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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

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Abstract

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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Availability of data and materials

All the data in this manuscript are displayed in the supplementary file, otherwise contact me by email (imanjafarifar96@gmail.com).

Abbreviations

APL:

Annular pressure loss

AV:

Apparent viscosity

ANN:

Artificial neural network

ARE:

Absolute relative error

AAPRE:

Average absolute percent relative error

BR:

Bayesian regularization

BFGS:

Broyden–Fletcher–Goldfarb–Shanno

BNN:

Bayesian neural network

BC:

Broyden class

CMIS:

Committee machine intelligent system

DFP:

Davidon–Fletcher–Powell

ID:

Inside diameter

ICOFC:

Iranian Central Oil Fields Company

LM:

Levenberg–Marquardt

MW:

Mud weight

MLP:

Multilayer perceptron

OD:

Outside diameter

PRE:

Percent relative error

PV:

Plastic viscosity

QN:

Quasi-Newton

RBF:

Radial basis function

RB:

Resilient backpropagation algorithm

RMSE:

Root-mean-square error

RF:

Random forest

R 2 :

Coefficient of determination

SC:

Soft computing

SCG:

Scaled conjugate gradient

SR1:

Symmetric rank one

SD:

Standard deviation

SVM:

Support vector machines

V :

Velocity of fluid

YP:

Yield point

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Acknowledgements

The authors would like to thank Iranian Central Oil Fields Company (ICOFC) for supporting this study. The authors are also grateful to Dr. Robello Samuel, Chief Technical Advisor and Halliburton Technology Fellow for guidance and assistance.

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Authors and Affiliations

  1. Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Iman Jafarifar

  2. Department of Petroleum Engineering, University of Tehran, Tehran, Iran

    Ali Simi

  3. National Irainian Oil Company, Tehran, Iran

    Hassan Abbasi

  4. Department of Chemical and Petroleum Engineering, University of Calgary, Alberta, Canada

    Mohammad Kiani

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  1. Iman Jafarifar
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Correspondence to Iman Jafarifar.

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Jafarifar, I., Simi, A., Abbasi, H. et al. Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields. Soft Comput 27, 16125–16142 (2023). https://doi.org/10.1007/s00500-023-07986-4

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