Stuck Pipe Best Practices – A Challenging Approach to Reducing Stuck Pipe Costs

Muqeem, Muhammad; Weekse, Alexander E.; Al-Hajji, Ali

doi:10.2118/160845-ms

Cited by 47 publications

(8 citation statements)

References 4 publications

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“…Based on the prediction results for the stuck pipes for all the ANN models, the best model is identified as being the ANN model that uses a logistic activation function with the network structure [19,10,8,6,1]. The best ANN model achieved an accuracy of 88.89%, a sensitivity of 91.89% and a specificity of 86.36%.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, model no. 580 has a network topology of [19,10,8,6,1]. The accuracy, sensitivity and specificity were 88.89%, 91.89% and 86.36%, respectively.…”

Section: Figurementioning

confidence: 99%

“…Energies 2020, 13, x FOR PEER REVIEW Figure 5. Topology [19,10,8,6,1] of the best ANN model using the logistic activation function (model no. 580).…”

Section: Figurementioning

confidence: 99%

“…Flat time can be understood as time allocated to carry out activities that does not increase bore-hole depth. According to a study conducted by Saudi Aramco in the year 2012, stuck pipes account for 25% of the NPT or the equivalent of two rig-years' worth of additional cost every year [6]. During a drilling company operation in Latin America, NPT is used to benchmark the severity of stuck pipe incidents [7].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Section: Discussionmentioning

confidence: 99%

“…On the other hand, model no. 580 has a network topology of [19,10,8,6,1]. The accuracy, sensitivity and specificity were 88.89%, 91.89% and 86.36%, respectively.…”

Section: Figurementioning

confidence: 99%

“…Energies 2020, 13, x FOR PEER REVIEW Figure 5. Topology [19,10,8,6,1] of the best ANN model using the logistic activation function (model no. 580).…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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“…In fact, stuck pipes account for about 25% of the nonproductive time. A third of the stuck pipes are due to differential pressure sticking (Muqeem et al 2012).…”

Section: Differential Pressure Stickingmentioning

confidence: 99%

Mudcake growth: Model and implications

Liu

Santamarina

2018

Journal of Petroleum Science and Engineering

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Oil and gas account for 60% of the world's energy consumption. Drilling muds that are used to advance oil and gas wells must be engineered to avoid wellbore integrity problems associated with mud cake formation, to favor cake erosion during cementing, and to prevent partial differential sticking. We developed a robust mud cake growth model for water-based mud based on wide stress-range constitutive equations within a Lagrangian reference system to avoid non-natural moving boundary solutions. The comprehensive mud cake growth model readily accommodates environmental factors (e.g., temperature, pH, and ionic concentration) and defines the yield stress distribution for displacement-erosion analyses. Results show that the mud cake thickness is more sensitive to time than to filtration pressure, therefore, time controls the non-uniform distribution of mudcake thickness during drilling. Long filtration time, high permeability, high salinity, high in-situ temperature and low viscosity exacerbate fluid loss and give rise to thick filter cakes. The analysis of residual cake thickness during cement displacement must take into account the effective stress dependent mudcake formation and the time-dependent mud thixotropy. Thixotropy dominates the mud yield stress at high void ratios, e.g. e>20. The offsetting force that causes differential pressure sticking increases sub-linearly as a power function of the still-time.

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