Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways

Abstract

Artificial Intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and oil and gas upstream industry is no exception to it. AI involves the use of sophisticated networking tools and algorithms in solving multifaceted problems in a way that imitates human intellect, with the aim of enabling computers and machines to execute tasks that could earlier be carried out only through demanding human brainstorming. Unlike other simpler computational automations, AI enables the designed tools to "learn" through repeated operation, thereby continuously refining the computing capabilities as more data is fed into the system. Over the years, AI has led to significant designing and computation optimizations in the global Petroleum Exploration and Production (E&P) industry, and its applications have only continued to grow with the advent of modern drilling and production technologies. Tools such as Artificial Neural Networks (ANN), Generic Algorithms, Support Vector Machines and Fuzzy Logic have a historic connection with the E & P industry for more than 16 years now, with the first application dated in 1989 for development of an intelligent reservoir simulator interface, and for well-log interpretation and drill bit diagnosis through neural networks. Devices and softwares with basis from the above mentioned AI tools have been proposed to abridge the technology gaps hindering automated execution and monitoring of key reservoir simulation, drilling and completion procedures including seismic pattern recognition, reservoir characterisation and history matching, permeability and porosity prediction, PVT analysis, drill bits diagnosis, overtime well pressure-drop estimation, well production optimization, well performance projection, well / field portfolio management and quick, logical decision making in critical and expensive drilling operations. The paper reviews and analyzes this successful integration of AI techniques as the missing piece of the puzzle in many reservoir, drilling and production aspects. It provides an update on the level of AI involvement in service operations and the application trends in the industry. A summary of various research papers and reports associated with AI usage in the upstream industry as well as its limitations has been presented.

Prospects of Intelligent Oil and Gas Fields

“…These problems include the intellectualization of analysis of large amounts of data collected from oil and gas fields, the intellectualization of drilling process, the forecast of reserves and optimization of oil and gas production, the optimization of the location and management of oil and gas fields, etc. For solving these and other problems, artificial neural networks [15][16][17][18][19], fuzzy logic [20][21][22][23][24], expert systems [25][26][27][28], machine learning methods [29], intelligent agents [30,31], genetic algorithms [32][33][34][35], data extracting methods [36,37], case-based reasoning -CBR [38][39][40], etc.…”

“…Today ANN is widely used to solve many problems in the oil industry. For example, ANNs are used to forecast oil [17] and gas reserves and to optimize oil and gas production in fields [18], when simulating drilling and oil and gas production [19], etc.…”

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

“…Fuzzy logic is more like everyday experience as related to human decision-making. The fuzzy inference system (FIS) logic is very helpful in regulating and managing incomplete information (Bello et al 2016). Hamdi-Cherif (2010) cited by Zoveidavianpoor et al (2012) listed merits and demerits of Fuzzy Logic to include the following: (a) Merits: FL mathematical models are veritable means for transforming human reasoning into useful set of rules that could easily be implemented using computer; fuzzy logic models are capable of handling any non-linear problems; FL models are vital techniques in addressing complex stochastic problems that cannot be solved by ordinary mathematical models; fuzzy logic models are robust and find application under information deficit scenarios; fuzzy models are adjudged as appropriate tools in generic decision-making.…”

Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy

“…Multiple algorithms can be combined taking competitive advantages of each algorithm to develop an ensemble AI tools. AI techniques can be deployed to solve routine boring tasks which would be completed faster with minimal errors and defects than human [21].…”