Method and Apparatus to Reduce the Probability of Differential Sticking

Amanullah, Md.; Al-Arfaj, Mohammad K

doi:10.2118/180506-ms

Cited by 7 publications

(7 citation statements)

References 10 publications

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“… 64 − 66 Simultaneously, the thinner filter cake resulting from perlite addition decreases the possibility of differential sticking. 67 Moreover, thinner filter cake makes the filter cake removal process easier, eliminating further complications to cementing and casing operations 68 and minimizing the nonproductive time (NPT). 69 The filtration results also support the findings of the previous study 52 conducted by Bageri et al, studying the effect of perlite particles on the filtration properties.…”

Section: Resultsmentioning

confidence: 99%

Impact of Perlite on the Properties and Stability of Water-Based Mud in Elevated-Temperature Applications

et al. 2020

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Barite settling is one of the common drilling fluid issues encountered while drilling deep wells. In this study, the effect of perlite on the properties and stability of water-based drilling fluid was investigated. Perlite is an inexpensive additive used in different industrial applications such as bricks, concrete, thermal insulators, sludge absorbents, fillers, tiles, ruminants, and poultry. Perlite additive was also introduced to the oil industry in drilling applications as an effective fluid loss control agent to reduce the drilling fluid invasion into the formations. Perlite was added to the drilling fluid in various concentrations, ranging between 0 and 3.0 lb/bbl. The sag test was performed to assess the drilling fluid’s stability under dynamic and static conditions at a temperature of 120/250 °F. Then, the impact of perlite on the properties of drilling fluid was assessed by measuring the density and pH at room temperature. While the rheological, viscoelastic, and filtration properties were evaluated at 250 °F. This study showed that an increase in perlite concentration, from 0 to 3 lb/bbl, slightly reduced the pH of the drilling mud; however, all of the values were within the acceptable pH range (9–11). In contrast, this concentration of perlite had an immeasurable impact on drilling fluid density. Perlite enhanced the drilling fluid’s homogeneity and stability by reducing the dynamic and static sag factors, and 3.0 lb/bbl perlite was adequate to eliminate barite sag at a temperature up to 250 °F. Perlite was found to be effective in improving the rheological and viscoelastic properties. A significant enhancement of filtration properties was observed by the reduction in filtrate volume and filter cake thickness by 64 and 31%, respectively.

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Section: Resultsmentioning

confidence: 99%

Impact of Perlite on the Properties and Stability of Water-Based Mud in Elevated-Temperature Applications

et al. 2020

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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%

“…Mechanical sticking can be due to various issues such as pack-off and bridging as well as wellbore geometry [1]. These two mechanisms can be triggered by factors such as poor bore-hole cleaning, poor mud rheology, wellbore instability, improper drilling practices, the quality of the mud-cake and excessive reaming or back-reaming [8]. It is essential to understand the theory and factors involved that drive these mechanisms so that appropriate actions can be taken to reduce the risk of stuck pipes.…”

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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Using the well plan™ software model to determine the optimal hole cleaning to reduce mechanical pipe sticking in a Southern Iraqi oil field

Issa,

Alrazzaq

2023

2nd International Conference on Applied Research and Engineering (Icarae2022)

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Method and Apparatus to Reduce the Probability of Differential Sticking

Cited by 7 publications

References 10 publications

Impact of Perlite on the Properties and Stability of Water-Based Mud in Elevated-Temperature Applications

Impact of Perlite on the Properties and Stability of Water-Based Mud in Elevated-Temperature Applications

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

Using the well plan™ software model to determine the optimal hole cleaning to reduce mechanical pipe sticking in a Southern Iraqi oil field

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