CN117972441A - Method for carrying out stratum comparison by using time domain gradient method - Google Patents

Abstract

The application discloses a method for carrying out stratum contrast by using a time domain gradient method, which comprises the following steps: calculating weighted average of amplitude deviation and gradient deviation of logging data in a certain sliding window length of a layer section corresponding to the reference well and the target well by using logging data of six logging methods, namely, micropotential, high-resolution lateral, microsphere, density, high-resolution sound wave and natural gamma of the corresponding layer of the reference well and the target well, so as to obtain a corresponding time domain gradient value; selecting depths corresponding to the layers with the minimum time domain gradient values in the whole well section of the target well by using six well logging methods, matching the depths corresponding to the layers of the reference well with depth differences of adjacent layers, and determining final matching depths; the method solves the problems that the prior computer automatically compares, the utilization of the morphological characteristics of the log data curve is insufficient, and the horizon comparison error is large.

Description

Method for carrying out stratum comparison by using time domain gradient method

Technical Field

The present disclosure relates to formation contrast techniques in the field of reservoir engineering-logging geology, and in particular to computer intelligent formation contrast techniques utilizing well logging data.

Background

The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

In addition to manual comparison, the existing formation comparison technology is that on Log software, firstly, a reference horizon of a reference well and a target well is manually compared, as shown in fig. 1, the reference well is arranged below, the target well is arranged above, and the general position of the target well S216 layer is determined according to the distance from the reference well S216 layer to the S215 layer and the distance is used for determining the general position of the target well S215 layer, so that the computer automatic formation comparison is realized. However, because the computer automatically compares the amplitude information of the logging data, the correlation comparison or image matching technology is adopted to perform stratum comparison, and when the logging data contains noise, physical properties, layer thickness and the like, the horizon comparison error is larger.

It should be noted that the information disclosed in the foregoing background section is only for enhancing understanding of the background of the present disclosure, and thus may contain information that does not constitute prior art.

Disclosure of Invention

In view of the above, the disclosure provides a method for performing formation comparison by using a time domain gradient method, which solves the problem of larger horizon comparison error when the formation lithology, physical properties and formation thickness change, resulting in the change of the profile of logging data.

In order to achieve the above object, the method for performing formation contrast by using a time domain gradient method is characterized by comprising the following steps:

Calculating weighted average of amplitude deviation and gradient deviation in a certain sliding window length of a layer section corresponding to the reference well and the target well by using logging data of six logging methods, namely, the micropotential, the high-resolution lateral, the microsphere, the density, the high-resolution sound wave and the natural gamma of the corresponding layer of the reference well and the target well, so as to obtain a corresponding time domain gradient value;

And selecting a horizon with the minimum time domain gradient value in the whole well section of the target well, and selecting a final matching depth according to the depth corresponding to the horizon.

In an embodiment of the present disclosure, a method for selecting a final matching depth according to a depth corresponding to the horizon includes:

In the whole well section of the target well, aiming at each well logging method, selecting the depth corresponding to the horizon with the smallest time domain gradient value as the optimal discrimination depth of the well logging method;

Selecting the same depth point in the optimal discrimination depths of the six well logging methods, and taking the depth point as the final matching depth if the depth point is one;

If the same depth points are more than two, selecting a horizon with the minimum time domain gradient value and a previous horizon, acquiring depth differences between the two corresponding horizons on a reference well as reference depth differences, respectively calculating the depth differences between the same depth points and the depth points of the previous horizon, and selecting the depth point closest to the reference depth difference in the depth differences as the final matching depth;

If the same depth point does not exist, respectively calling the logging data of the adjacent wells with the same horizon as the optimal discrimination depth to replace the logging data of the same horizon of the reference well to recalculate the optimal discrimination depth, and redefining the final matching depth according to the recalculated optimal discrimination depth;

If all the wells at the same horizon in the area are recalculated, the same depth point is not found yet, and the wells are treated according to faults.

In an embodiment of the disclosure, the logging data is normalized to eliminate the influence of measurement units among different logging data, while reducing the influence of measurement noise on calculating the amplitude deviation and the gradient deviation.

In the embodiment of the disclosure, the normalization processing is to respectively normalize the logging data of the six logging methods of the reference well horizon window and the whole well section of the target well in a sliding window with a certain sliding window length to obtain corresponding normalized logging data.

In an embodiment of the present disclosure, the normalization method includes:

And removing the measured values of other measuring points of the corresponding logging method in the sliding window by using the maximum value of the logging data of the various logging methods in each sliding window to obtain normalized logging data of the various logging data in the sliding window.

In an embodiment of the present disclosure, the magnitude deviation and the gradient deviation are calculated using the normalized log data.

In an embodiment of the disclosure, the calculation formula of the amplitude deviation is:

The calculation formula of the gradient deviation is as follows:

The calculation formula of the time domain gradient value is as follows:

Wherein: x _i,y_i is the normalized log data of the i-th point of the target well and the reference well;

x _i-1、y_i-1 is normalized log data for the i-1 th point of the target well and the reference well; Normalizing the gradient of the change of the logging data for the target well and the reference well; m1 and m2 are weighting coefficients, and the value ranges are respectively m1:0.1 to 0.9, m2:0.9 to 0.1.

In an embodiment of the disclosure, both m1 and m2 are 0.5.

In an embodiment of the present disclosure, the certain sliding window length is 8-200 measurement data points.

The method has the following beneficial effects:

According to the method for carrying out stratum comparison by utilizing the time domain gradient method, through the optimized six types of logging information, the gradient similarity and the weight of deviation between the reference curve and the target curve are adopted, the logging similarity is measured based on the amplitude characteristics and the gradient characteristics, an optimized similarity algorithm is constructed by integrating logging amplitude information and gradient information, and the reference well reference interval logging information and the target well logging information are compared to find the final matching depth, namely the determined horizon depth. The time domain gradient method can effectively extract the similarity of the logging curves of the reference well and the target well, and the intelligent comparison of the reservoirs by using logging data is successfully realized by adopting a weighted average mode of the amplitude deviation and the gradient deviation of the logging data of the corresponding intervals of the target well and the reference well.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of computer-automated comparison of reference well and target well horizon depth matching as described in the present disclosure;

FIG. 2 is a flow chart of a method of formation comparison using time domain gradient methods in accordance with an embodiment of the present disclosure;

FIG. 3 is a reference well S24 horizon log feature of an embodiment of the present disclosure;

FIG. 4 is a target well S24 horizon log feature and contrast depth according to an embodiment of the present disclosure;

FIG. 5 is reference well S24 horizon six log data and response characteristics for an embodiment of the present disclosure;

FIGS. 5-1 through 5-6 are six log response features of FIG. 5;

FIG. 6 is a graph of six log data, response characteristics and processing, comparison results for a target well S24 horizon according to embodiments of the present disclosure;

FIGS. 6-1 through 6-6 are six log response features of FIG. 6;

FIG. 7 is a graph of the self-test effect of a target well and a reference well of an embodiment of the present disclosure being the same well;

FIG. 8 is a graph of the comparative effects of a salve set of target wells and reference wells being different wells, in accordance with an embodiment of the present disclosure.

Detailed Description

The present disclosure is described below based on embodiments, but it is worth noting that the present disclosure is not limited to these embodiments. In the following detailed description of the present disclosure, certain specific details are set forth in detail. However, for portions not described in detail, those skilled in the art can also fully understand the present disclosure.

Furthermore, those of ordinary skill in the art will appreciate that the drawings are provided solely for purposes of illustrating the objects, features, and advantages of the disclosure and that the drawings are not necessarily drawn to scale.

Meanwhile, unless the context clearly requires otherwise, throughout the description and the claims, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".

The design concept of the method disclosed by the invention is as follows: according to the principle of a time domain gradient method, the optimized logging data is utilized, weighted average of gradient similarity and deviation of a reference curve and a target curve is adopted, the logging similarity is measured based on gradient characteristics, an optimized similarity algorithm is constructed by integrating logging numerical information and gradient information, and the final matching depth is selected to be the determined horizon through comparison of logging information of a reference interval of a reference well and logging information of a target well. That is, the method and the system effectively extract the similarity of the reference well logging curve and the target well logging curve by using the time domain gradient values, and perform intelligent reservoir comparison by using logging data in a mode of weighted average of amplitude deviation and gradient deviation of logging data of corresponding intervals of the target well and the reference well.

Under the guidance of the inventive concept, the disclosure designs a specific technical scheme. FIG. 2 is a flow chart of a method of formation comparison using time domain gradient methods in accordance with an embodiment of the present disclosure; as shown in fig. 2: the method for performing stratum contrast by using the time domain gradient method comprises the following steps: calculating weighted average of amplitude deviation and gradient deviation in a certain sliding window length of a layer section corresponding to the reference well and the target well by using logging data of six logging methods, namely, the micropotential, the high-resolution lateral, the microsphere, the density, the high-resolution sound wave and the natural gamma of the corresponding layer of the reference well and the target well, so as to obtain a corresponding time domain gradient value; and selecting a horizon with the minimum time domain gradient value in the whole well section of the target well, and selecting a final matching depth according to the depth corresponding to the horizon.

Specifically, taking the same-layer comparison of the S24 layer of the target well and the S24 layer of the reference well in a certain area as an example, the method for performing formation comparison by using the time domain gradient method in the disclosure is described in detail as follows.

And (3) a step of: the weighted average of amplitude deviation and gradient deviation in a certain sliding window length of a corresponding interval of the reference well and the target well is calculated through logging data of six logging methods, namely the micropotential, the high-resolution lateral, the microsphere, the density, the high-resolution sound wave and the natural gamma of the reference well and the target well, so that a corresponding time domain gradient value is obtained.

As is well known, geophysical well logging methods in oil fields are many, but well logging methods suitable for the method are continuously researched by the inventor, and finally, the well logging data of the six well logging methods are determined to carry out amplitude deviation and gradient deviation calculation.

1. Normalization of log data

In a sliding window with a certain window length, respectively carrying out normalization processing on the micropotential logging data, the high-resolution lateral logging data, the microsphere logging data, the density logging data, the high-resolution acoustic logging data and the natural gamma logging data of six logging methods of a reference well horizon window and a whole well section of a target well to obtain corresponding normalized logging data, wherein the normalization processing is as follows:

(1) Determining a certain window length

8 To 200 measurement data points were selected as a sliding window length.

(2) Acquiring log data

And acquiring logging data corresponding to six logging methods, namely microelectrodes, high-resolution lateral directions, microspheres, density, high-resolution sound waves and natural gamma of corresponding horizon windows of the target well and the reference well respectively. Wherein, fig. 3 and 4 show the log characteristics of the reference well S24 and the target well S24, and fig. 5, fig. 5-1 to fig. 5-6 and fig. 6-1 to fig. 6-6 show six log data and response characteristics of the horizons of the reference well S24 and the target well S24, respectively.

(3) Normalizing the logging data

And carrying out normalization processing on the logging data in the sliding window so as to eliminate the influence of measurement units among logging data of different types and reduce the influence of measurement noise on a calculation result. And removing the measured values of other measuring points in the sliding window of the logging method by utilizing the maximum value of the logging data corresponding to the six logging methods in each sliding window to obtain the normalized values of various logging data in the sliding window.

And calculating a normalization value according to the method every time the sliding window slides by one point until normalized logging data of the target well whole well section and the reference well horizon window are calculated.

2. Calculating weighted average of amplitude deviation and gradient deviation in a certain sliding window length of a corresponding interval of the reference well and the target well through the normalized logging data respectively to obtain corresponding time domain gradient values, wherein the weighted average is as follows:

(1) Calculating amplitude deviation and gradient deviation

Calculating amplitude deviation and gradient deviation by using the normalized logging data of the corresponding layers of the reference well and the target well obtained in the step 1 (3); the formula for calculating the amplitude deviation is as follows: Wherein: x _i,y_i is normalized log data for the i-th point of the target well and the reference well.

The gradient bias was calculated using the formula: Wherein: x _i,y_i is normalized log data for the i-th point of the target and reference wells, and x _i-1、y_i-1 is normalized log data (meaning herein x, y) for the i-1-th point of the target and reference wells (i.e., the point immediately preceding the i-th point).

(2) Weighted average amplitude bias and gradient bias

The formula adopted is:

Wherein: x _i,y_i is normalized log data for the target well, reference well, point i,

For the change gradient of normalized logging data of a target well and a reference well, m1 and m2 are weighting coefficients, the value ranges of m1 and m2 are respectively 0.1-0.9 and 0.9-0.1, and the selection of m1 and m2 values is generally determined through experiments according to the needs of problems, and the m1 and m2 are selected to be 0.5.

And II: and selecting a horizon with the minimum time domain gradient value in the whole well section of the target well, and selecting a final matching depth according to the depth corresponding to the horizon.

In the whole well section of the target well, aiming at each well logging method, selecting the depth corresponding to the horizon with the smallest time domain gradient value as the optimal discrimination depth of the well logging method;

Selecting the same depth point in the optimal discrimination depth, and taking the depth point as the final matching depth if the depth point is one;

If the same depth point is more than two, selecting the horizon with the smallest time domain gradient value and the previous horizon, if the horizon with the smallest time domain gradient value is S24, the previous horizon is S23; obtaining a depth difference between two corresponding layers on a reference well as a reference depth difference, namely, taking the depth difference between an S23 layer and an S24 layer of the reference well as a reference depth difference; respectively calculating depth differences between the same depth points and the depth points of the previous layer, and selecting the depth point closest to the reference depth difference in the depth differences as the final matching depth;

If the same depth point does not exist, respectively calling the logging data of adjacent wells with the same level as the optimal discrimination depth (when a third well is not arranged between connecting straight lines where two wells are positioned, the two wells are adjacent to each other), replacing the logging data of the same level of the reference well, recalculating the optimal discrimination depth, and redetermining the final matching depth according to the recalculated optimal discrimination depth;

if all the wells at the same horizon in the traversing area are subjected to recalculation of the optimal discrimination depth, and are subjected to recalculation according to the recalculated optimal discrimination depth, the same depth point is not found yet, and the fault treatment is performed.

According to the method and the device, the adjacent other wells are selected as the reference wells again, the logging data of the same horizon of the adjacent wells are used for replacing the logging data of the same horizon of the reference wells to perform recalculation, and the defects that the logging data of the same horizon of the adjacent wells have measurement errors and lithology changes to change curve morphology and cause errors in stratum comparison according to the reference well data can be overcome.

The comparison process and comparison result through the method of the present disclosure are shown in fig. 4 and 6. As can be seen from fig. 4 and 6, and fig. 6-1 to 6-6, the S24 horizon depth calculated by the layer comparison method of the present disclosure completely coincides with the manual comparison depth.

In order to further verify the accuracy of the method, the same well and different wells are detected for the target well and the reference well respectively.

Wherein, fig. 7 is a self-detection result of the target well and the reference well being the same well, the self-detection is to the same well, the manual depth comparison and the comparison by adopting the method of the present disclosure are respectively performed, and the result is that the comparison results of the two are 100% coincident.

Fig. 8 is a graph of the comparison effect of different wells for a certain reservoir group target well and a reference well of the Daqingsal map, wherein the coincidence rate is more than 90% by manual comparison and comparison by the method.

According to the method disclosed by the invention, because the specific six types of logging data are selected, and then the logging data are subjected to corresponding normalization processing, the morphological characteristics of the logging method are reserved, and the influences of measurement errors, measurement units, random noise, system noise and the like among the logging data are eliminated; meanwhile, proper sliding window length is selected according to different logging data resolutions, depth errors among curves are eliminated, and finally, intelligent comparison of stratum of a reference well and a target well is successfully achieved according to the magnitude of a time domain gradient value and the combination of the horizon information of the reference well, so that stratum comparison by using a time domain gradient method is achieved.

The above examples are merely representative of embodiments of the present disclosure, which are described in more detail and are not to be construed as limiting the scope of the present disclosure. It should be noted that modifications, equivalent substitutions, improvements, etc. can be made by those skilled in the art without departing from the spirit of the present disclosure, which are all within the scope of the present disclosure. Accordingly, the scope of protection of the present disclosure should be determined by the following claims.

Claims (7)

1. A method for performing formation contrast using a time domain gradient method, comprising:

Calculating weighted average of amplitude deviation and gradient deviation in a certain sliding window length of a layer section corresponding to the reference well and the target well by using logging data of six logging methods, namely, the micropotential, the high-resolution lateral, the microsphere, the density, the high-resolution sound wave and the natural gamma of the corresponding layer of the reference well and the target well, so as to obtain a corresponding time domain gradient value;

And selecting a horizon with the minimum time domain gradient value in the whole well section of the target well, and selecting a final matching depth according to the depth corresponding to the horizon.

2. The method of formation comparison of claim 1, wherein: the method for selecting the final matching depth according to the depth corresponding to the horizon comprises the following steps:

In the whole well section of the target well, aiming at each well logging method, selecting the depth corresponding to the horizon with the smallest time domain gradient value as the optimal discrimination depth of the well logging method;

Selecting the same depth point in the optimal discrimination depths of the six well logging methods, and taking the depth point as the final matching depth if the depth point is one;

If the same depth points are more than two, selecting a horizon with the minimum time domain gradient value and a previous horizon, acquiring depth differences between the two corresponding horizons on a reference well as reference depth differences, respectively calculating the depth differences between the same depth points and the depth points of the previous horizon, and selecting the depth point closest to the reference depth difference in the depth differences as the final matching depth;

If the same depth point does not exist, respectively calling the logging data of the adjacent wells with the same horizon as the optimal discrimination depth to replace the logging data of the same horizon of the reference well to recalculate the optimal discrimination depth, and redefining the final matching depth according to the recalculated optimal discrimination depth;

If all the wells at the same horizon in the area are recalculated, the same depth point is not found yet, and the wells are treated according to faults.

3. The method of formation comparison according to claim 1 or 2, characterized in that:

normalizing the logging data to eliminate the influence of measurement units among different logging data, and reducing the influence of measurement noise on calculation of the amplitude deviation and the gradient deviation;

The normalization processing is to respectively normalize the logging data of the six logging methods of the reference well horizon window and the whole well section of the target well in a sliding window with a certain sliding window length so as to obtain corresponding normalized logging data.

4. A method of formation comparison according to claim 3, wherein the method of normalizing treatment comprises:

And removing the measured values of other measuring points of the corresponding logging method in the sliding window by using the maximum value of the logging data of the various logging methods in each sliding window to obtain normalized logging data of the various logging data in the sliding window.

5. The method of formation comparison of claim 4, wherein:

and calculating the amplitude deviation and the gradient deviation by using the normalized logging data.

6. A method of formation comparison according to any one of claims 3 to 5, wherein:

The calculation formula of the amplitude deviation is as follows:

The calculation formula of the gradient deviation is as follows:

The calculation formula of the time domain gradient value is as follows:

Wherein: x _i,y_i is the normalized log data of the i-th point of the target well and the reference well;

x _i-1、y_i-1 is normalized log data for the i-1 th point of the target well and the reference well; Normalizing the gradient of the change of the logging data for the target well and the reference well; m1 and m2 are weighting coefficients, and the value ranges are respectively m1:0.1 to 0.9, m2:0.9 to 0.1.

7. The method of formation comparison of claim 6, wherein:

and m1 and m2 are 0.5.