Evaluation and optimization of water-salt based drilling fluids for slim-hole wells in one of Iranian central oil fields

Jafarifar, Iman; Dehkordi, Babak Karimi; Abbasi, Hassan; Schaffie, Mahin; Ranjbar, Mohammad

doi:10.1016/j.upstre.2020.100010

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…inch; yet this value may range from 3 1/2 to 6 inch in a slim-hole (Jafarifar et al, 2020;Azari and Soliman, 1997;Enilari et al, 2006). The pressure losses are significantly affected by the reduced annular clearance in slim-holes, while 90% of the pressure losses take place in the bit nozzles and the drill string in a conventional well.…”

Section: Introductionmentioning

confidence: 99%

“…Reduction of the overall drilling costs has been mentioned among the most significant issues in the petroleum industry (Brunsman et al, 1994;De Sousa et al, 1999). One of the most reliable methods to attain a considerable decrease in costs is the use of slim-hole drilling (Jafarifar et al, 2020;Pks and Yerubandi, 2010;Scott and Earl, 1961;Cohen et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

“…Their research underscored the performance assessment of the considered algorithms and their pitfalls to accurately predict pressure loss. Jafarifar et al (2020) was conducted a research in one of Iranian central oil field to study the development of water-salt based drilling fluids for slim-hole wells. In their research, the Bingham Plastic and Power-Law models were used to evaluate each composition and then the results obtained for each model were compared.…”

Section: Introductionmentioning

confidence: 99%

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Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

et al. 2023

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In order to have a better control over the drilling process and reduce the overall cost of this drilling operation, engineers have tried to use soft computing (SC) techniques to conduct the preestimation of drilling events. It is critically important to estimate the annular pressure losses (APL) for non-Newtonian drilling muds within annulus in order to specify pump rates and also to be able to choose the most appropriate mud pump systems while conducting the drilling operations. To develop the vigorous and exact models to enable the prediction of APL, two popular models were employed, i.e., multilayer perceptron (MLP) [optimized by levenbergmarquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG), Resilient back propagation (RB), and broyden fletcher goldfarb shanno (BFGS)] and radial basis function (RBF). Subsequently, applying a committee machine intelligent system (CMIS), the four top models were combined into a unit paradigm. Several tools such as error distribution diagram, cross plot, trend analysis, and cumulative frequency diagram were used in conjunction with statistical calculation to assess the efficiency of models. Consequently, the CMIS model was introduced as the most exact technique which has the greatest coefficient of determination (R 2 close to one) as well as the lowest root mean square error (RMSE close to zero) for the tested dataset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Jafarifar

Abbasi

Kiani

2022

Preprint

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In order to have a better control over the drilling process and reduce the overall cost of this drilling operation, engineers have tried to use soft computing (SC) techniques to conduct the pre-estimation of drilling events. It is critically important to estimate the annular pressure losses (APL) for non-Newtonian drilling muds within annulus in order to specify pump rates and also to be able to choose the most appropriate mud pump systems while conducting the drilling operations. To develop the vigorous and exact models to enable the prediction of APL, two popular models were employed, i.e., multilayer perceptron (MLP) [optimized by levenberg-marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG), Resilient back propagation (RB), and broyden fletcher goldfarb shanno (BFGS)] and radial basis function (RBF). Subsequently, applying a committee machine intelligent system (CMIS), the four top models were combined into a unit paradigm. Several tools such as error distribution diagram, cross plot, trend analysis, and cumulative frequency diagram were used in conjunction with statistical calculation to assess the efficiency of models. Consequently, the CMIS model was introduced as the most exact technique which has the greatest coefficient of determination (R2 close to one) as well as the lowest root mean square error (RMSE close to zero) for the tested dataset.

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Application of soft computing approaches for modeling fluid transport ratio of slim-hole wells in one of Iranian central oil fields

Jafarifar

Simi

2023

Earth Sci Inform

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Evaluation and optimization of water-salt based drilling fluids for slim-hole wells in one of Iranian central oil fields

Cited by 8 publications

References 5 publications

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Application of soft computing approaches for modeling fluid transport ratio of slim-hole wells in one of Iranian central oil fields

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