A well gas channeling identification method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a production well gas channeling identification method and device, electronic equipment and storage medium, the method comprising: determining a target production well and at least one candidate gas injection well having a corresponding relationship with the target production well; dividing the target production well into multiple intervals according to well depth, each interval forming an identification unit with each candidate gas injection well; obtaining multi-source data related to the target production well and the candidate gas injection well at a current sampling time; determining a first risk score corresponding to each identification unit, a second risk score corresponding to each candidate gas injection well, and a third risk score corresponding to each interval based on the multi-source data; determining a comprehensive score corresponding to each identification unit based on the first risk score, the second risk score, and the third risk score, and the comprehensive score is used to determine a target candidate gas injection well and a target interval causing gas channeling in the target production well.

Description

Technical Field

[0001] This disclosure relates to the field of gas channeling identification technology, and in particular to a method, apparatus, electronic device and storage medium for identifying gas channeling in production wells. Background Technology

[0002] Because natural gas has low viscosity and high mobility, and reservoirs generally exhibit strong heterogeneity, injected gas tends to preferentially penetrate along high-permeability bands, fractures, or other advantageous channels within the formation, leading to premature gas breakthrough in production wells and resulting in gas channeling. Gas channeling causes ineffective circulation of injected gas, a rapid increase in the gas-oil ratio in production wells, a decrease in oil production, and a significant reduction in sweep efficiency and ultimate recovery rate.

[0003] Existing technologies have conducted extensive research on gas channeling identification, but most methods focus on determining whether gas channeling has occurred or classifying the intensity of gas channeling, making it difficult to further identify the main source of gas channeling in the target production well. Existing methods often fail to accurately locate the main channeling zone within the production well, thus failing to directly support precise near-wellbore sealing and segmented treatment design. Some methods rely on tracers or specialized downhole tests, which are costly and time-consuming to implement, limiting their field application. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for identifying gas channeling in production wells, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0005] According to a first aspect of this disclosure, a method for identifying gas channeling in production wells is provided, the method comprising: Identify the target production well and at least one candidate injection well that corresponds to the target production well; The target production well is divided into multiple segments according to its depth, and each segment and each candidate gas injection well form an identification unit. Acquire multi-source data related to the target production well and the candidate gas injection well at the current sampling time; Based on the multi-source data, a first risk score is determined for each identification unit, a second risk score is determined for each candidate gas injection well, and a third risk score is determined for each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel. The second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well. The third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well. Based on the first risk score, the second risk score, and the third risk score, a comprehensive score is determined for each identification unit. The comprehensive score is used to determine the target candidate gas injection well and the target layer that cause gas channeling in the target production well.

[0006] In one possible implementation, after acquiring multi-source data related to the target production well and the candidate injection well at the current sampling time, the method further includes: Based on the multi-source data, the dynamic breakthrough index of the target production well at the current sampling time is determined, and the dynamic breakthrough index is used to determine the severity of gas channeling in the target production well. Determine whether the dynamic breakthrough index is greater than the first threshold within the preset sampling period; If the dynamic breakthrough index is greater than the first threshold within a preset sampling period, then the target candidate gas injection well and target layer that caused the gas channeling of the target production well are determined.

[0007] In one possible implementation, determining the dynamic breakthrough index of the target production well at the current sampling time based on the multi-source data includes: Based on the multi-source data, the gas-oil ratio, gas production, oil production, and natural gas component content in the produced gas of the target production well are determined. Based on the gas-oil ratio, gas production, oil production, and natural gas component content in the produced gas of the target production well, determine the dynamic breakthrough index of the target production well at the current sampling time.

[0008] In one possible implementation, determining the first risk score corresponding to each identification unit based on the multi-source data includes: Based on the multi-source data, determine the heterogeneity data, effective connectivity thickness data, inter-well channel matching data, structural potential difference data, and perforation and layer matching data between the target production well and any candidate gas injection well. Based on the heterogeneity data between the target production well and any candidate injection well, the effective connectivity thickness data, the inter-well channel matching data, the structural potential difference data, and the perforation and layer matching data, the first risk score corresponding to each identification unit is determined.

[0009] In one possible implementation, based on the multi-source data, a second risk score is determined for each candidate gas injection well, including: Based on the multi-source data, determine the dynamic changes of the candidate gas injection wells, as well as the changes in the gas-oil ratio, gas production, and gas composition of the target production well within multiple different preset time lags. The correlation between the dynamic changes of candidate injection wells and the changes in gas-oil ratio, gas production, and gas composition of target production wells at different preset time lag times was determined. Based on the correlation within different preset time delays, the first sub-risk score, the second sub-risk score, and the third sub-risk score are determined respectively corresponding to the changes in the gas-oil ratio, the changes in gas production, and the changes in the composition of produced gas of the target production well. Based on the first sub-risk score, the second sub-risk score, and the third sub-risk score, the second risk score corresponding to each candidate gas injection well is determined.

[0010] In one possible implementation, based on the multi-source data, a third risk score corresponding to each segment is determined, including: Based on the multi-source data, the gas production of each layer, the gas-oil ratio of each layer, the temperature anomaly and pressure anomaly of each layer, as well as the matching degree data of well logging interpretation results or numerical simulation are determined. Based on the gas production of each segment, the gas-oil ratio of each segment, the temperature anomaly and pressure anomaly of each segment, and the matching degree data of well logging interpretation results or numerical simulation, the third risk score corresponding to each segment is determined.

[0011] In one possible implementation, determining at least one candidate gas injection well that corresponds to the target production well includes: Determine whether the target production well and the surrounding gas injection wells are in the same development stratum, have the same injection-production relationship, or whether the distance between the gas injection well and the target production well is less than a preset distance; If a gas injection well meets any of the conditions, then the gas injection well is considered a candidate gas injection well that corresponds to the target production well.

[0012] According to a second aspect of this disclosure, a production well gas channeling identification device is provided, the device comprising: The first determining module is used to determine the target production well and at least one candidate gas injection well that corresponds to the target production well. The segmentation module is used to divide the target production well into multiple segments according to the well depth, and each segment and each candidate gas injection well form an identification unit. The acquisition module is used to acquire multi-source data related to the target production well and the candidate gas injection well at the current sampling time; The second determining module is used to determine, based on the multi-source data, a first risk score corresponding to each identification unit, a second risk score corresponding to each candidate gas injection well, and a third risk score corresponding to each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel. The second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well. The third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well. The third determining module is used to determine a comprehensive score corresponding to each identification unit based on the first risk score, the second risk score and the third risk score. The comprehensive score is used to determine the target candidate gas injection well and the target layer that cause gas channeling in the target production well.

[0013] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0014] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this disclosure.

[0015] This disclosure discloses a method, device, electronic equipment, and storage medium for identifying gas channeling in production wells. By constructing an identification unit consisting of a target production well, a candidate injection well, and a layer, gas channeling identification is refined from the traditional overall well group scale to the layer scale. Combined with multi-source data, it calculates a first risk score, a second risk score, and a third risk score, achieving a multi-dimensional evaluation of static geological conditions, dynamic response characteristics, and stratified channeling anomalies. The results are then integrated to obtain a comprehensive score, enabling precise location of the gas-channeling-dominant injection well and the main channeling layer, improving the accuracy and targeting of gas channeling identification. The identification results can directly support near-wellbore sealing and segmented treatment without relying on high-cost testing, reducing the difficulty and time required for on-site implementation, and providing reliable technical support for gas control and oil production enhancement in natural gas injection reservoirs.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0017] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0018] Figure 1 A flowchart of a production well gas channeling identification method provided in this embodiment of the disclosure; Figure 2 A schematic diagram of the structure of the production well gas channeling identification device provided in this embodiment of the disclosure; Figure 3 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0020] Based on this, the present disclosure provides a method for identifying gas channeling in production wells. Figure 1 A flowchart of the gas channeling identification method for production wells provided in this disclosure embodiment is shown below. Figure 1 As shown, the method includes: Step 101: Determine the target production well and at least one candidate injection well that corresponds to the target production well.

[0021] In one embodiment, determining at least one candidate gas injection well that corresponds to the target production well includes: Determine whether the target production well and the surrounding gas injection wells are in the same development stratum, have the same injection-production relationship, or whether the distance between the gas injection well and the target production well is less than a preset distance; If a gas injection well meets any of the conditions, then the gas injection well is considered a candidate gas injection well that corresponds to the target production well.

[0022] Specifically, a production well is first selected as the target production well. Then, with the target production well as the center, candidate gas injection wells are screened according to the same development stratum, the same injection-production relationship, or a certain spatial range. Gas injection wells located in the same main sand body, the same injection-production unit, or within a preset distance from the target production well are selected as candidate gas injection wells.

[0023] In this disclosure, candidate injection wells are first screened from multiple injection wells because not all injection wells usually affect the target production well. If screening is not done at the beginning, the amount of analysis will be greatly increased later, and it is easy to bring uninterested injection wells into the identification process, reducing the accuracy of judgment.

[0024] Step 102: Divide the target production well into multiple segments according to the well depth, and each segment and each candidate gas injection well form an identification unit.

[0025] Assuming the target production well is P1, there are three candidate production wells corresponding to P1: G1, G2, and G3. P1 is divided into four segments, A1-A4, according to its depth. Based on the correspondence between "candidate injection well - target production well - segment," a total of 12 identification units are constructed: G1-P1-A1, G1-P1-A2, G1-P1-A3, G1-P1-A4, G2-P1-A1 to G3-P1-A4.

[0026] This disclosure refines the identification scale from the well group level to the stratigraphic level, providing a foundation for subsequent source well attribution and main stratigraphic location.

[0027] Step 103: Obtain multi-source data related to the target production well and candidate gas injection well at the current sampling time.

[0028] In this embodiment, the multi-source data to be collected includes three types of data: static geological data, dynamic production data, and stratigraphic data. Static geological data includes, but is not limited to: stratigraphic location division, sand body connectivity, effective connectivity thickness, porosity, permeability, permeability gradient, interlayer thickness, fracture development, well location relationships, tectonic potential differences, and perforated well sections. Dynamic production data includes, but is not limited to: gas injection volume, injection pressure, injection time, and shutdown time of candidate gas injection wells, as well as daily oil production, daily gas production, gas-oil ratio, wellhead pressure, casing pressure, flowing pressure, water cut, and produced gas composition of target production wells. Stratigraphic data includes, but is not limited to: stratigraphic fluid production profile, stratigraphic gas production profile, gas intake profile, well logging interpretation results, well temperature anomalies, well pressure anomalies, and, if necessary, numerical simulation stratigraphic results.

[0029] After the data is collected, data from different sources are first unified to the same well number, the same layer number, and the same time scale; daily, weekly, and monthly data are resampled according to a unified period; missing values ​​are processed by interpolation, completion of adjacent periods, or removal; obvious outliers are processed by box plot discrimination, 3σ criterion, or manual verification; and dynamic curves with large noise are smoothed by 3-point, 5-point, or 7-point moving averages.

[0030] In one embodiment, after acquiring multi-source data related to the target production well and candidate injection wells at the current sampling time, the method further includes: Based on multi-source data, the dynamic breakthrough index of the target production well at the current sampling time is determined. The dynamic breakthrough index is used to determine the severity of gas channeling in the target production well. Determine whether the dynamic breakthrough index is greater than the first threshold within the preset sampling period; If the dynamic breakthrough index is greater than the first threshold within the preset sampling period, the target candidate gas injection well and target layer that caused the gas channeling of the target production well are identified.

[0031] After data acquisition, instead of immediately searching for candidate injection wells that caused gas channeling in the target production well, the first step is to determine the current stage of the target production well. This is because in actual field conditions, an increased gas-oil ratio in a target production well does not necessarily indicate severe gas channeling; it could simply be the effect of normal gas injection, localized enhanced spillover, or short-term operational fluctuations. Therefore, this disclosure first identifies the stage of the target production well, determining whether it is currently in the normal production stage, the gas breakthrough stage, or the severe gas channeling stage. Only when the target production well is in the gas breakthrough stage or the severe gas channeling stage will the search for candidate injection wells and target formations that caused the gas channeling in the target production well continue.

[0032] In one embodiment, determining the dynamic breakthrough index of the target production well at the current sampling time based on multi-source data includes: Based on multi-source data, the gas-oil ratio, gas production, oil production, and natural gas component content in the produced gas of the target production well were determined. Based on the gas-oil ratio, gas production, oil production, and natural gas composition content in the produced gas of the target production well, determine the dynamic breakthrough index of the target production well at the current sampling time.

[0033] Specifically, the dynamic breakout index is determined using the following formula:

[0034] in, The dynamic breakthrough index of the target production well at time t. The gas-oil ratio of the target production well at time t. The content of natural gas components in the produced gas of the target production well at time t. Let the target production well produce gas at time t. The average gas production during the baseline phase, Let be the oil production of the target production well at time t. The average oil production during the baseline period, , , , These are the weighting coefficients.

[0035] In practice, the threshold of the dynamic breakthrough index can be determined first using historical normal wells and known gas channeling well samples. When the dynamic breakthrough index exceeds the first threshold for consecutive preset sampling periods, it is determined to be in the gas breakthrough stage; if it continues to exceed a higher threshold and is accompanied by a continuous decline in oil production, it is determined to be in the severe gas channeling stage. Only by first clarifying whether the target production well has truly entered the gas breakthrough stage or the severe gas channeling stage can the subsequent attribution of the source well and the location of the formation have a clear target. Otherwise, if the target production well itself is still in the normal production stage, rashly attempting to plug it may not only lead to misjudgment but also affect the normal development effect.

[0036] Step 104: Based on multi-source data, determine the first risk score corresponding to each identification unit, the second risk score corresponding to each candidate gas injection well, and the third risk score corresponding to each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel. The second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well. The third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well.

[0037] In one embodiment, determining a first risk score corresponding to each identification unit based on multi-source data includes: Based on multi-source data, determine the heterogeneity data, effective connectivity thickness data, inter-well channel matching data, structural potential difference data, and perforation and layer matching data between the target production well and any candidate injection well; Based on the heterogeneity data between the target production well and any candidate injection well, the effective connectivity thickness data, the inter-well channel matching data, the structural potential difference data, and the perforation and layer matching data, the first risk score corresponding to each identification unit is determined.

[0038] Specifically, for each identification unit, static geological data of its corresponding layer is obtained, and after standardizing these data, its first risk score is calculated.

[0039] The first risk score can be calculated using the following formula:

[0040] in, The first risk score of candidate gas injection well i and target production well j in interval k; The data is heterogeneous. To effectively connect thickness data; Matching data for the inter-well passage; To construct potential difference data; This is perforation and stratigraphic matching data; These are the weighting coefficients for each data point.

[0041] Heterogeneity data refers to the degree of inhomogeneity of reservoir rock physical properties between candidate injection well i and target production well j within the identification unit k. Heterogeneity data includes: permeability, the difference between the highest and lowest permeability grades, the degree of fracture growth (density, aperture, extension, and shape), the scale of high-permeability bands (thickness and width, inter-well influence), and the thickness of interlayers between layers. If a layer exhibits a significant permeability grade difference, contains high-permeability bands, and has weak interlayers, the score for this indicator will be high, indicating that the layer is prone to forming a dominant gas flow channel.

[0042] In practice, if a certain layer simultaneously possesses the following characteristics: injection and production wells are located within the same main sand body, effective connectivity is large, permeability is high with significant grade differences, the structure is structurally favorable for gas upflow, interlayers are weak, and the perforation layers of the two wells have a strong correspondence, then the first risk score of this identification unit will be high, indicating that it is more likely to form a gas-dominant flow channel under geological conditions. Conversely, if the interlayers have thick interlayers, poor effective connectivity, large well-to-well distances, and low permeability, then its first risk score will be low.

[0043] In one embodiment, a second risk score is determined for each candidate gas injection well based on multi-source data, including: Based on multi-source data, the dynamic changes of candidate gas injection wells, as well as the changes in gas-oil ratio, gas production, and gas composition of target production wells are determined within multiple different preset time delays. The correlation between the dynamic changes of candidate injection wells and the changes in gas-oil ratio, gas production, and gas composition of target production wells at different preset time lag times was determined. Based on the correlation within different preset time delays, the first sub-risk score, the second sub-risk score, and the third sub-risk score are determined respectively corresponding to the changes in the gas-oil ratio, the changes in gas production, and the changes in the composition of produced gas of the target production well. Based on the first sub-risk score, the second sub-risk score, and the third sub-risk score, the second risk score corresponding to each candidate gas injection well is determined.

[0044] In practice, there is often more than one injection well around the target production well. In many cases, several injection wells may be connected to the target production well in some way. Therefore, it is necessary to identify which candidate injection well contributes the most to the gas channeling anomaly of the target production well. In this disclosure, the dynamic changes of each candidate injection well (including changes in injection volume, injection pressure, and start / stop injection time) are extracted and compared with the changes in gas-oil ratio, gas production volume, and produced gas composition of the target production well.

[0045] Since changes in candidate injection wells are not immediately reflected in the target production wells, there is usually a certain time lag between them. Therefore, it is necessary to calculate the correlation between the target production well and each candidate injection well one by one within several preset time lags and find the lag time with the strongest correlation.

[0046] For example, if the time lag interval is set to 1-4 months, the preset time lag times can be 1 month, 2 months, 3 months, and 4 months. The correlation between the target production well and each candidate injection well is calculated within 1 month, 2 months, 3 months, and 4 months respectively to determine the time lag time corresponding to the highest correlation. Assuming that the target production well P1 and the candidate injection well G1 have the highest correlation at a time lag time of 2 months, the second risk score is determined based on the correlation corresponding to the time lag time of 2 months.

[0047] Specifically, the correlation between the dynamic changes of candidate injection wells and the gas-oil ratio changes of target production wells, and the first sub-risk score are determined using the following formula:

[0048]

[0049] in, Candidate gas injection wells With the target production well The change in the gas-oil ratio in time lag The correlation below, For candidate gas injection well i in time delay The dynamic changes within. The change in the gas-oil ratio of the target production well j. The first sub-risk score of candidate gas injection well i. This represents the time-delay decay coefficient. It is understandable that the second sub-risk score can be obtained using the same method. and third-child risk score .

[0050] Specifically, the second sub-risk score is obtained using the following formula. and third-child risk score :

[0051]

[0052]

[0053]

[0054] in, Candidate gas injection wells With the target production well The change in gas production in time lag The correlation below, Let be the change in gas production of the target production well j. Candidate gas injection wells With the target production well The change in the composition of the produced gas in time lag The correlation below, The change in the composition of the produced gas from the target production well j.

[0055] Then, the first, second, and third sub-risk scores are weighted and fused to obtain the second risk score. In practice, the candidate injection well with the highest second risk score is likely to be the target candidate injection well that causes gas channeling in the target production well.

[0056] In this disclosure, to avoid misjudgment based on a single indicator, the identification of candidate gas injection wells that cause gas channeling in the target production well is preferably based on multiple dynamic signals, rather than simply looking at the dynamic changes of the candidate gas injection wells and the gas-oil ratio of the target production well. For example, the dynamic changes of the candidate gas injection wells can be analyzed in multiple dimensions, such as the changes in the gas-oil ratio, gas production, and gas composition of the target production well. If a gas injection well is not only highly correlated with the gas-oil ratio changes of the target production well, but also shows a similar lag in the gas composition of the target well after gas injection adjustments, then the credibility of this gas injection well as the source well will be significantly higher.

[0057] Here, after calculating the second risk score of different candidate gas injection wells to the abnormal response of the target production well, the candidate gas injection wells are ranked according to the size of the second risk score, and the dominant gas channeling source well and possible multi-source interaction relationships are identified.

[0058] For example, if the second risk score for candidate injection well G1 is 0.79, for well G2 it is 0.53, and for well G3 it is 0.41, and the difference in the second risk scores between well G1 and both wells G2 and G3 is greater than a preset difference of 0.1, then well G1 can be considered the target candidate injection well causing gas channeling in the target production well, and the problem is mainly caused by well G1, with the other two candidate injection wells having negligible impact. However, if the second risk score for well G1 is 0.79, for well G2 it is 0.73, and for well G3 it is 0.41, and the difference in the second risk scores between well G1 and G2 is less than a preset difference of 0.1, then although well G1 is the target candidate injection well causing gas channeling in the target production well, well G2 can be considered a secondary factor, and the gas channeling in the target production well can be seen as a result of the combined effects of wells G1 and G2.

[0059] In one embodiment, determining a third risk score corresponding to each segment based on multi-source data includes: Based on multi-source data, determine the gas production of each layer, the gas-oil ratio of each layer, the temperature anomaly and pressure anomaly of each layer, as well as the matching degree data of well logging interpretation results or numerical simulation. Based on the gas production of each segment, the gas-oil ratio of each segment, the temperature anomaly and pressure anomaly of each segment, and the matching degree data of well logging interpretation results or numerical simulation, the third risk score corresponding to each segment is determined.

[0060] Specifically, the third risk score can be determined using the following formula:

[0061] in, Target production well The third risk score on segment k; Let k be the gas production rate of section k. The gas-oil ratio for section k; This is an abnormal temperature value; This is an abnormal pressure value; For well logging interpretation results or numerical simulation matching degree data, These are the weighting coefficients for each item. Among them, the temperature anomaly refers to the difference between the actual measured fluid temperature at segment k of the target production well j and the normal reference temperature below that segment; the pressure anomaly refers to the difference between the actual measured flow pressure at segment k of the target production well j and the normal pressure of that segment when gas channeling has not occurred (or under the average condition of the entire well). The well logging interpretation results or numerical simulation matching degree data refers to the degree of mutual verification between the original formation conditions identified using physical exploration technology (well logging) or the underground fluid movement trajectory deduced using computer software (numerical simulation) and the actual abnormal conditions (such as sudden increases in gas production, sudden changes in temperature and pressure) occurring in the target production well.

[0062] A stratified evaluation table is established for each segment of the target production well. If a segment simultaneously exhibits a high percentage of gas production from its strata, a significantly higher gas-oil ratio than the overall well average, abnormal well temperature or pressure near that segment, and good physical properties indicated by logging interpretation, and connectivity with the corresponding segment of the source well, then that segment is classified as a high-probability crossflow segment. After calculating the third risk score, each segment is ranked from highest to lowest score, with the highest score designated as the primary crossflow segment and the second highest score as the secondary primary crossflow segment.

[0063] Step 105: Based on the first risk score, the second risk score, and the third risk score, determine the comprehensive score corresponding to each identification unit. The comprehensive score is used to determine the target candidate gas injection well and target layer that cause gas channeling in the target production well.

[0064] Specifically, for each identification unit, the first risk score, the second risk score, and the third risk score are weighted and fused to obtain a comprehensive score, as shown in the following formula:

[0065] in, For the overall score, Let be the weight coefficients of each item, and satisfy the following conditions: .

[0066] After the comprehensive score is calculated, all identified units are sorted from highest to lowest score. The identified unit with the highest comprehensive score is determined to be the primary channel, and the corresponding candidate gas injection well is the target candidate gas injection well, and the corresponding layer is the target layer. Identified units with the second highest score or those in the second-highest range are determined to be secondary primary channels. Furthermore, based on the range of the comprehensive score, channels can be divided into three levels: strong channeling, moderate channeling, and weak channeling, for subsequent determination of treatment intensity.

[0067] In this disclosure, by constructing an identification unit of target production well, candidate injection well, and layer, gas channeling identification is refined from the traditional overall well group scale to the layer scale. Combined with multi-source data, a first risk score, a second risk score, and a third risk score are calculated to achieve a multi-dimensional evaluation of static geological conditions, dynamic response characteristics, and stratified channeling anomalies. The comprehensive score is then integrated to accurately locate the gas channeling-dominant injection well and the main channeling layer, improving the accuracy and targeting of gas channeling identification. The identification results can directly support near-wellbore sealing and segmented treatment without relying on high-cost testing, reducing the difficulty and cycle of on-site implementation, and providing reliable technical support for gas control and oil production enhancement in natural gas injection reservoirs.

[0068] Based on the identification results using a comprehensive score, a confidence assessment is conducted by combining identification stability and score difference. Confidence refers to the reliability and stability of the current identification results, and its purpose is to determine whether subsequent governance designs can be directly based on them. The confidence assessment can be conducted from three aspects: score difference index, anti-interference ability (data sensitivity), and consistency of repeated calculations.

[0069] The score difference index examines whether there is a significant score gap between the top-ranked candidate and the second-ranked candidate. A larger score difference indicates a higher degree of certainty regarding the chosen target. The formula for the score difference index is as follows:

[0070] in, The difference in fractions. The highest overall score. This is the second-highest overall score. When A value greater than a preset significance threshold (e.g., 0.1) indicates a significant difference.

[0071] Anti-interference capability refers to whether the original highest score ranking remains stable after a specific type of data is removed during testing, thus verifying whether the identification conclusions rely excessively on single, accidental data and lack stability. The formula for calculating anti-interference capability is as follows:

[0072] in, The difference between the two rankings. The identification unit is the original highest-scoring ranking unit. To remove a specific type of data The recalculated rankings for The original rankings. When When the value is 0 (meaning the new ranking is still number 1), it is considered to have strong anti-interference ability.

[0073] Repeated calculation consistency refers to verifying whether the identification results consistently point to the same target candidate gas injection well and target layer under different time windows or multiple repeated calculations. The formula for repeated calculation consistency is shown below:

[0074] in, The consistency ratio, This refers to the total number of repeated measurements after changing parameters (such as different time windows). This refers to the number of times the calculation results point to the same initial target candidate gas injection well and target interval. When It exhibits high consistency when the number of occurrences exceeds a preset threshold (e.g., 95%).

[0075] When the system evaluation finds that the candidate target scores differ significantly ( (Greater than the preset significance threshold), strong anti-interference ability ( =0), and the results of multiple measurements are highly consistent ( Only when the number of occurrences exceeds a preset threshold will the current conclusion be considered to have high confidence.

[0076] When the identification results are stable, the target candidate gas injection well, target formation, channel strength level, and treatment priority are directly output. When the identification results are unstable, supplementary verification measures are initiated. Ultimately, the identification results are transformed into the basis for treatment decisions such as near-wellbore plugging, staged treatment, and gas control and oil enhancement. Supplementary verification refers to the process of reconfirming the identification results by adding tests or verification methods when existing data is insufficient to support a clear conclusion. Its purpose is to reduce the risk of misjudgment.

[0077] For sections identified as primary runoff channels, they should be prioritized for near-wellbore plugging. For secondary runoff channels, they should be prioritized for reinforcement plugging or as the second-most-priority treatment area. For sections that are not primary runoff channels but have strong static connectivity and remaining oil potential, they can be treated in conjunction with oil enhancement strategies. If the identification results indicate that a single-source well is dominant, a targeted plan should be developed focusing on the injection well-production well pair. If multiple sources are involved, the injection and production systems should be optimized from the perspective of well group coordination, and segmented treatment should be implemented.

[0078] This disclosure also provides a production well gas channeling identification device. Figure 2 This is a schematic diagram of the structure of the production well gas channeling identification device provided in the embodiments of this disclosure, as shown below. Figure 2 As shown, the device includes: The first determining module 201 is used to determine the target production well and at least one candidate gas injection well that corresponds to the target production well. The segmentation module 202 is used to divide the target production well into multiple segments according to the well depth, and each segment and each candidate gas injection well form an identification unit. The acquisition module 203 is used to acquire multi-source data related to the target production well and candidate gas injection well at the current sampling time; The second determining module 204 is used to determine, based on multi-source data, a first risk score corresponding to each identification unit, a second risk score corresponding to each candidate gas injection well, and a third risk score corresponding to each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel, the second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well, and the third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well. The third determination module 205 is used to determine the comprehensive score corresponding to each identification unit based on the first risk score, the second risk score and the third risk score. The comprehensive score is used to determine the target candidate gas injection well and the target layer that cause gas channeling in the target production well.

[0079] The specific details of each part of the above-mentioned device have been described in detail in the method section of the implementation plan. For any undisclosed details, please refer to the implementation plan of the method section, and therefore will not be repeated here.

[0080] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0081] Figure 3A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0082] like Figure 3 As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 302 or a computer program loaded from storage unit 308 into random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0083] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the production well gas crosstalk identification method. For example, in some embodiments, the production well gas crosstalk identification method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the production well gas crosstalk identification method described above can be performed. Alternatively, in other embodiments, the computing unit 301 can be configured to perform the production well gas crosstalk identification method by any other suitable means (e.g., by means of firmware).

[0085] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0089] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0090] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0091] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0092] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0093] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for identifying gas channeling in production wells, characterized in that, The method includes: Identify the target production well and at least one candidate injection well that corresponds to the target production well; The target production well is divided into multiple segments according to its depth, and each segment and each candidate gas injection well form an identification unit. Acquire multi-source data related to the target production well and the candidate gas injection well at the current sampling time; Based on the multi-source data, a first risk score is determined for each identification unit, a second risk score is determined for each candidate gas injection well, and a third risk score is determined for each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel. The second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well. The third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well. Based on the first risk score, the second risk score and the third risk score, a comprehensive score is determined for each identification unit. The comprehensive score is used to determine the target candidate gas injection well and the target layer that cause gas channeling in the target production well. The step of determining the first risk score corresponding to each identification unit based on the multi-source data includes: Based on the multi-source data, determine the heterogeneity data, effective connectivity thickness data, inter-well channel matching data, structural potential difference data, and perforation and layer matching data between the target production well and any candidate gas injection well. Based on the heterogeneity data between the target production well and any candidate injection well, the effective connectivity thickness data, the inter-well channel matching data, the structural potential difference data, and the perforation and layer matching data, the first risk score corresponding to each identification unit is determined; Based on the multi-source data, a second risk score is determined for each candidate gas injection well, including: Based on the multi-source data, determine the dynamic changes of the candidate gas injection wells, as well as the changes in the gas-oil ratio, gas production, and gas composition of the target production well within multiple different preset time lags. The correlation between the dynamic changes of candidate injection wells and the changes in gas-oil ratio, gas production, and gas composition of target production wells at different preset time lag times was determined. Based on the correlation within different preset time delays, the first sub-risk score, the second sub-risk score, and the third sub-risk score are determined respectively corresponding to the changes in the gas-oil ratio, the changes in gas production, and the changes in the composition of produced gas of the target production well. Based on the first sub-risk score, the second sub-risk score, and the third sub-risk score, determine the second risk score corresponding to each candidate gas injection well; Based on the multi-source data, a third risk score is determined for each segment, including: Based on the multi-source data, the gas production of each layer, the gas-oil ratio of each layer, the temperature anomaly and pressure anomaly of each layer, as well as the matching degree data of well logging interpretation results or numerical simulation are determined. Based on the gas production of each segment, the gas-oil ratio of each segment, the temperature anomaly and pressure anomaly of each segment, and the matching degree data of well logging interpretation results or numerical simulation, the third risk score corresponding to each segment is determined.

2. The method according to claim 1, characterized in that, After acquiring multi-source data related to the target production well and the candidate gas injection well at the current sampling time, the method further includes: Based on the multi-source data, the dynamic breakthrough index of the target production well at the current sampling time is determined, and the dynamic breakthrough index is used to determine the severity of gas channeling in the target production well. Determine whether the dynamic breakthrough index is greater than the first threshold within the preset sampling period; If the dynamic breakthrough index is greater than the first threshold within a preset sampling period, then the target candidate gas injection well and target layer that caused the gas channeling of the target production well are determined.

3. The method according to claim 2, characterized in that, The step of determining the dynamic breakthrough index of the target production well at the current sampling time based on the multi-source data includes: Based on the multi-source data, the gas-oil ratio, gas production, oil production, and natural gas component content in the produced gas of the target production well are determined. Based on the gas-oil ratio, gas production, oil production, and natural gas component content in the produced gas of the target production well, determine the dynamic breakthrough index of the target production well at the current sampling time.

4. The method according to claim 1, characterized in that, Identifying at least one candidate gas injection well that corresponds to the target production well, including: Determine whether the target production well and the surrounding gas injection wells are in the same development stratum, have the same injection-production relationship, or whether the distance between the gas injection well and the target production well is less than a preset distance; If a gas injection well meets any of the conditions, then the gas injection well is considered a candidate gas injection well that corresponds to the target production well.

5. A gas channeling detection device for production wells, characterized in that, The device includes: The first determining module is used to determine the target production well and at least one candidate gas injection well that corresponds to the target production well. The segmentation module is used to divide the target production well into multiple segments according to the well depth, and each segment and each candidate gas injection well form an identification unit. The acquisition module is used to acquire multi-source data related to the target production well and the candidate gas injection well at the current sampling time; The second determining module is used to determine, based on the multi-source data, a first risk score corresponding to each identification unit, a second risk score corresponding to each candidate gas injection well, and a third risk score corresponding to each layer. The first risk score is used to determine the probability that each identification unit will form a gas dominant channel. The second risk score is used to determine the contribution intensity of each candidate gas injection well to the gas channeling of the target production well. The third risk score is used to determine the contribution intensity of each layer to the gas channeling of the target production well. The third determining module is used to determine the comprehensive score corresponding to each identification unit based on the first risk score, the second risk score and the third risk score. The comprehensive score is used to determine the target candidate gas injection well and the target layer that cause gas channeling in the target production well. The second determining module is specifically used to determine, based on the multi-source data, the heterogeneity data, effective connectivity thickness data, inter-well channel matching data, structural potential difference data, and perforation and layer matching data between the target production well and any candidate gas injection well; Based on the heterogeneity data between the target production well and any candidate injection well, the effective connectivity thickness data, the inter-well channel matching data, the structural potential difference data, and the perforation and layer matching data, the first risk score corresponding to each identification unit is determined; The second determining module is further specifically used to determine, based on the multi-source data, the dynamic changes of the candidate gas injection wells, as well as the changes in the gas-oil ratio, gas production, and gas composition of the target production well within multiple different preset time delays. The correlation between the dynamic changes of candidate injection wells and the changes in gas-oil ratio, gas production, and gas composition of target production wells at different preset time lag times was determined. Based on the correlation within different preset time delays, the first sub-risk score, the second sub-risk score, and the third sub-risk score are determined respectively corresponding to the changes in the gas-oil ratio, the changes in gas production, and the changes in the composition of produced gas of the target production well. Based on the first sub-risk score, the second sub-risk score, and the third sub-risk score, determine the second risk score corresponding to each candidate gas injection well; The second determining module is also specifically used to determine the gas production of each layer, the gas-oil ratio of each layer, the temperature anomaly and pressure anomaly of each layer, and the matching degree data of well logging interpretation results or numerical simulation based on the multi-source data. Based on the gas production of each segment, the gas-oil ratio of each segment, the temperature anomaly and pressure anomaly of each segment, and the matching degree data of well logging interpretation results or numerical simulation, the third risk score corresponding to each segment is determined.

6. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

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