Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology

Hou, Xinxin; Yang, Jin; Yin, Qian; Liu, Hexing; Chen, Haodong; Zheng, Jinlong; Wang, Junxiang; Cao, Bohan; Zhao, Xin; Hao, Mingxuan; Liu, Xun

doi:10.4043/30653-ms

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…The performance of the ANN to predict six lost circulation types is good. The proposed model satisfies the need for drilling engineering and can provide guidance for the estimation of lost circulation risks prior to drilling [17].…”

Section: -Loss Of Circulation Prediction Reviewmentioning

confidence: 84%

Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review

Salih

Hussein

2022

IJCPE

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Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses is to prevent them. Drilling fluid loss is a complex problem that is difficult to predict using simple and traditional methods. Artificial intelligence represents a modern and accurate technology for solving complex problems such as drilling fluid loss. Artificial intelligence through supervised machine learning provides the possibility of predicting these losses before they occur based on field data such as drilling fluid properties, drilling parameters, rock properties, and geomechanical parameters that are related to the loss of circulation of the wells suffered from losses problem located in the same area. In this paper, several supervised machine learning models have been reviewed that were used for detecting and predicting of loss of drilling fluids during the drilling process. The paper provides an inclusive review of drilling fluid prediction and detection from simplest to more complected intelligent models.

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Section: -Loss Of Circulation Prediction Reviewmentioning

confidence: 84%

Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review

Salih

Hussein

2022

IJCPE

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“…AlKinani et al [2] demonstrated that the Levenberg-Marquardt (LM) algorithm performs better than other ANN training algorithms in predicting the lost circulation through induced fractures. Hou et al [45] also developed an ANN model with satisfactory outcomes for predicting lost circulation in an offshore high-pressure high-temperature (HPHT) field. However, the ANN and SVR approaches typically do not perform well for lost circulation in natural and hydraulically induced fractures associated with large datasets [46].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

Mardanirad

Wood

Zakeri³

2021

SN Appl. Sci.

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In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently. Article Highlights Three deep learning models classify fluid loss severity in an oil field carbonate reservoir. Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records. Convolution neural network outperformed other deep learning methods.

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“…The results demonstrated the effectiveness and practicality of both models in predicting lost circulation incidents. Manshad et al 6 employed SVM and radial basis function models to predict drilling fluid seepage in the Maroun oilfield. The findings highlighted the high accuracy of both the SVM model and the radial basis function model in predicting the drilling fluid seepage.…”

Section: Introductionmentioning

confidence: 99%

Lost Circulation Prediction Method Based on an Improved Fruit Fly Algorithm for Support Vector Machine Optimization

Deng,

Pei,

Yan

et al. 2023

ACS Omega

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Lost circulation events during drilling operations are known for their abruptness and are difficult to control. Traditional diagnostic methods rely on qualitative indicators, such as mud pit volume changes or anomalous logging curve patterns. However, these methods are subjective and rely heavily on empirical knowledge, resulting in delayed or inaccurate predictions. To address this problem, there is an urgent need to develop efficient methods for a timely and accurate lost circulation prediction. In this study, a novel approach is proposed by combining principal component analysis (PCA) and empirical analysis to reduce the dimensionality of the model data. This dimensionality reduction helps to streamline the analysis process and improve prediction accuracy. The predictive model also incorporates an improved fruit fly optimization algorithm (IFOA) in conjunction with support vector machine (SVM) techniques. The actual instances of lost circulation serve as the evaluation criteria for this integrated method. To overcome the challenges associated with irregular population distribution within randomly generated individuals, a tent map strategy is introduced to ensure a more balanced and representative sample. In addition, the model addresses issues such as premature convergence and slow optimization rates by employing a sine−cosine search strategy. This strategy helps to achieve optimal results and speeds up the prediction process. The improved prediction model demonstrates exceptional performance, achieving accuracy, precision, recall, and F1 scores of 96.8, 97, 96, and 96%, respectively. These results indicate that the IFOA-SVM approach achieves the highest accuracy with a reduced number of iterations, proving to be an efficient and fast method for predicting the lost circulation events. Implementation of this methodology in drilling operations can lead to improved efficiency, reliability, and overall performance.

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Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology

Cited by 21 publications

References 5 publications

Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review

Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review

The application of deep learning algorithms to classify subsurface drilling lost circulation severity in large oil field datasets

Lost Circulation Prediction Method Based on an Improved Fruit Fly Algorithm for Support Vector Machine Optimization

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