A Novel Artificial Neural Network-Based Correlation for Evaluating the Rate of Penetration in a Natural Gas Bearing Sandstone Formation: A Case Study in a Middle East Oil Field

Carlos Michel,Ahmad Al-Abduljabbar,Ahmed Abdulhamid Mahmoud,Mahmoud Abughaban,Salaheldin Elkatatny

doi:10.1155/2022/9444076

Carlos Michel, Ahmad Al-Abduljabbar + Show 3 more

Open Access

https://doi.org/10.1155/2022/9444076

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Abstract

This study presented an empirical correlation to estimate the drilling rate of penetration (ROP) while drilling into a sandstone formation. The equation developed in this study was based on the artificial neural networks (ANN) which was learned to assess the ROP from the drilling mechanical parameters. The ANN model was trained on 630 datapoints collected from five different wells; the suggested equation was then tested on 270 datapoints from the same training wells and then validated on three other wells. The results showed that, for the training data, the learned ANN model predicted the ROP with an AAPE of 7.5%. The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with AAPE of 8.1%. The equation was then validated on three wells, and it determined the ROP with AAPEs of 9.0%, 10.7%, and 8.9% in Well-A, Well-B, and Well-D, respectively. Compared with the available empirical equations, the equation developed in this study was most accurate in estimating the ROP.

Highlights

Evaluation of the formation drillability is a critical process that is highly dependent on the speed at which the drillbit will be able to drill through the formation or what is called the rate of penetration (ROP) [1]
The extracted equation was tested on data gathered from the same training wells where it estimated the ROP with average absolute percentage error (AAPE)
Regarding the number of the inputs, the results indicated that for improving the artificial neural networks (ANN) model for ROP prediction, the use of all the five drilling mechanical parameters as inputs is a must, the ANN model should consist of a single training layer associated with five neurons, and the training process will be conducted using trainlm while the data will data transferred from the training layer to the output layer using the pure linear function

Summary

Introduction

Evaluation of the formation drillability is a critical process that is highly dependent on the speed at which the drillbit will be able to drill through the formation or what is called the rate of penetration (ROP) [1]. Alteration of uncontrollable parameters like the drilling fluid types or drill bit size is costly; in addition, modification of any of these parameters affects the others, which complicate predictability of how modification of a single parameter contributes to the change in ROP [4, 5]. Different traditional models were optimized to evaluate the ROP; these models were developed from regression analysis to assess the ROP based on various inputs, the accuracy of these models is significantly affected by the inputs considered [6,7,8]. The first regression model for ROP predicted was suggested by Maurer [9]; the author developed this model to estimate the ROP for the tricone bit based on the DSR, WOB, and the drill bit size only. Bingham [10] conducted several laboratory experiments; based on the results of these experiments, he suggested another ROP model which

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