Drill Bit Selection Optimization Based on Rate of Penetration: Application of Artificial Neural Networks and Genetic Algorithms

Abstract

The drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the modeling part, a one-dimension mechanical earth model (1D MEM) parameters, drilling fluid properties, and rig- and bit-related parameters, were included as inputs. The optimizing process was then performed to propose the optimum drilling parameters to select the drilling bit that provides the maximum possible ROP. To achieve this, the corresponding mathematical function of the ANNs model was implemented in a procedure using the genetic algorithm (GA) to obtain operating parameters that lead to maximum ROP. The output will propose an optimal bit selection that provides the maximum ROP along with the best drilling parameters. The statistical analysis of the predicted bit types and optimum drilling parameters comparing the actual flied measured values showed a low root mean square error (RMSE), low average absolute percentage error (AAPE), and high correction coefficient (R2). The proposed methodology provides drilling engineers with more choices to determine the best-case scenario for planning and/or drilling future wells. Meanwhile, the newly developed model can be used in optimizing the drilling parameters, maximizing ROP, estimating the drilling time, and eventually reducing the total field development expenses.

Intelligent Drilling of Oil and Gas Wells Using Response Surface Methodology and Artificial Bee Colony

“…They selected the drill bit types based on the optimum values of ROP and drilling variables. Abbas et al [9] also supported the notion of drill bit selection depending upon optimum values of ROP using ANN and GA. Here, ANN was primarily utilized for the development of the objective function and GA was utilized for optimization of ROP objective function for the drill bit selection.…”

“…The dullness of the drill bit is a crucial factor as if the bit wears fast, it adds to drilling cost and greater time consumption, which indirectly affects the economics of drilling operation. So, one needs to carefully select the bit by evaluating data, as it will lead to extra cost to projects [9].…”

“…Thus, various empirical and data-driven models have been developed based on the known relationships between drilling variables to select the most suitable bit types. Recently, data-driven intelligent models have been utilized to find suitable types of drill bits [4][5][6][7][8][9]. These models are reported to be more accurate as they learn from previous well data, defying traditional methods for selecting the appropriate drill bit [9].…”

“…Recently, data-driven intelligent models have been utilized to find suitable types of drill bits [4][5][6][7][8][9]. These models are reported to be more accurate as they learn from previous well data, defying traditional methods for selecting the appropriate drill bit [9]. However, researchers reported several issues related to the models, which are critically reviewed in the 'Literature review' section of the paper [4][5][6][7][8][9].…”

Drill Bit Selection in a Formation With Different Sedimentary Facies Using the Markov Chain: A Case Study at One of the Oil Fields in the South of Iran

“…There are a lot of parameters in drilling operations that must be evaluated and designed to improve the efficiency of the drilling process (Edalatkhah et al, 2010;Karadzhova, 2014). One of these challenges is selecting the proper bit for different formations (Abbas et al, 2019). The process of drilling a well on the ground requires a drill bit.…”