Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

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.

The technique of drilling a well using the casing string has earned itself a well deserved place as one of the most effective and cost-saving ways to drill and complete a hydrocarbon well; the cornerstone of this is the casing pipe, composed by the pipe body itself and the threaded connection. Normally an API-Buttress type connector is used; nevertheless, it has been proven in both controlled and field conditions that this connector is not completely adequate for Casing Drilling per se, as high torque capacities are demanded. Apart from that, and due to this condition, the constant need for using torque enhancers represents a high limitation to the Drilling with Casing technique itself. For this, Premium-type connectors address this point in a more proper way in form of improved geometries in thread and shoulder profiles and enhanced sealing capabilities. Despite of this, Premium Connections are seldom used in Casing Drilling applications, being them almost relegated to very special, seldom drilling deployments. However, successful use of Premium Connections in Casing Drilling projects offshore Australia, Norway and onshore Mexico are paving the road for an intensive inclusion of Premium Connections in the field; this has also come as the result of the current diversification of the OCTG market for this connectors coming after a better understanding of load distribution and torque "control" by means of improved geometric design combined with the successful integration of axial-symmetric modeling (Finite Element Method) and Full Scale Testing for design and validation of new couplings. In this work, a holistic approach will address the technological highlights making up the past and current market offer and future tendencies in OCTG specialty connectors for casing drilling, understanding this as the direct result of the evolution of this drilling technique and the recent improvements in Full Scale Testing and computer-assisted design. It was observed that the drilling of horizontal sections with a casing string in some field developments worldwide was only possible between 1990-2010 due to important modifications made to Full Scale Testing procedures employed to validate newly designed Premium Connections. In this case, the inclusion of a testing stage with bending broadened the usage spectrum in which some Premium Connection geometries were able to perform well; this serves as a concise, yet significative example of the constant evolution the OCTG sector underwent in the last decades, and also a great example of the superb applicability of Premium Connections in high demanding oilfield operations like drilling a well with casing pipe. This work brings new light into the understanding of the potential of Premium Connections as a standard selection parameter for OCTG used in Casing Drilling, understanding the combined usage both technologies as a clear warrant towards well integrity.

About 70% of the world's oil and gas fields are brownfields with production history of 25 years or older. These makes additional recovery from brownfields a focus of interest for the industry. A handful of known technologies exists which have been applied to brownfield redevelopments in a timely and efficient manner to maximize oil recovery. In this paper, we present an innovative approach to rejuvenate brownfields which relies on the application of direct search methods to place, design and selectively perforate multi-pattern infill wells in unexploited hydrocarbon regions while considering multiple history-matched models and geological uncertainties. The field development optimization (FDO) approach requires the definition of design parameters for efficient well placement and selective perforation in untapped sweet spots using I-J-K coordinates. Fluid saturation distribution from a finite difference reservoir simulator at the end of production history is utilized to identify unexploited regions. Sampling techniques are applied to design parameters to screen the solution space for the best candidates that satisfies the FDO problem. Optimization of selected best candidates are performed using the genetic algorithm (GA), level-set optimizer (LSO) and the covariance matrix adaptation evolution strategy (CMA-ES). In addition, we take into consideration the influence of geological uncertainties on multi-pattern well placement and oil recovery maximization. A partially real field model is used to test proposed FDO approach. Oil recovery maximization and overall optimization time is compared for all three optimization methods evaluated. Using fluid saturation distribution realized after model validation, favourable unexploited regions are identified for multi-pattern well placement by computing and inspecting the product of permeability-by-thickness-by-fluid-saturation per grid block. Optimization algorithms applied to the FDO problem show the tendency for multi-pattern infill wells to seek untapped regions of the reservoir. Furthermore, we parameterize infill well perforations to control the drainage strategy in our test model. Results show that the optimization of perforations delivered better final solutions. Optimization under uncertainty has an effect on the maximization of oil recovery with lesser effect observed on optimum well placement.

Drilling rig selection is one the major bottom line during the development phase of hydrocarbons or geothermal well based on comprehensive consideration of a variety of factors for its purpose. The process of drilling a well is highly cost as it involves hiring a drilling rig and crew for the duration of drilling the well. Problem of drilling rig performance and selection has always been viewed as the most important responsibility during the well design phase of a field development, this is mainly caused due to multiple rig manufacturers in the market that meets the operational conditions but not in accordance most operators design specifications. This solution has always been a complex process as various criteria, known and half known are involved in making final decision. Thus, there is a need for simple, systematic applications/tools that can be used to support the decision making considering a number of selection attributes and their interrelations in making right decisions. This study proposes a peculiar approach, an intelligent decision support application hybridizing distance based approach (DBA) and an artificial intelligence technique, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) that will assist well engineers, E&P operators in contacting multiple appropriate rig manufacturer that will deliver a reliable drilling performance resulting to safety drilling operations, mitigate effect of time delay, environmental friendly and most importantly be economically viable for different application. Currently, the conventional approach for the selection of appropriate drill rig for onshore operational activities is based on method of exclusion associated with engineering experience and lithology of the field to be developed serving as key drive factors. In order to make it scientific, both qualitative and quantitative parameters influencing drill rig selections were first established using database management system and was later implemented using both two techniques, DBA and FTOPSIS. However, for the purpose of this study, selected onshore rigs are considered for the hybrid model implementation. The novelty of this study lies in the application of a hybrid approach to a real E&P industry case. This study has dealt with one of the most important subjects as related to well design planning, providing a better decision for the selection of drill rig using appropriate quantitative techniques. This work also helps to select the best way to proceed in a situation when the outcomes are uncertain. The proposed tool will also set up a benchmarking process providing a feedback to the rig manufacturers for further improvement of their equipments that account to safety, less environmental impact, minimal NPT and better drilling performance operation.