# Casing Damage Prediction Model Based on the Data-Driven Method

Authors: Chaodong Tan; Wei Yan; Qing Tang; Hua Wu; Hongguang Bu; Said Juma Kambi; Jiankang Liu
Journal: Mathematical Problems in Engineering
 (2020)

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## Abstract

Casing damage caused by sand production in unconsolidated sandstone reservoirs often results in oil wells unable to produce normally. However, due to the complex mechanism of sheath damage caused by sand production, there is no more mature technology for predicting the risk of casing damage in advance. Data-driven method can better integrate various factors and use a large amount of historical data to solve complex classification prediction problems. In this paper, XGBoost and LightGBM algorithms are used to establish casing damage prediction models, and 13 model application experiments are carried out to optimize the set of casing damage factors. These two algorithms are used to calculate the feature importance of each factor and determine the final set of factors. The evaluation results of five key metrics show that both prediction models show good performance, and the prediction accuracy is 0.99 for the XGBoost model and 0.94 for the LightGBM model. Applying the established prediction model can determine reasonable range of the maximum daily liquid production of a single layer (Qlmax) to reduce the probability of casing damage. In addition, at certain Qlmax, increasing the perforation density can significantly reduce the probability of casing damage. Therefore, increasing the perforation density can achieve high production without causing casing damage.


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## Body

## 1. Introduction

Casing damage is a common problem in sand production reservoirs. Preidentifying the risk of casing damage and taking effective preventive measures can reduce the probability of casing damage in oil wells. Some scholars analyze wellbore integrity by establishing the mechanism model. Yin and Gao [1] established a mechanical model of casing in directional well under in situ stresses and inside hydrostatic pressure. Lin et al. [2] established two theoretical models for casing strength degradation due to wear and corrosion to analyze the effects of relevant parameters on residual strength of defective casing. De Simone et al. [3] developed an analytical solution to assess the wellbore stresses and integrity during the drilling, construction, and production phases, which considered plane strain conditions, continuous homogeneous isotropic media, and linear elastic materials. Yin et al. [4] established a finite element model to analyze nonlinear mechanical behavior of casing crossing slip formation, and the analysis results can provide a reference for casing design. Deng et al. [5] studied the collapse failure mechanism of cemented casing under nonuniform load and obtained the stress-strain laws of cemented casing by the electrical method. Han et al. [6] proposed that the incongruous displacement in mudstone is the main mechanism of casing deformation and developed a fluid-solid coupling seepage mechanical model to capture the flow and deformation in interlayers. Mohammed et al. [7] summarized the existing tools used for the assessment of well integrity issues and their respective limitations.Some scholars use the data-driven method to solve the problems in oilfield production. Noshi et al. [8] used nine unsupervised algorithms, including support vector machine, Bootstrap, random forest, and some other algorithms to identify the characteristics of casing failure during drilling and fracturing. Noshi et al. [9] applied artificial neural networks and boosted ensemble trees to build the prediction model of casing failure probability, which was used to evaluate 26 different features compiled from drilling, fracturing, and geologic data. Song and Zhou [10] selected 10 parameters that have the greatest impact on casing damage, including sand layer, casing, and perforation information and established the algorithm model of casing damage risk assessment by using GBDT, and the prediction accuracy of the model could reach 86.3%. Tang et al. [11] selected 19 casing damage influencing factors and established the casing damage risk prediction model by using XGBoost and LightGBM algorithms. Tan et al. [12] established a data-driven casing damage prediction model and proposed that the main control factors of casing damage were perforation density and production pressure difference.At present, it is difficult to predict casing damage by the mechanism method. Machine learning method has been used to solve many problems in the process of oil and gas field development. The factor set of the casing damage prediction model established by Qing T and Chaodong T includes the production pressure difference, but these data are difficult to obtain completely and accurately. Therefore, it is necessary to optimize the set of casing damage influencing factors to enhance the practicability of the casing damage prediction model.


## 2. Influencing Factors of Casing Damage

This paper analyzes data integrity and accuracy of influencing factors of casing damage in the existing database of Gangxi oilfield and then analyzes the characteristics of each factor. Based on the original set of influencing factors of casing damage [11, 12], some parameters were deleted and some other comprehensive parameters were added to form a new casing damage dataset with 23 influencing factors, as shown in Table 1.Table 1
Influencing factors of casing damage in production wells.
123456Wellhead abscissaWellhead ordinatePerforation topPerforation bottomPerforation thicknessPerforation density789101112Perforator phasingCasing wall thicknessCasing steel gradeSand layer topSand layer bottomSand layer thickness131415161718Reservoir groupPermeabilityPorosityMaximum daily liquid production of the single layerMaximum water cutMaximum fluid production intensity1920212223QlcumQlmax/KHQlmax/HNCasing service timeSand layer production timeProduction pressure difference of a small proportion of oil wells can be calculated by using the existing data in the database; this makes the calculation results uncertain, so the production pressure difference is omitted in the set of casing damage influencing factors. Since the outer diameter of casing in most wells of Gangxi oilfield is 139.7 mm, the mechanism of casing damage is different when this parameter is different. It is generally difficult to obtain a better understanding with fewer samples, so this article only focuses on the well with the casing outer diameter of 139.7 mm.Because some of the geological parameters are difficult to obtain and the physical parameters of adjacent wells are generally close, the horizontal coordinates and vertical coordinates of the wellhead are used to approximately characterize these parameters. During oil well production, fluid carries formation sand into the wellbore. As the amount of sand produced increases, the formation around the casing in the production section is hollowed out, causing the casing to lose its lateral restraint. The overlying stratigraphic pressure on the formation acts on the casing axially, which causes the load on the casing shaft to increase dramatically. When the critical bending load of the casing in this section is exceeded, the casing will bend, leading to the casing damage [13].Therefore, in this paper, the maximum daily liquid production of a single layer is used as one of the casing damage influencing factors. In addition, to illustrate the casing by prolonged production of fluids, an additional arbitrage factor, i.e., cumulative liquid production of a single layer, was added. Due to differences in physical properties and perforation parameters between wells, two comprehensive parameters were added, namely, Qlmax/KH and Qlmax/HN. Qlmax/KH contains the physical properties and output information of a single layer, which expresses the supply and procurement balance relationship to some extent. Qlmax/HN represents the maximum flow rate per hole. When the flow rate per hole exceeds the critical sand carrying flow rate, the formation sand will be carried into the wellbore. Qlcum represents the cumulative liquid production of the single layer, reflecting the damage to the casing caused by long-term fluid production. In Table1, Qlmax stands for the maximum daily liquid production of a single layer. K, H, and N represent the permeability, the perforation thickness, and the perforation density of the sand layer.


## 3. Casing Damage Probability Prediction

The workflow of casing damage probability prediction is shown in Figure1. Firstly, the required data in Table 1 are extracted from the existing database to form a sample dataset of casing damage. Secondly, a casing damage probability prediction model is established based on the XGBoost and LightGBM algorithms based on the above dataset. Thirdly, the established models are used to carry out multiple data experiments, compare the prediction results of these experiments, and determine the influencing factors to be eliminated. Fourthly, the feature importance of the remaining impact factors is calculated and analyzed. Fifthly, the less important impact factor is deleted to optimize the input parameters of the model. Finally, the optimized model is used to predict the casing damage probability of the oil well.Figure 1
Workflow of casing damage probability prediction.


### 3.1. Data Preparation and Processing

The data used in this paper include the geological parameters, engineering parameters, production data, and casing damage information of 244 production layers in 133 wells in Gangxi oilfield. Among them, the casings of 68 production layers in 64 wells were damaged. The casing damage rate of the production layers was 27.9%. The required data in Table1 are extracted from the existing database of Gangxi oilfield to form a sample set of casing damage. In this sample set, casing steel grade and oil reservoir group are text type, and different numbers are used to represent different types. Numbers 1, 2, 3, and 4 are used to indicate the casings with steel grades of J55, K55, N80, and P110, respectively. Numbers 1 and 2 are used to indicate Ming II and Ming III reservoir groups, respectively. In addition, missing data were completely added, and incorrect data were revised. The data overview of these 244 production layers is shown in Table 2.Table 2
Data overview of 244 production layer samples.
SymbolInfluencing factorsUnitMinimumMaximumAveragef0Wellhead abscissaM———f1Wellhead ordinateM———f2Perforation topM8271408.71067.1f3Perforation bottomM833.81412.41071.1f4Perforation thicknessM1.014.03.9f5Perforation densityhole/m1032—f6Perforator phasingdegree90135—f7Casing wall thicknessmm6.29.17—f8Casing steel gradeJ55, K55, N80, P110f9Sand layer topM826.91408.71066.6f10Sand layer bottomM833.81412.41072.6f11Sand layer thicknessM119.25.9f12Reservoir groupMing II, Ming IIIf13PermeabilitymD162.95431.11415.7f14Porosity%20.272.134.4f15Qlmaxm3/d1.2359.727.9f16Max water cut%28.710090.9f17Max fluid production intensitym3/m0.3167.367.82f18Qlcumm319.62,123,57659,579f19Qlmax/KH1000m3/(d·mD·m)0.14125.087.01f20Qlmax/HNm3/d/hole0.025.180.55f21Casing service timeAnnual0.4850.3415.16f22Sand layer production timeAnnual0.1841.7910.7


### 3.2. Casing Damage Probability Prediction Model

Casing damage prediction is a classification problem; it could be predicted whether the casing is going to be damaged or not. XGBoost and LightGBM are the two most popular algorithms that are often used to solve classification problems [11, 12]. The two algorithms have got various advantages over others. Therefore, in this paper, the two mentioned algorithms are used to establish the casing damage prediction model. In the prediction model, 23 influencing factors in Table 2 are taken as input parameters, and casing damage probability is used as the output parameter, as shown in Figure 2. In the sample data, number 1 means that the casing has been damaged, and number 0 means that the casing has not been damaged. The split function in Python is used to extract 80% of the sample data as the training dataset and 20% as the testing dataset.Figure 2
Casing damage probability prediction model.


### 3.3. Prediction Model Application Tests

Taking different influencing factor combinations as the input of the model, applying the established prediction model to carry out data tests, comparing the prediction results of these tests, and determining which influencing factors to eliminate, thirteen data tests were carried out, and the parameters eliminated from each test and prediction results are shown in Table3.Table 3
Model application test prediction results.
ProjectAlgorithmTraining setTest setNumber of factorsParameters removedDamagedUndamagedDamagedUndamagedActual—561391237——test1XGBoost56139113523—LightGBM531391036test2XGBoost5413893421Casing service timeSand layer production timeLightGBM32138736test3XGBoost56139113521Perforation bottomSand layer bottomLightGBM531391036test4XGBoost56139103520Perforation bottomSand layer bottomPerforator phasingLightGBM531391036test5XGBoost56139103520Perforation bottomSand layer bottomQlmaxLightGBM531391036test6XGBoost56139113420Perforation bottomSand layer bottomMax water cutLightGBM491391036test7XGBoost56139113520Perforation bottomSand layer bottomMax fluid production intensityLightGBM541391036test8XGBoost56139113520Perforation bottomSand layer bottom QlcumLightGBM541391036test9XGBoost55139103520Perforation bottomSand layer bottomQlmax/KHLightGBM541391036test10XGBoost56139113520Perforation bottomSand layer bottomQlmax/HNLightGBM541391035test11XGBoost54138103320Perforation bottomSand layer bottomCasing service timeLightGBM47137935test12XGBoost56139103520Perforation bottomSand layer bottomSand layer production timeLightGBM541391036test13XGBoost55139103519Perforation bottomSand layer bottomMax fluid production intensityQlcumLightGBM541391035Comparing the prediction results of the training dataset and the testing dataset of the 13 tests in Table3, it is found that the prediction effects of test7 and test8 are both the best. In the training set, the XGBoost model has higher prediction accuracy than the LightGBM model. In the testing dataset, the prediction accuracy of the XGBoost model and the LightGBM model is the same, and the number of casing damaged wells predicted by the XGBoost model is closer to reality.Perforation bottom and sand layer bottom were eliminated in these two experiments, so these two parameters are excluded from the sample data. Test7 deleted max fluid production intensity, and test8 deleted Qlcum; test13 excluded these two parameters at the same time. The prediction result of test13 is not as good as test7 and test8. In the following, we will discuss which of these two parameters to exclude.


### 3.4. Feature Importance Analysis

Figures3 and 4 show the calculation results of feature importance using the XGBoost and the LightGBM algorithms, respectively. The parameters represented by the symbols of the ordinate are shown in Table 2. It can be seen that, in the results of the two algorithms, max fluid production intensity is less important than Qlcum. Therefore, in the final prediction sample, max fluid production intensity is eliminated. Therefore, the input parameters of the prediction model use the 20 factors used in test8.Figure 3
Feature importance based on the XGBoost algorithm.Figure 4
Feature importance based on the LightGBM algorithm.


### 3.5. Prediction Model Evaluation

The 20 casing damage influencing factors identified above are used as input parameters, and casing damage probability is used as the output parameter. XGBoost and LightGBM models are used to predict the casing damage probability of 49 production layers (testing dataset). The prediction results are shown in Figure5.Figure 5
Casing damage probability prediction by XGBoost and LightGBM models.In the testing dataset, there are actually 12 production layers with casing damage and 37 production layers with undamaged casing. Among the 12 casing damaged samples, XGBoost model and LightGBM model correctly predicted 11 and 10, respectively. In Figure5, the 1st sample is a casing damaged sample, but it is incorrectly predicted by both models as undamaged. The 48th sample is also a casing damaged sample, which is correctly predicted by the XGBoost model but incorrectly predicted by the LightGBM model. Among the 37 undamaged samples, XGBoost model and LightGBM model correctly predicted 35 and 36, respectively. The 26th sample is an undamaged sample but is incorrectly predicted to be casing damaged by both models. The 37th sample is also an undamaged sample, which is correctly predicted by LightGBM and incorrectly predicted by the XGBoost model.Five key metrics are used to evaluate the performance of the casing damage prediction model, including accuracy, precision, recall, F1-score, and AUC (area under ROC curve). The first four parameters can be expressed as(1)accuracy=TP+TNTP+TN+FP+FN,precision=TPTP+FP,recall=TPTP+FN,F1−score=2recall⋅precisionrecall+precision.If the casing in a production layer was actually damaged and was predicted to be damaged by the model, then it is true positive (TP). If the casing in a production layer was actually undamaged but was predicted to be damaged, then it is false positive (FP). If the casing in a production layer was actually undamaged and was predicted to be undamaged, then it is true negative (TN). Finally, if the casing in a production layer was actually damaged but was predicted to be undamaged, then it is false negative (FN).The confusion matrix of casing damage prediction is shown in Figure6. For the training dataset, the prediction results of the XGBoost model are all correct, while the LightGBM model predicts the 2 casing damaged layers as undamaged. For the testing dataset, the XGBoost model predicts one more casing damaged sample correctly than LightGBM but one less casing undamaged sample correctly than LightGBM.Figure 6
Confusion matrix of casing damage prediction.As can be seen from Table4, accuracy, recall, and F1-score of the XGBoost model are higher than LightGBM, but precision is lower than LightGBM. The AUC curve is shown in Figure 7, the AUC of both models is very high, and the AUC of LightGBM is slightly higher.Table 4
Four key metrics for prediction model evaluation.
SampleModelAccuracyPrecisionRecallF1-scoreTraining datasetXGBoost1111LightGBM0.9910.960.98Testing datasetXGBoost0.940.850.920.88LightGBM0.940.910.830.87Figure 7
Area under the ROC curve of the testing dataset.In general, the prediction effects of both models are very good. In actual application, the prediction results of the two models can be combined for comprehensive evaluation. If the prediction results of the two models are both casing damaged, it reflects to a certain extent that the probability of casing damage in the target production layer is higher, and technicians should pay great attention to the well. If only one model predicts that the result is casing damaged, the technician needs to further analyze and evaluate based on the actual data on the oilfield.


## 3.1. Data Preparation and Processing

The data used in this paper include the geological parameters, engineering parameters, production data, and casing damage information of 244 production layers in 133 wells in Gangxi oilfield. Among them, the casings of 68 production layers in 64 wells were damaged. The casing damage rate of the production layers was 27.9%. The required data in Table1 are extracted from the existing database of Gangxi oilfield to form a sample set of casing damage. In this sample set, casing steel grade and oil reservoir group are text type, and different numbers are used to represent different types. Numbers 1, 2, 3, and 4 are used to indicate the casings with steel grades of J55, K55, N80, and P110, respectively. Numbers 1 and 2 are used to indicate Ming II and Ming III reservoir groups, respectively. In addition, missing data were completely added, and incorrect data were revised. The data overview of these 244 production layers is shown in Table 2.Table 2
Data overview of 244 production layer samples.
SymbolInfluencing factorsUnitMinimumMaximumAveragef0Wellhead abscissaM———f1Wellhead ordinateM———f2Perforation topM8271408.71067.1f3Perforation bottomM833.81412.41071.1f4Perforation thicknessM1.014.03.9f5Perforation densityhole/m1032—f6Perforator phasingdegree90135—f7Casing wall thicknessmm6.29.17—f8Casing steel gradeJ55, K55, N80, P110f9Sand layer topM826.91408.71066.6f10Sand layer bottomM833.81412.41072.6f11Sand layer thicknessM119.25.9f12Reservoir groupMing II, Ming IIIf13PermeabilitymD162.95431.11415.7f14Porosity%20.272.134.4f15Qlmaxm3/d1.2359.727.9f16Max water cut%28.710090.9f17Max fluid production intensitym3/m0.3167.367.82f18Qlcumm319.62,123,57659,579f19Qlmax/KH1000m3/(d·mD·m)0.14125.087.01f20Qlmax/HNm3/d/hole0.025.180.55f21Casing service timeAnnual0.4850.3415.16f22Sand layer production timeAnnual0.1841.7910.7


## 3.2. Casing Damage Probability Prediction Model

Casing damage prediction is a classification problem; it could be predicted whether the casing is going to be damaged or not. XGBoost and LightGBM are the two most popular algorithms that are often used to solve classification problems [11, 12]. The two algorithms have got various advantages over others. Therefore, in this paper, the two mentioned algorithms are used to establish the casing damage prediction model. In the prediction model, 23 influencing factors in Table 2 are taken as input parameters, and casing damage probability is used as the output parameter, as shown in Figure 2. In the sample data, number 1 means that the casing has been damaged, and number 0 means that the casing has not been damaged. The split function in Python is used to extract 80% of the sample data as the training dataset and 20% as the testing dataset.Figure 2
Casing damage probability prediction model.


## 3.3. Prediction Model Application Tests

Taking different influencing factor combinations as the input of the model, applying the established prediction model to carry out data tests, comparing the prediction results of these tests, and determining which influencing factors to eliminate, thirteen data tests were carried out, and the parameters eliminated from each test and prediction results are shown in Table3.Table 3
Model application test prediction results.
ProjectAlgorithmTraining setTest setNumber of factorsParameters removedDamagedUndamagedDamagedUndamagedActual—561391237——test1XGBoost56139113523—LightGBM531391036test2XGBoost5413893421Casing service timeSand layer production timeLightGBM32138736test3XGBoost56139113521Perforation bottomSand layer bottomLightGBM531391036test4XGBoost56139103520Perforation bottomSand layer bottomPerforator phasingLightGBM531391036test5XGBoost56139103520Perforation bottomSand layer bottomQlmaxLightGBM531391036test6XGBoost56139113420Perforation bottomSand layer bottomMax water cutLightGBM491391036test7XGBoost56139113520Perforation bottomSand layer bottomMax fluid production intensityLightGBM541391036test8XGBoost56139113520Perforation bottomSand layer bottom QlcumLightGBM541391036test9XGBoost55139103520Perforation bottomSand layer bottomQlmax/KHLightGBM541391036test10XGBoost56139113520Perforation bottomSand layer bottomQlmax/HNLightGBM541391035test11XGBoost54138103320Perforation bottomSand layer bottomCasing service timeLightGBM47137935test12XGBoost56139103520Perforation bottomSand layer bottomSand layer production timeLightGBM541391036test13XGBoost55139103519Perforation bottomSand layer bottomMax fluid production intensityQlcumLightGBM541391035Comparing the prediction results of the training dataset and the testing dataset of the 13 tests in Table3, it is found that the prediction effects of test7 and test8 are both the best. In the training set, the XGBoost model has higher prediction accuracy than the LightGBM model. In the testing dataset, the prediction accuracy of the XGBoost model and the LightGBM model is the same, and the number of casing damaged wells predicted by the XGBoost model is closer to reality.Perforation bottom and sand layer bottom were eliminated in these two experiments, so these two parameters are excluded from the sample data. Test7 deleted max fluid production intensity, and test8 deleted Qlcum; test13 excluded these two parameters at the same time. The prediction result of test13 is not as good as test7 and test8. In the following, we will discuss which of these two parameters to exclude.


## 3.4. Feature Importance Analysis

Figures3 and 4 show the calculation results of feature importance using the XGBoost and the LightGBM algorithms, respectively. The parameters represented by the symbols of the ordinate are shown in Table 2. It can be seen that, in the results of the two algorithms, max fluid production intensity is less important than Qlcum. Therefore, in the final prediction sample, max fluid production intensity is eliminated. Therefore, the input parameters of the prediction model use the 20 factors used in test8.Figure 3
Feature importance based on the XGBoost algorithm.Figure 4
Feature importance based on the LightGBM algorithm.


## 3.5. Prediction Model Evaluation

The 20 casing damage influencing factors identified above are used as input parameters, and casing damage probability is used as the output parameter. XGBoost and LightGBM models are used to predict the casing damage probability of 49 production layers (testing dataset). The prediction results are shown in Figure5.Figure 5
Casing damage probability prediction by XGBoost and LightGBM models.In the testing dataset, there are actually 12 production layers with casing damage and 37 production layers with undamaged casing. Among the 12 casing damaged samples, XGBoost model and LightGBM model correctly predicted 11 and 10, respectively. In Figure5, the 1st sample is a casing damaged sample, but it is incorrectly predicted by both models as undamaged. The 48th sample is also a casing damaged sample, which is correctly predicted by the XGBoost model but incorrectly predicted by the LightGBM model. Among the 37 undamaged samples, XGBoost model and LightGBM model correctly predicted 35 and 36, respectively. The 26th sample is an undamaged sample but is incorrectly predicted to be casing damaged by both models. The 37th sample is also an undamaged sample, which is correctly predicted by LightGBM and incorrectly predicted by the XGBoost model.Five key metrics are used to evaluate the performance of the casing damage prediction model, including accuracy, precision, recall, F1-score, and AUC (area under ROC curve). The first four parameters can be expressed as(1)accuracy=TP+TNTP+TN+FP+FN,precision=TPTP+FP,recall=TPTP+FN,F1−score=2recall⋅precisionrecall+precision.If the casing in a production layer was actually damaged and was predicted to be damaged by the model, then it is true positive (TP). If the casing in a production layer was actually undamaged but was predicted to be damaged, then it is false positive (FP). If the casing in a production layer was actually undamaged and was predicted to be undamaged, then it is true negative (TN). Finally, if the casing in a production layer was actually damaged but was predicted to be undamaged, then it is false negative (FN).The confusion matrix of casing damage prediction is shown in Figure6. For the training dataset, the prediction results of the XGBoost model are all correct, while the LightGBM model predicts the 2 casing damaged layers as undamaged. For the testing dataset, the XGBoost model predicts one more casing damaged sample correctly than LightGBM but one less casing undamaged sample correctly than LightGBM.Figure 6
Confusion matrix of casing damage prediction.As can be seen from Table4, accuracy, recall, and F1-score of the XGBoost model are higher than LightGBM, but precision is lower than LightGBM. The AUC curve is shown in Figure 7, the AUC of both models is very high, and the AUC of LightGBM is slightly higher.Table 4
Four key metrics for prediction model evaluation.
SampleModelAccuracyPrecisionRecallF1-scoreTraining datasetXGBoost1111LightGBM0.9910.960.98Testing datasetXGBoost0.940.850.920.88LightGBM0.940.910.830.87Figure 7
Area under the ROC curve of the testing dataset.In general, the prediction effects of both models are very good. In actual application, the prediction results of the two models can be combined for comprehensive evaluation. If the prediction results of the two models are both casing damaged, it reflects to a certain extent that the probability of casing damage in the target production layer is higher, and technicians should pay great attention to the well. If only one model predicts that the result is casing damaged, the technician needs to further analyze and evaluate based on the actual data on the oilfield.


## 4. Prediction Model Application

The casing damage probability prediction model established above is used to predict the casing damage probability of the current production layers. For the production layers with higher risk of casing damage, the controllable parameters are optimized to reduce the risk.It can be seen from Figure8 that, in the four production layers, when Qlmax increases, the casing damage probability of layer X27-18/1272.8∼1277.5 m increases, but the predicted result is still below the casing damage limit. For the other three wells, when Qlmax increases, the probability of casing damage may exceed 0.5, which means that the casings of these layers may be damaged.Figure 8
Casing damage probability of 4 production layers vs. Qlmax.For production layers x8-9-2/962.3∼965.5 m, x10-8-3/1052.5∼1056.5 m, and x2-5-5/1054.1∼1058.9 m, critical Qlmax of casing damage is 45, 58, and 63, respectively. It can be found that due to differences in physical properties, perforation parameters, and other parameters in the production layers between wells, critical casing damage Qlmax is also different. In actual production, Qlmax is controlled below the critical value to reduce the probability of casing damage.It can be seen from Figure9 that when Qlmax is lower than 80 m3/d, as the perforation density increases, the probability of casing damage is greatly reduced. Actual Qlmax of production layer X8-9-2/962.3∼965.5 m is 48 m3/d, and its casing damage probability is higher than 0.5. The perforation density can be increased to reduce the probability of casing damage while maintaining a high production. If the perforation density is increased to 32, Qlmax can be increased to 80 m3/d.Figure 9
Casing damage probability of a production layer (X8-9-2/962.3∼965.5 m) vs. different perforation densities.


## 5. Conclusions

(1)
XGBoost and LightGBM are used to establish the casing damage prediction model. The split function in Python is used to extract 80% of the sample data as the training dataset and 20% as the testing dataset. The evaluation results show that both XGBoost model and LightGBM model can achieve good prediction results, and the prediction accuracy is 0.99 and 0.94, respectively.(2)
Accuracy, recall, and F1-score of the XGBoost model are higher than LightGBM, but precision is lower than LightGBM. The AUC of both models is very high, and the AUC of LightGBM is slightly higher. In general, the prediction effects of both models are very good.(3)
The casing damage probability prediction model established above is used to predict the casing damage probability of the current production layers. In the four production layers, when Qlmax increases, the casing damage probability of layer X27-18/1272.8∼1277.5 m increases. In actual production, Qlmax is controlled below the critical value to reduce the probability of casing damage.(4)
When Qlmax is lower than 80 m3/d, as the perforation density increases, the probability of casing damage is greatly reduced. The perforation density can be increased to reduce the probability of casing damage while maintaining a high production. If the perforation density is increased to 32, Qlmax can be increased to 80 m3/d.(5)
Due to the differences in physical properties, perforation parameters, and other parameters in the production layers and wells, critical casing damage Qlmax is also different. In practice, controlling Qlmax below the critical value theoretically can reduce the probability of casing damage.



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*Source: 8315908-2020-08-26.xml*