Research and Practice of Risk Early Warning Technology for Lost Circulation with Drilling under the Conditions of Geological Engineering Information Fusion: The Example of the Yuanba Area

Liu, Zhikun; Luo, Rongtao; Yang, Zhe; Wang, Linjie; Wang, Liupeng

doi:10.3390/pr10122516

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Drilling engineering is very important for the development of underground fluid resources, such as oil, natural gas and gas hydrates, and is a systemic engineering consisting of a long process, complex technology and multiple procedures. As oil and gas exploration expands to unconventional and more complicated areas, such as shale gas, shale oil, deep coal mines and hot dry rocks, the risk of drilling safety accidents is on the rise [1,2]. If such accidents occur, they may result in serious losses, including personal injury, economic losses and environmental pollution [3,4].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

Fan,

Sun,

et al. 2024

Processes

View full text Add to dashboard Cite

In order to avoid potential personnel and financial losses, the evaluation of pre-drilling safety is of great importance in oil and gas exploration and development. This paper presents a method of evaluating pre-drilling safety through combining the Analytic Hierarchy Process (AHP) with the Alternating Condition Expectation (ACE) method. An indicator system with a 9-3-1 structure was established, incorporating various unrestricted variables to describe the technical factor. Additionally, nine membership functions and weights were determined in order to build the AHP model by connecting the independent variables in the basic layer to dependent variables in the middle layer. Four transformed functions were also formulated to construct the ACE model by linking the middle variables to the pre-drilling safety value in the final layer. A total of 28 sets of on-site drilling data from three oilfields were collected for the establishment and verification of the AHP-ACE model. Average absolute error (AAE) and average absolute relative error (AARE) of the model to predict the training data are 0.03 and 4.29%, respectively, whereas the AAE and AARE for verification samples are 0.03 and 4.51%, respectively. The sensitivity ranking of the three potential variables is as follows: human factor exhibits the highest degree of sensitivity, followed by natural factor and technical factor, in descending order. The AHP-ACE model for pre-drilling safety assessment faces limitations in universal applicability and scope, particularly in real-time drilling activities. However, its potential for improvement lies in integrating insights from past operations and expanding the dataset to enhance accuracy and broaden safety assessment coverage. This method is not limited by blocks, which is of great significance to ensure drilling safety.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

Fan,

Sun,

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…In addition, the amount of mud loss is different, and the corresponding plugging methods are different. If the mud loss is detected in advance, it can be solved by adding plugging agents within 48 h to prevent it from further developing into more serious mud loss [3,4]. Therefore, the purpose of this study is to train neural networks using historical drilling data and establish a predictive model capable of forecasting early mud circulation loss.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying Improved WOA-BiLSTM

Liu,

Jia,

et al. 2023

Processes

View full text Add to dashboard Cite

Drilling hazards can be significantly decreased by anticipating potential mud loss and then putting the right well control measures in place. Therefore, it is critical to provide early estimates of mud loss. To solve this problem, an enhanced WOA (Whale Optimization Algorithm) and a BiLSTM (Bidirectional Long Short Term Memory) optimization based prediction model of lost circulation prior to drilling has been created. In order to minimize the noise in the historical comprehensive logging data, a wavelet filtering technique was first used. Then, according to the nonlinear Spearman rank correlation coefficient between mud loss and logging parameter values from large to small, seven characteristic parameters were preferred, and the sliding window was used to extract the relevant data. Secondly, the number of neurons in the first and second hidden layers, the maximum training time, and the initial learning rate of the BiLSTM model were optimized using the enhanced WOA method. The BiLSTM network was given the acquired superparameters in order to improve the model’s ability to predict occurrences. Finally, the model was trained and tested using the processed data. In comparison to the LSTM model, BiLSTM model, and WOA-BiLSTM model, respectively, the improved WOA-BiLSTM early mud loss prediction in southwest Chinese oil fields suggested in this study beat the others, receiving 22.3%, 18.7%, and 4.9% higher prediction accuracy, respectively.

show abstract

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

Qu,

Liao,

Wang

et al. 2024

Journal of Industrial Information Integration

View full text Add to dashboard Cite

Research and Practice of Risk Early Warning Technology for Lost Circulation with Drilling under the Conditions of Geological Engineering Information Fusion: The Example of the Yuanba Area

Cited by 3 publications

References 4 publications

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

Prediction of Lost Circulation in Southwest Chinese Oil Fields Applying Improved WOA-BiLSTM

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

Contact Info

Product

Resources

About