Applications of Artificial Intelligence Techniques in Optimizing Drilling

Abstract

Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved.

How the Electric Toothbrush, Search Engine, Smartphone, Social Media and Artificial Intelligence Decision-Making Processes Amplify the Exercise of Power at State and Global Levels: A Media Ecology Analysis

“…This stems from the fact that optimisation is a logic that underlies the operation of AI decision-making processes (Badar, Umre and Junghare 2014, 39). Optimisation involves the use of criteria for the decision-making process which enable the designer to attain the desired goal to the greatest extent possible while satisfying the constraints (Koopialipoor and Noorbakhsh 2020). In practice, this decisionmaking process involves selecting individuals with the highest score on an attribute for access to an opportunity (Barocas and Selbst 2016, 679).…”

Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

“…Two indicators of root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were selected to assess the quality of the suggested ML techniques concerning forecasting the various problem [21,22,29,46,101,102]. The indicators used in the ML approach to defining training and testing groups are explained below:…”

Advanced Geological Prediction

“…At present, the location, scale, and distribution of most unfavorable geological bodies have been detected by corresponding technologies, but it is still impossible to determine the rock mechanic properties of unfavorable geological bodies, the detection range is mostly limited to two-dimensional plane, the detection effect is not ideal in complex environment with large disturbance, the construction area is occupied and the time-consuming is longer, etc., which are still urgent problems to be solved in the field of advanced forecasting and detection. In addition, the development direction of future advanced geological prediction technology is the ultra-long leading horizontal drilling technology, the advanced geological prediction method while drilling, the fine imaging technology of multi-physical field information joint inversion, the quantitative technology of advanced prediction, the real-time advanced prediction technology, and the intelligent interpretation technology of advanced prediction [20][21][22][23]. At the same time, in order to obtain more accurate quantitative information in the tunnel, it is necessary to combine advanced geological prediction and surface exploration technology; use aviation electromagnetic exploration technology and system and intelligent geologic deep drilling; adapt to complex environmental geophysical techniques as breakthrough points; and form the comprehensive exploration and prediction technology that includes space-borne remote sensing, airborne exploration, and surface geophysical exploration and intelligent drilling.…”