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

Abstract

The oil and gas industry plays a vital role in meeting the ever-growing energy demand of the human race needed for its sustainable existence. Newer unconventional wells are drilled for the extraction of hydrocarbons that requires advanced innovations to encounter the challenges associated with the drilling operations. The type of drill bits utilized in any drilling operation has an economical influence on the overall drilling operation. The selection of suitable drill bits is a challenging task for driller while planning for new wells. Usually, when it comes to deciding the drill bit type, generally, the data of previously drilled wells present in similar geological formation are analyzed manually, making it subjective, erroneous, and time consuming. Therefore, the main objective of this study was to propose an automatic data-driven bit type selection method for drilling the target formation based on the Optimum Penetration Rate (ROP). Response Surface Methodology (RSM) and Artificial Bee Colony (ABC) have been utilized to develop a new data-driven modeling approach for the selection of optimum bit type. Data from three nearby Norwegian wells have been utilized for the testing of the proposed approach. RSM has been implemented to generate the objective function for ROP due to its strong data-fitting characteristic, while ABC has been utilized to locate the global optimal value of ROP. The proposed model has been generated with a 95% confidence level and compared with the existing model of Artificial Neural Network and Genetic Algorithm. The proposed approach can also be applied over any other geological field to automate the drill bit selection, which can minimize human error and drilling cost. The United Nations Development Programme also promotes innovations that are economical for industrial sectors and human sustainability.

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