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Article · International Journal of Emerging Technology and Advanced Engineering · March 1, 2023

Application of Artificial Neural Network to Estimate Rate of Penetration for Geothermal Well Drilling in South Sumatera

Muhammad Taufiq Fathaddin, Sonny Irawan, Taufan Marhaendrajana, Pri Agung Rakhmanto, Marmora Titi Malinda, Asri Nugrahanti

Sonny Irawan
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Abstract

In order to plan an optimum geothermal well drilling scheme, a proper identification of drilling parameters should be well known. Information of the parameters consists of weight on bit (WOB), true vertical depth (TVD), rate of penetration (ROP), foam flowrate (FF), and rotary speed (N). The valuable information can be provided by the drilled geothermal wells. Correlation of the drilling parameters is then obtained based on the information. The application of Artificial Neural (ANN) Network is needed since the relationships among the parameters are very complex and nonlinear. Moreover, the relationships are not easily known. In this paper, Artificial Neural Network was promoted to estimate penetration rate. Data were obtained from three wells at a field in South Sumatera, Indonesia. Three ANN models were generated. Each model includes different input parameters. Based on the comparison results, the ANN-3 model has the best level of accuracy with the average values of the parameters MAE, MARE, MSE, ARMSE, and the correlation coefficients are 0.8883, 9.54%, 1.1878, 1.0825, and 0.9938 respectively. ANN models can play a role in identifying parameters that affect the characteristics of penetration rate. Keywords—Drilling, WOB, Geothermal, ROP, ANN.

Drilling and Well EngineeringTunneling and Rock MechanicsAdvanced machining processes and optimizationrate of penetrationgeothermal drillingartificial neural networks

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