Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation

Alsaihati, Ahmed; Abughaban, Mahmoud; Elkatatny, Salaheldin; Shehri, Dhafer Al

doi:10.1021/acsomega.2c00970

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…The number of OH − that can launch effective attacks decreases, resulting in an increase in the time required for complete hydrolysis. (3) The increase in the concentration of metal ions in the solution leads to a decrease in the osmotic pressure difference between the inside of CKS-DPPG and the solution, and the driving force for the solution to enter the inside of CKS-DPPG is weakened. In summary, a certain concentration of salt solution has little effect on the swelling and degradation properties of CKS-DPPG, indicating that CKS-DPPG has good adaptability to salt solutions.…”

Section: Effect Of Mineralization On Swelling and Degradation Perform...mentioning

confidence: 99%

“…Lost circulation may result in matrix permeability decreasing, oil and gas channels becoming choked, non-production time (NPT) increasing, etc. [ 1 , 2 ], and in severe cases, even secondary reservoir contamination and well collapse [ 3 , 4 , 5 ]. The conventional countermeasures use lost circulation materials (LCM) to prevent and treat leakage.…”

Section: Introductionmentioning

confidence: 99%

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Preparation and Degradation Performance Study of P(AM/GG/PEGDA) Nanocomposite Self-Degradation Gel Plugging Material

Bao,

Liu,

Zhang

et al. 2023

Gels

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Lost circulation is a world-class problem, and the contradiction between plugging and unplugging in reservoirs is a problem that needs to be solved urgently. The traditional LCM is not suitable for reservoirs and the complex subsequent operations. Currently, a self-degrading plugging material is proposed. In this paper, a new self-degradation plugging material, CKS-DPPG, was prepared by AM, GG, nano silica, and PEGDA. The effects of reactant concentration, pH, mineralization, etc., on the swelling and degradation performance of CKS-DPPG were investigated. The plugging capacity was tested by fracture plugging equipment, and the mechanism of self-degradation was revealed. The results show that the CKS-DPPG reached a 50% degradation rate in 54 h and complete degradation in 106 h at 80 °C and pH = 8. Low temperatures, high mineralization, and weak alkaline conditions prolong the complete degradation time of CKS-DPPG, which facilitates subsequent operations. The simulation of the 3 mm opening fracture plugging experiment showed that the pressure-bearing capacity reached 6.85 MPa and that a 0.16 MPa pressure difference could unplug after degradation. The ester bond of PEGDA is hydrolyzed under high-temperature conditions, and the spatial three-dimensional structure of CKS-DPPG becomes linear. The CKS-DPPG can effectively reduce subsequent unplugging operations and lower production costs.

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Section: Effect Of Mineralization On Swelling and Degradation Perform...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preparation and Degradation Performance Study of P(AM/GG/PEGDA) Nanocomposite Self-Degradation Gel Plugging Material

Bao,

Liu,

Zhang

et al. 2023

Gels

View full text Add to dashboard Cite

show abstract

“…In another study, Alsaihati et al [18] proposed an MLbased solution using the mechanical surface parameters for forecasting loss of circulation rate (LCR) during drilling based on mechanical surface parameters and active pit volume measurements. e authors collected the data from seven wells experiencing extreme or partial LCR.…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells

Aslam

Khan

Alansari

et al. 2022

Applied Computational Intelligence and Soft Computing

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The worldwide demand for oil has been rising rapidly for many decades, being the first indicator of economic development. Oil is extracted from underneath reservoirs found below land or ocean using oil wells. An offshore oil well is an oil well type where a wellbore is drilled underneath the ocean bed to obtain oil to the surface that demands more stability than other oil wells. The sensors of oil wells generate massive amounts of multivariate time-series data for surveillance engineers to analyze manually and have continuous insight into drilling operations. The manual analysis of data is challenging and time-consuming. Additionally, it can lead to several faulty events that could increase costs and production losses since the engineers tend to focus on the analysis rather than detecting the faulty events. Recently, machine learning (ML) techniques have significantly solved enormous real-time data anomaly problems by decreasing the data engineers’ interaction processes. Accordingly, this study aimed to utilize ML techniques to reduce the time spent manually to establish rules that detect abnormalities in oil wells, leading to rapid and more precise detection. Four ML algorithms were utilized, including random forest (RF), logistic regression (LR), k-nearest neighbor (K-NN), and decision tree (DT). The dataset used in this study suffers from the class imbalance issue; therefore, experiments were conducted using the original and sampled datasets. The empirical results demonstrated promising outcomes, where RF achieved the highest accuracy, recall, precision, F1-score, and AUC of 99.60%, 99.64%, 99.91%, 99.77%, and 1.00, respectively, using the sampled data, and 99.84%, 99.91%, 99.91%, 99.91%, and 1.00, respectively, using the original data. Besides, the study employed Explainable Artificial Intelligence (XAI) to enable surveillance engineers to interpret black box models to understand the causes of abnormalities. The proposed models can be used to successfully identify anomalous events in the oil wells.

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Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

Elmousalami,

Sakr

2024

J Petrol Explor Prod Technol

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Lost circulation and mud losses cause 10 to 20% of the cost of drilling operations under extreme pressure and temperature conditions. Therefore, this research introduces an integrated system for an automated lost circulation severity classification and mitigation system (ALCSCMS). This proposed system allows decision makers to reliability predict lost circulation severity (LCS) based on a few drilling drivers before starting drilling operations. The proposed system developed and compared a total of 11 ensemble machine learning (EML) based on collection 65,377 observations, the data was pre-processed, cleaned, and normalized to be filtered using factor analysis. For each generated algorithm, the proposed system performed Bayesian optimization to acquire the best possible results. As a result, the optimized random forests (RF) model algorithm was the optimal model for classification at 100% classification accuracy based on testing data set. Mitigation optimization model based on genetic algorithm has been incorporated to convert high severe classes into acceptable classes of lost circulation. The system classifies the LCS into 5 classes where the classes from 2 to 4 are converted to be class 0 or 1 to minimize lost circulation severity by optimizing the input parameters. Therefore, the proposed model is reliable to predict and mitigate lost circulation during drilling operations. The main drivers that served as LCS inputs were explained using the SHapley Additive exPlanations (SHAP) approach.

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Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation

Cited by 10 publications

References 45 publications

Preparation and Degradation Performance Study of P(AM/GG/PEGDA) Nanocomposite Self-Degradation Gel Plugging Material

Preparation and Degradation Performance Study of P(AM/GG/PEGDA) Nanocomposite Self-Degradation Gel Plugging Material

Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells

Automated lost circulation severity classification and mitigation system using explainable Bayesian optimized ensemble learning algorithms

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