Natural gamma logging data processing method and device, medium and electronic equipment

Abstract

The embodiment of the invention provides a natural gamma logging data processing method and device, a medium and electronic equipment. The method comprises the steps that a first natural gamma curve measured before casing of a reference well and a second natural gamma curve measured after casing of the reference well are obtained, and the natural gamma curves are used for representing gamma ray intensity of different stratum depths in the well; based on the first natural gamma curve and the second natural gamma curve, a correlation model is fitted, and the correlation model is used for representing the correlation between the gamma ray intensity of the reference well before casing and the gamma ray intensity of the reference well after casing; a third natural gamma curve, measured after casing, of the target well is obtained; and based on the third natural gamma curve, a target natural gamma curve of the target well before casing is speculated through the correlation model. According to the invention, the integrity of natural gamma curve data in the well can be improved.

Description

Technical Field

[0001] This application relates to the field of geological exploration and data processing technology, and more specifically, to a natural gamma logging data processing method, apparatus, medium, and electronic equipment. Background Technology

[0002] Natural gamma ray logging (NGR) is a logging method for geological analysis that measures the natural radioactivity within a formation. Using a gamma ray detector, measurements are taken along the wellbore, and the resulting NGR curves can be used for formation correlation, classifying sandstone and mudstone, calculating clay content, identifying lithology, and evaluating source-reservoir-seal assemblages. Factors affecting NGR curves include formation thickness, logging rate, instrument time constant, instrument standardization, well parameters, and statistical fluctuation errors in the radioactive logging curves. Currently, many oilfields have completed large-scale drilling, but logging data is often incomplete, primarily due to the lack of in-well gamma ray data before casing (i.e., open-hole radioactivity data at different depths within the well). Because open-hole radioactivity data cannot be directly detected after casing, incomplete data exists in subsequent reservoir evaluation and multi-formation evaluation, affecting lithological identification and other studies within the well.

[0003] Therefore, improving the integrity of natural gamma curve data in wells is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] Embodiments of this application provide a natural gamma logging data processing method, apparatus, computer program product or computer program, computer-readable storage medium and electronic device, which can at least to some extent improve the integrity of natural gamma curve data in the well.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a natural gamma logging data processing method is provided. The method includes: acquiring a first natural gamma curve measured before casing of a reference well and a second natural gamma curve measured after casing of the reference well, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well; fitting a correlation model based on the first natural gamma curve and the second natural gamma curve, the correlation model being used to characterize the correlation between the gamma ray intensity of the reference well before casing and the gamma ray intensity of the reference well after casing; acquiring a third natural gamma curve measured after casing of a target well; and inferring a target natural gamma curve before casing of the target well based on the third natural gamma curve using the correlation model.

[0007] In some embodiments of this application, based on the foregoing scheme, before inferring the target natural gamma curve of the target well before casing based on the third natural gamma curve using the correlation model, the method further includes: acquiring first casing feature data of the reference well and second casing feature data of the target well; determining whether the target well and the reference well are similar wells based on the second casing feature data and the first casing feature data; and determining whether to modify the correlation model based on whether the target well and the reference well are similar wells.

[0008] In some embodiments of this application, based on the foregoing scheme, determining whether the target well and the reference well are similar wells according to the second casing feature data and the first casing feature data includes: calculating the similarity between the target well and the reference well according to the second casing feature data and the first casing feature data; if the similarity is lower than the preset threshold, then the target well and the reference well are determined to be similar wells; if the similarity is higher than or equal to the preset threshold, then the target well and the reference well are determined to be dissimilar wells.

[0009] In some embodiments of this application, based on the foregoing scheme, determining whether to modify the correlation model based on whether the target well and the reference well are similar wells includes: if the target well and the reference well are similar wells, then the correlation model is not modified; if the target well and the reference well are dissimilar wells, then the correlation model is modified based on the second casing feature data and the first casing feature data.

[0010] In some embodiments of this application, based on the foregoing scheme, the casing characteristic data includes one or more of the following: casing material, casing size, location and / or quantity of casing couplings, model and / or thickness of wellbore cement sheath, and well diameter.

[0011] In some embodiments of this application, based on the foregoing scheme, the method further includes: obtaining the target natural gamma curve of the target well before casing; and analyzing the lithological characteristics and reservoir development status of the target well at different formation depths based on the gamma ray intensity reflected by the target natural gamma curve at different formation depths, so as to predict the hydrocarbon distribution at different formation depths in the target well.

[0012] In some embodiments of this application, based on the foregoing scheme, the method is applied to predict the distribution of hydrocarbons at different formation depths in old wells, using natural gamma curve data of the old wells before the casing is removed.

[0013] According to one aspect of the embodiments of this application, a natural gamma logging data processing apparatus is provided. The apparatus includes: a first acquisition unit, configured to acquire a first natural gamma curve measured before casing of a reference well, and a second natural gamma curve measured after casing of the reference well, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well; a fitting unit, configured to fit a correlation model based on the first natural gamma curve and the second natural gamma curve, the correlation model being used to characterize the correlation between the gamma ray intensity of the reference well before casing and the gamma ray intensity of the reference well after casing; a second acquisition unit, configured to acquire a third natural gamma curve measured after casing of a target well; and an inference unit, configured to infer a target natural gamma curve of the target well before casing based on the third natural gamma curve using the correlation model.

[0014] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0015] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method described in the above embodiments.

[0016] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0017] Based on the technical solution proposed in this application, by acquiring the first natural gamma curve measured before casing and the second natural gamma curve measured after casing of a well without missing naked-eye radioactivity data, and fitting a correlation model to characterize the attenuation degree of radioactive rays in the formation by the casing, the target natural gamma curve before casing of the target well can be inferred based on the third natural gamma curve after casing of the target well through the correlation model, thereby improving the integrity of the natural gamma curve data in the target well.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0020] Figure 1 A flowchart of a natural gamma logging data processing method according to an embodiment of this application is shown;

[0021] Figure 2 A schematic diagram of a correlation model according to an embodiment of this application is shown;

[0022] Figure 3 A comparison diagram of gamma ray intensity before and after casing of a target well according to an embodiment of this application is shown;

[0023] Figure 4 A block diagram of a natural gamma logging data processing apparatus according to an embodiment of this application is shown.

[0024] Figure 5 A schematic diagram of the structure of an electronic device in an embodiment of this application is shown. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0026] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0029] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0031] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0032] In this application, it should first be stated that in the exploration and extraction of crude oil or natural gas, drilling is usually required to penetrate the formation and explore for and extract crude oil or natural gas within the formation. Furthermore, after drilling, casing needs to be reinforced in the well. It should be noted that adding casing to the well is to ensure the safety and efficiency of the drilling process and oil and gas production, such as preventing wellbore collapse, isolating the formation, and protecting drilling equipment.

[0033] During exploration, gamma-ray detectors are generally used to measure along the wellbore. The natural gamma curve data obtained can be used for stratigraphic correlation, classifying sandstone and mudstone, calculating mud content, identifying lithology, and evaluating source-reservoir-seal combinations. Furthermore, based on these measurements, the distribution of hydrocarbons (such as crude oil and natural gas) at different formation depths in the well can be predicted.

[0034] However, in practice, due to various reasons, some wells have incomplete natural gamma ray logging (NGR) data. This is mainly because when probing NGR data in wells, only certain specific formations were probed; specifically, only naked-eye radiometric data (i.e., gamma ray data before casing) was obtained from certain specific formations. However, as oil and gas field development progresses, it becomes necessary to strengthen the evaluation and deployment of multiple formations along the reservoir's vertical direction. At this point, because the casing in the well has been reinforced, it is impossible to directly obtain naked-eye radiometric data reflecting specific lithologies. This leads to a situation where there is a high demand for well data (e.g., naked-eye radiometric data from other formations in the well), but the data is incomplete, thus hindering better inter-well comparisons and lithology identification. Based on this, this application proposes a NGR logging data processing scheme for wells lacking naked-eye radiometric data to determine their naked-eye radiometric data and improve the completeness of NGR data in wells.

[0035] Figure 1 A flowchart of a natural gamma ray logging data processing method according to an embodiment of this application is shown. This natural gamma ray logging data processing method can be executed by a device with computational processing capabilities. (Refer to...) Figure 1 As shown, this natural gamma logging data processing method includes at least steps 110 to 140, which are detailed below:

[0036] In step 110, a first natural gamma curve of the reference well measured before casing and a second natural gamma curve of the reference well measured after casing are obtained, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well.

[0037] In this application, the inventors have considered that some drilled wells do not lack open-hole radiometric data (i.e., the first natural gamma curve measured before casing). Therefore, these wells can be used as reference wells. The first natural gamma curve measured before casing and the second natural gamma curve measured after casing of the reference wells are obtained. This data can then be used to infer the open-hole radiometric data in wells lacking open-hole radiometric data. Specifically, please refer to... Figure 1 Steps 120 to 140 in the process.

[0038] In step 120, a correlation model is fitted based on the first natural gamma curve and the second natural gamma curve. The correlation model is used to characterize the correlation between the gamma intensity of the reference well before casing and the gamma intensity of the reference well after casing.

[0039] To enable those skilled in the art to better understand this application, the following is combined with Figure 2 The following is an illustration using a specific example.

[0040] See Figure 2 The diagram illustrates a correlation model according to an embodiment of this application.

[0041] Based on the first and second natural gamma curves, the pre-casing gamma intensity (i.e., gamma intensity before casing) and post-casing gamma intensity (i.e., gamma intensity after casing) of the reference well can be determined, respectively. Specifically, this can be reflected in... Figure 2 The x and y coordinates (i.e., X and Y axis coordinates) of each point shown can be fitted to obtain the correlation model "y = 0.5509x + 1.3062", where "x" represents the gamma ray intensity before casing in the well and "y" represents the gamma ray intensity after casing in the well.

[0042] The inventors of this application considered that, after casing is installed in a well, the casing absorbs radioactive rays (such as gamma rays) emitted by the formation, leading to attenuation of the radioactive rays and thus a reduction in the intensity of the radioactive rays measured in the well. Therefore, this application uses a correlation model to fit a first natural gamma ray curve before casing and a second natural gamma ray curve after casing in a reference well. The resulting correlation model characterizes the correlation between the gamma ray intensity before and after casing in the reference well; that is, it uses the correlation model to characterize the degree of attenuation of radioactive rays emitted by the formation by the casing. This data can be used to subsequently infer naked-eye radioactivity data in wells lacking naked-eye radioactivity data. For details, please refer to... Figure 1 Steps 130 to 140 in the process.

[0043] In step 130, the third natural gamma curve of the target well, measured after casing, is obtained.

[0044] It should be noted that, in this application, the target well is the well for which naked-eye radiometric data is missing. Since the target well is located after the casing, the gamma ray intensity of the target well after the casing can be directly detected at any time. Therefore, the third natural gamma curve of the target well measured after the casing can be directly obtained, that is, the gamma ray intensity at different formation depths in the target well after the casing.

[0045] In step 140, based on the third natural gamma curve, the target natural gamma curve of the target well before the casing is inferred through the correlation model.

[0046] It is understood that, based on the third natural gamma curve, the target natural gamma curve of the target well before the casing can be inferred through the correlation model by directly substituting the gamma ray intensity of each formation depth in the third natural gamma curve into the correlation model determined above, thereby calculating the gamma ray intensity at different formation depths in the target well before the casing, and thus obtaining the target natural gamma curve of the target well before the casing.

[0047] Based on the technical solution proposed in this application, by acquiring the first natural gamma curve measured before casing and the second natural gamma curve measured after casing of a well without missing naked-eye radioactivity data, and fitting a correlation model to characterize the attenuation degree of radioactive rays in the formation by the casing, the target natural gamma curve before casing of the target well can be inferred based on the third natural gamma curve after casing of the target well through the correlation model, thereby improving the integrity of the natural gamma curve data in the target well.

[0048] Furthermore, the inventors of this application have considered that casings with different characteristics will attenuate radioactive rays in the formation to varying degrees. Based on this, this application further proposes the following natural gamma logging data processing scheme:

[0049] In this application, as Figure 1 Before step 140, i.e., before inferring the target natural gamma curve of the target well before casing based on the third natural gamma curve using the correlation model, the following steps 151 to 153 may also be performed:

[0050] Step 151: Obtain the first casing characteristic data of the reference well and the second casing characteristic data of the target well.

[0051] Step 152: Based on the second casing feature data and the first casing feature data, determine whether the target well and the reference well are similar wells.

[0052] Step 153: Determine whether to modify the correlation model based on whether the target well and the reference well are similar wells.

[0053] In this application, the casing feature data may include one or more of the following:

[0054] 1. Sleeve material (Material: Different sleeve materials absorb and scatter radioactive rays to varying degrees. Common sleeve materials include steel and aluminum, whose density and chemical composition affect the attenuation of radioactive rays; Thickness: The greater the sleeve thickness, the stronger the absorption and scattering of radioactive rays, thereby reducing the intensity of radioactive rays).

[0055] 2. Sleeve size (diameter: the diameter of the sleeve affects the propagation path and attenuation of radioactive rays. A larger diameter may cause more radioactive rays to be absorbed or scattered by more material before reaching the detector; length: the length of the sleeve also affects the propagation path of radioactive rays, especially over long distances, where radioactive rays may experience more attenuation).

[0056] 3. Sleeve couplings (coupling location: the location of sleeve couplings can cause changes in the intensity of local radioactive rays because the coupling is usually thicker or made of different materials than other parts of the sleeve; number of couplings: the more couplings there are, the more attenuation and scattering points the radioactive rays encounter during propagation, thus affecting the overall intensity).

[0057] 4. Wellbore Cement Sheath (Cement type: The density and uniformity of different cement sheath types will affect the propagation of radioactive rays. If the cement quality is uneven or there are voids, it will lead to uneven distribution of radioactive ray intensity; Cement thickness: The thickness of the cement sheath will also affect the degree of attenuation of radioactive rays, and a thicker cement sheath will increase the attenuation of radioactive rays).

[0058] 5. Well diameter (Well diameter variation: Variation in well diameter affects the propagation path and attenuation of radioactive rays. A larger well diameter may cause radioactive rays to be absorbed or scattered by more material before reaching the detector).

[0059] In summary, the influence of casing on the intensity of radioactive radiation in a well is multifaceted, including casing material, dimensions, couplings, cement sheath, and well diameter. These factors need to be considered when implementing the technical solution proposed in this application to ensure the accuracy and reliability of radioactive radiation prediction.

[0060] In step 152 above, that is, determining whether the target well and the reference well are similar wells based on the second casing feature data and the first casing feature data, can be performed according to the following steps 1521 to 1523:

[0061] Step 1521: Calculate the similarity between the target well and the reference well based on the second casing feature data and the first casing feature data.

[0062] Step 1522: If the similarity is lower than the preset threshold, then the target well and the reference well are determined to be similar wells.

[0063] Step 1523: If the similarity is higher than or equal to a preset threshold, then the target well and the reference well are determined to be dissimilar wells.

[0064] In one embodiment of this application, the similarity between the target well and the reference well is calculated based on the second casing feature data and the first casing feature data, which can be performed according to the following steps 15211 to 15212:

[0065] Step 15211: Calculate the absolute value of the difference between the feature values ​​of the same casing feature in the second casing feature data and the first casing feature data.

[0066] Step 15212: Summate the absolute values ​​of the feature value differences of each casing feature, and determine the reciprocal of the sum as the similarity between the target well and the reference well.

[0067] Specifically, for example, taking the sleeve material as an example, since iron has a significantly higher absorption capacity for gamma rays than aluminum, while steel has a slightly higher absorption capacity for gamma rays than iron, the characteristic value of steel can be defined as 8, the characteristic value of iron as 7, and the characteristic value of aluminum as 3. If the sleeve material in the second sleeve characteristic data is iron and the sleeve material in the first sleeve characteristic data is steel, then the absolute value of the difference between the characteristic values ​​of the second sleeve characteristic data and the first sleeve characteristic data on the sleeve material is 1.

[0068] For example, taking the well wall cement sheath as an example, since the greater the cement thickness, the higher the absorption capacity of gamma rays, we can define the characteristic value of cement thickness as 5 for about 0.5 meters and the characteristic value of cement thickness as 6 for about 0.6 meters. If the cement thickness in the second casing characteristic data is 0.5 meters and the cement thickness in the first casing characteristic data is 0.6 meters, then the absolute value of the difference between the characteristic values ​​of cement thickness in the second casing characteristic data and the first casing characteristic data is 1.

[0069] It is understandable that the smaller the sum of the absolute values ​​of the differences in the characteristic values ​​of each casing feature, the greater the similarity between the target well and the reference well.

[0070] In other embodiments of this application, the similarity between the target well and the reference well is calculated based on the second casing feature data and the first casing feature data, and can also be performed according to the following steps 15213 to 15214:

[0071] Step 15213: Calculate the absolute value of the difference between the feature values ​​of the same casing feature in the second casing feature data and the first casing feature data.

[0072] Step 15214: Count the number of casing features whose absolute value of the feature value difference is greater than a preset absolute value threshold, and determine the similarity between the target well and the reference well based on the number of casing features.

[0073] Understandably, the smaller the number of casing features, the greater the similarity between the target well and the reference well. Conversely, the larger the number of casing features, the smaller the similarity between the target well and the reference well.

[0074] It should be noted that, for those skilled in the art, under the inventive concept of the above embodiments, there are many ways to calculate the similarity between the target well and the reference well based on the second casing feature data and the first casing feature data, and it is not limited to those listed above.

[0075] In step 153 above, that is, determining whether to modify the correlation model based on whether the target well and the reference well are similar wells, it can be performed according to the following steps 1531 to 1532:

[0076] Step 1531: If the target well and the reference well are similar wells, then the correlation model is not modified.

[0077] Step 1532: If the target well and the reference well are dissimilar wells, then the correlation model is corrected based on the second casing feature data and the first casing feature data.

[0078] In this application, if the target well and the reference well are similar wells, since the casing in similar wells weakens radioactive rays in the formation to a similar degree, it is not necessary to modify the correlation model. The target natural gamma curve of the target well before the casing can be directly inferred through the correlation model.

[0079] If the target well and the reference well are dissimilar wells, the correlation model needs to be modified because the attenuation of radioactive rays in the formation by the casing in dissimilar wells differs significantly. The target natural gamma curve of the target well before the casing can then be inferred using the modified correlation model.

[0080] In this application, taking the correlation model "y = 0.5509x + 1.3062" in the above embodiment as an example, since "x" represents the pre-casing gamma ray intensity in the well and "y" represents the post-casing gamma ray intensity in the well, if the target well and the reference well are dissimilar wells, it is necessary to adjust the parameter values ​​"0.5509" and / or "1.3062" in the correlation model "y = 0.5509x + 1.3062" to correct the correlation model.

[0081] Based on the technical solution proposed in this application, if the target well and the reference well are dissimilar wells, before inferring the target natural gamma curve of the target well before the casing based on the third natural gamma curve and the correlation model, the correlation model is corrected based on the second casing feature data and the first casing feature data, which can ensure the accuracy and reliability of the radioactive ray inference.

[0082] In this application, after speculating the target natural gamma curve of the target well before the casing, the following steps 161 to 162 may also be performed:

[0083] Step 161: Obtain the target natural gamma curve of the target well before casing.

[0084] Step 162: Based on the gamma ray intensity at different formation depths in the target well as reflected by the target natural gamma curve, analyze the lithological characteristics and reservoir development status at different formation depths in the target well to predict the hydrocarbon distribution at different formation depths in the target well.

[0085] To enable those skilled in the art to better understand this application, the following uses a target well as an example, combined with... Figure 3 The following is an illustration using a specific example.

[0086] See Figure 3 The diagram shows a comparison of gamma ray intensity before and after casing of a target well according to an embodiment of the present application.

[0087] like Figure 3 Neutron plot (a) shows the gamma ray curve of the target well after casing, as shown. Figure 3 Neutron plot (b) shows the gamma-ray curves of the target well before the casing. It can be seen that the gamma-ray intensity at various formation depths before the casing in the target well is greater than the gamma-ray intensity at various formation depths after the casing. Specifically, according to the gamma-ray curves before the casing in the target well, the gamma-ray intensity is higher at formation depths of approximately 3750 meters to 3950 meters (e.g., ...). Figure 3 Therefore, given that region A is located at this depth, it can be inferred that there may be large reserves of hydrocarbons, such as crude oil.

[0088] In actual production, for older wells with long history of crude oil or natural gas extraction, there is often a lack of open-hole radiometric data due to various reasons. Specifically, the natural gamma ray logging data before casing is missing. This affects the identification of lithology within the well, and consequently, the exploration of whether new crude oil or natural gas resources exist in the older well. Therefore, the natural gamma ray logging data processing scheme proposed in this application can be applied to production scenarios that predict the distribution of hydrocarbons at different formation depths in older wells, thereby providing support for the exploration of whether new crude oil or natural gas resources exist in older wells.

[0089] Specifically, the natural gamma logging data processing scheme proposed in this application is applied to production scenarios where hydrocarbon distribution at different formation depths in old wells is predicted. The specific implementation method can be referred to in the following embodiment, which includes steps 1 to 2:

[0090] Step 1: For an old well in the work area that has missing naked-eye radiometric data, obtain the third natural gamma curve of the old well measured after the casing. Based on the casing characteristic data of the old well, select a reference well in the work area that is similar to the old well, wherein the reference well does not have missing naked-eye radiometric data.

[0091] Step 2: Obtain the first natural gamma curve of the reference well before casing and the second natural gamma curve of the reference well after casing.

[0092] Step 3: Fit a correlation model based on the first natural gamma curve and the second natural gamma curve.

[0093] Step 4: Based on the third natural gamma curve, infer the target natural gamma curve of the old well before casing using the correlation model.

[0094] Step 5: Based on the target natural gamma curve of the old well before casing, evaluate and deploy multiple strata in the vertical direction of the reservoir, study the lithological characteristics of the old well, and predict the distribution of hydrocarbons at different strata depths in the old well.

[0095] Therefore, in practical production applications, for wells with missing naked-eye radioactivity data, the technical solution proposed in this application can be used to infer the naked-eye radioactivity data in the well, improve the integrity of the natural gamma curve data in the well, and at the same time ensure the accuracy and reliability of the natural gamma curve data in the well.

[0096] The following describes an embodiment of the apparatus described in this application, which can be used to execute the natural gamma logging data processing method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the natural gamma logging data processing method described above in this application.

[0097] Figure 4 A block diagram of a natural gamma logging data processing apparatus according to an embodiment of this application is shown.

[0098] Reference Figure 4 As shown, a natural gamma logging data processing device 400 according to an embodiment of this application includes: a first acquisition unit 401, a fitting unit 402, a second acquisition unit 403, and an inference unit 404.

[0099] The system includes a first acquisition unit 401, which acquires a first natural gamma curve measured before casing of a reference well and a second natural gamma curve measured after casing of the reference well, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well; a fitting unit 402, which fits a correlation model based on the first and second natural gamma curves, wherein the correlation model is used to characterize the correlation between the gamma ray intensity of the reference well before casing and the gamma ray intensity of the reference well after casing; a second acquisition unit 403, which acquires a third natural gamma curve measured after casing of a target well; and an inference unit 404, which infers the target natural gamma curve before casing of the target well based on the third natural gamma curve and the correlation model.

[0100] In some embodiments of this application, based on the foregoing scheme, the apparatus further includes: a judgment unit, configured to acquire first casing feature data of the reference well and second casing feature data of the target well before inferring the target natural gamma curve of the target well before casing based on the third natural gamma curve and the correlation model; determine whether the target well and the reference well are similar wells based on the second casing feature data and the first casing feature data; and determine whether to correct the correlation model based on whether the target well and the reference well are similar wells.

[0101] In some embodiments of this application, based on the foregoing scheme, the determination unit is configured to: calculate the similarity between the target well and the reference well based on the second casing feature data and the first casing feature data; if the similarity is lower than the preset threshold, then determine that the target well and the reference well are similar wells; if the similarity is higher than or equal to the preset threshold, then determine that the target well and the reference well are dissimilar wells.

[0102] In some embodiments of this application, based on the foregoing scheme, the determination unit is configured as follows: if the target well and the reference well are similar wells, then the correlation model is not modified. If the target well and the reference well are dissimilar wells, then the correlation model is modified based on the second casing feature data and the first casing feature data.

[0103] In some embodiments of this application, based on the foregoing scheme, the casing characteristic data includes one or more of the following: casing material, casing size, location and / or quantity of casing couplings, model and / or thickness of wellbore cement sheath, and well diameter.

[0104] In some embodiments of this application, based on the foregoing scheme, the apparatus further includes: a prediction unit, used to acquire the target natural gamma curve of the target well before casing; and to analyze the lithological characteristics and reservoir development status of the target well at different formation depths based on the gamma ray intensity reflected by the target natural gamma curve, so as to predict the hydrocarbon distribution at different formation depths in the target well.

[0105] In some embodiments of this application, based on the foregoing scheme, the device is used to predict the distribution of hydrocarbons at different formation depths in old wells, specifically the natural gamma curve data of the old wells before the casing is removed.

[0106] As another embodiment of this application, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0107] Based on the same inventive concept, embodiments of this application also provide an electronic device. (Reference) Figure 5 The diagram shows a schematic of the structure of an electronic device according to an embodiment of this application. The electronic device includes one or more memories 504, one or more processors 502, and at least one computer program (program code) stored in the memory 504 and executable on the processor 502. When the processor 502 executes the computer program, it implements the method described above.

[0108] Among them, Figure 5In this document, a bus architecture (represented by bus 500) is used. Bus 500 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 can be used to store data used by processor 502 during operation.

[0109] As another embodiment of this application, a computer-readable storage medium is also provided. This computer-readable storage medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0110] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium, including instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0114] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing natural gamma-ray logging data, characterized in that, The method includes: Acquire a first natural gamma curve of the reference well before casing and a second natural gamma curve of the reference well after casing, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well. Based on the first natural gamma curve and the second natural gamma curve, a correlation model is fitted. The correlation model is used to characterize the correlation between the gamma intensity of the reference well before casing and the gamma intensity of the reference well after casing. Obtain the third natural gamma curve of the target well measured after casing; Based on the third natural gamma curve, the target natural gamma curve of the target well before casing is inferred through the correlation model.

2. The method according to claim 1, characterized in that, Before inferring the target natural gamma curve of the target well before casing based on the third natural gamma curve using the correlation model, the method further includes: Obtain the first casing characteristic data of the reference well and the second casing characteristic data of the target well; Based on the second casing feature data and the first casing feature data, it is determined whether the target well and the reference well are similar wells; Whether the correlation model needs to be modified depends on whether the target well and the reference well are similar wells.

3. The method according to claim 2, characterized in that, The step of determining whether the target well and the reference well are similar wells based on the second casing feature data and the first casing feature data includes: Based on the second casing feature data and the first casing feature data, the similarity between the target well and the reference well is calculated; If the similarity is lower than the preset threshold, the target well and the reference well are determined to be similar wells; If the similarity is higher than or equal to a preset threshold, the target well and the reference well are determined to be dissimilar wells.

4. The method according to claim 2, characterized in that, The step of determining whether to modify the correlation model based on whether the target well and the reference well are similar wells includes: If the target well and the reference well are similar wells, then the correlation model will not be modified; If the target well and the reference well are dissimilar wells, the correlation model is modified based on the second casing feature data and the first casing feature data.

5. The method according to claim 2, characterized in that, Casing characteristic data includes one or more of the following: casing material, casing size, location and / or quantity of casing couplings, type and / or thickness of wellbore cement sheath, and well diameter.

6. The method according to claim 1, characterized in that, The method further includes: Obtain the target natural gamma curve of the target well before casing; Based on the gamma ray intensity reflected by the target natural gamma curve at different formation depths in the target well, the lithological characteristics and reservoir development status at different formation depths in the target well are analyzed to predict the distribution of hydrocarbons at different formation depths in the target well.

7. The method according to any one of claims 1 to 6, characterized in that, The method is applied to predict hydrocarbon distribution at different formation depths in old wells, using natural gamma curve data from before casing loss.

8. A natural gamma logging data processing device, characterized in that, The device includes: The first acquisition unit is used to acquire a first natural gamma curve measured before casing of the reference well and a second natural gamma curve measured after casing of the reference well, wherein the natural gamma curve is used to characterize the gamma ray intensity at different formation depths in the well. A fitting unit is used to fit a correlation model based on the first natural gamma curve and the second natural gamma curve. The correlation model is used to characterize the correlation between the gamma intensity of the reference well before casing and the gamma intensity of the reference well after casing. The second acquisition unit is used to acquire the third natural gamma curve of the target well measured after casing. The inference unit is used to infer the target natural gamma curve of the target well before casing based on the third natural gamma curve and through the correlation model.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations performed by the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operation performed by the method as described in any one of claims 1 to 7.

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