the K matrix;
and multiplying the inverse matrix of the K matrix by the M matrix to obtain a weight coefficient, and weighting to obtain the attribute value of the point to be interpolated.
3. The shale reservoir three-dimensional modeling method based on big data according to claim 2, wherein the K matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variation function between the nth known point and the nth known point, and the value of n columns in the n rows before the K matrix is the value of the variation function between every two known points.
4. A shale reservoir three-dimensional modeling method based on big data according to claim 3, wherein the M matrix is constructed as follows:
wherein, gamma (v) n ,v n ) Is the value of the variance function between the nth known point and the nth known point, and the value of the variance function between the point currently requiring evaluation at the first n behaviors of the M matrix and each known point.
5. The three-dimensional modeling method of shale reservoir based on big data according to claim 4, wherein the pre-drilling three-dimensional geological model of the target area is constructed by the kriging interpolation method, and the measuring of the downhole data of the drill bit during drilling comprises the following steps:
s11, establishing a blank model;
s12, setting the lengths of the three directions of the three-dimensional model and the step length of each grid according to the size of the drilled block, and reading adjacent well data and seismic data;
s13, building a pre-drilling three-dimensional geological model through the attribute value of the point to be interpolated by the Kriging interpolation method, and measuring downhole data of the drill bit in real time in the drilling process.
6. The method for three-dimensional modeling of shale reservoirs based on big data according to claim 1, wherein the steps of transmitting downhole data to a software platform in real time, correcting a pre-drilling three-dimensional geological model by the software platform, and obtaining the three-dimensional geological model comprise the following steps:
s21, receiving underground data transmitted in real time by a software platform, and modifying a pre-drilling three-dimensional geological model in real time according to the data while drilling;
s22, slicing, scaling and rotating the pre-drilling three-dimensional geological model, and continuously correcting the pre-drilling three-dimensional geological model;
s23, continuously correcting until the required three-dimensional geological model is obtained.
7. The three-dimensional modeling method of shale reservoir based on big data according to claim 1, wherein the real-time displaying of the underground stratum condition on the display terminal by using the three-dimensional geological model comprises the following steps:
s31, constructing a deposition microphase identification model;
s32, processing the three-dimensional geological model in the display terminal through a deposition microphase identification model, and identifying the underground stratum condition in the three-dimensional geological model;
s33, displaying the underground stratum condition in the three-dimensional geological model on a display terminal in a visual mode.
8. The method for three-dimensional modeling of shale reservoirs based on big data according to claim 7, wherein the construction of the sedimentary microphase identification model comprises the following steps:
s311, inputting a stratum image of the underground to a GoogleNet input layer;
s312, extracting the image features of the underground stratum through convolution operation of a convolution layer and pooling treatment of a downsampling layer;
s313, inputting a full connection layer to judge the target category of the underground stratum image;
s314, converting the multi-classification output numerical value into a corresponding probability score through a softmax loss function, and outputting a classification result.
9. The three-dimensional modeling method for shale reservoirs based on big data according to claim 8, wherein the model formula of GoogleNet is:
wherein X is the dimension of an input signal, Y is the dimension of an output signal, W is a convolution kernel, b is a bias term, θ is a model weight parameter, and X is an input object.
10. The three-dimensional modeling method of shale reservoir based on big data according to claim 8, wherein the softmax loss function is formulated as:
in the formula, softmax (z i ) For the probability score of the ith neuron, z i For the output value of the ith neuron, n is the number of neurons participating in classification, and e is the bottom of the natural logarithm.
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