Description: Petroleum Production Engineering, A Computer-Assisted Approach provides handy guidelines to designing, analyzing and optimizing petroleum production systems. Broken into four parts, this book covers the full scope of petroleum production engineering, featuring stepwise calculations and computer-based spreadsheet programs. Part one contains discussions of petroleum production engineering fundamentals, empirical models for production decline analysis, and the performance of oil and natural gas wells. Part two presents principles of designing and selecting the main components of petroleum production systems including: well tubing, separation and dehydration systems, liquid pumps, gas compressors, and pipelines for oil and gas transportation. Part three introduces artificial lift methods, including sucker rod pumping systems, gas lift technology, electrical submersible pumps and other artificial lift systems. Part four is comprised of production enhancement techniques including, identifying well problems, designing acidizing jobs, guidelines to hydraulic fracturing and job evaluation techniques, and production optimization techniques. Provides complete coverage of the latest techniques used for designing and analyzing petroleum production systems Increases efficiency and addresses common problems by utilizing the computer-based solutions discussed within the book Presents principles of designing and selecting the main components of petroleum production systems

In this review, we survey the widespread use of optimisation or mathematical programming approaches in the upstream sector of the petroleum industry, specifically to problems in the area of (1) production systems design and operations, (2) lift gas and production rate allocation and (3) reservoir development, planning, management and optimisation. Early applications have adopted Linear Programming (LP) alongside heuristics-based methods, but the recent ongoing explosion in computing power and advances in optimisation, simulation and computational techniques have enabled the adoption of increasingly complex models. These formulations include non-linear programming and Mixed-Integer Linear (MILP) and Non-Linear (MINLP) programming models. Within these representations, various algorithms and approaches have been employed, for example, metaheuristics such as genetic algorithms to address non-smooth objective functions; techniques for simultaneous decision making in design, planning and scheduling and stochastic programming to handle uncertainty in reservoir information, with the ultimate aim of improving solution quality while reducing computational intensity.

Proceedings of the first international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA/AIE '88, 1988

An expert system known as the Automated Project Design System (APDS TM) has been developed to assist process and facilities engineers in performing preliminary feasibility studies, optimization studies, and provide the basic information required for the initiation of the detailed design for offshore oil and gas production facilities. Given the feedstock and product specifications, the expert system produces a preliminary process flow diagram showing all major pieces of equipment and determines all utility system requirements. Rigorous material and energy balance calculations are done via an interface to a conventional process simulator. All of the required equipment is sized and an estimated cost for each item is produced. An overall cost estimate for the production facility is also produced. Various reports, drawings, and equipment datasheets are generated to summarize the final results. The system is extremely flexible and allows the user to easily change input parameters, to customize equipment sizes and costs and to produce customized reports containing whatever information is desired. APDS TM runs on Symbolics 3600 series computers. APDS TM uses Intellicorp's Knowledge Engineering Environment (KEE TM) expert system development shell and makes extensive use of Symbolics ZetaLisp and Common Lisp. The system also uses Hyprotech's Hysim TM process simulator to perform process calculations. It runs on a PC AT computer which is linked to the Symbolics computer. * Devise a processing scheme to achieve the desired results.

The upstream of the petroleum industry involves itself in the business of oil and gas exploration and production (E & P) activities. While the exploration activities find oil and gas reserves, the production activities deliver oil and gas to the downstream of the industry (i.e., processing plants). The petroleum production is definitely the heart of the petroleum industry. Petroleum production engineering is that part of petroleum engineering that attempts to maximize oil and gas production in a cost-effective manner. To achieve this objective, production engineers need to have a thorough understanding of the petroleum production systems with which they work. To perform their job correctly, production engineers should have solid background and sound knowledge about the properties of fluids they produce and working principles of all the major components of producing wells and surface facilities. This part of the book provides graduating production engineers with fundamentals of petroleum production engineering. Materials are presented in the following eight chapters: Chapter

Engineering with Computers, 2003

Typically, the optimization of oil production systems is conducted as a non-systematic effort in the form of trial and error processes for determining the combination of variables that leads to an optimal behavior of the system under consideration. An optimal or near optimal selection of oil production system parameters could significantly decrease costs and add value. This paper presents a solution methodology for the optimization of integrated oil production systems at the design and operational levels, involving the coupled execution of simulation models and optimization algorithms (SQP and DIRECT). The optimization refers to the maximization of performance measures such as revenue present value or cumulative oil production as objective functions, and tubing diameter, choke diameter, pipeline diameter, and oil flow rate as optimization variables. The reference configuration of the oil production system includes models for the reservoir, tubing, choke, separator, and business economics. The optimization algorithms Sequential Quadratic Programming (SQP) and DIRECT are considered as state-of-the-art in non-linear programming and global optimization methods, respectively. The proposed solution methodology effectively and efficiently optimizes integrated oil production systems within the context of synthetic case studies, and holds promise to be useful in more general scenarios in the oil industry.

Bulletin of Pure & Applied Sciences- Physics, 2016

Journal of Advanced Research in Dynamical & Control Systems, , 2018

The nature of the oil and gas industry is dynamic. The price of crude oil is moving on roller coaster ride. Companies are finding difficult to predict their business plans and the focus has been shifted on to achieving operational efficiency and well maintaining the existing infrastructure. In addition to these difficulties, environmental regulations are becoming tougher in view of making companies avoiding accidents and to install safety management measures. Safety management issues in Oil and gas industry brought the technological challenges and risk mitigation challenges to the fore front. The goal is to provide better human life and the safety working environment in the oil and gas industry. It is believed that achieving process control will help in mitigating the risks. A crucial realization is that automation in this kind of risky environment will be highly beneficial. The control process involves observing actual performances, comparing it will some standards, and then taking action of observed performance is significantly different from the standard. For this purpose it uses modern computer aided techniques. Objective of this paper is to describe the five elements and their relationship mentioned in Gryna (2007) (17) supported by CRASP methodology. The goal is to support the real-time decision-making by Oil & Gas companies in view of facilitating them with predictive drill maintenance/downtime as well as drilling processes, which need tools that integrate and synthesize diverse data sources into a unified whole. Oil and gas industrial operations need special and specific skills for continuously monitoring and ensuring the smooth functioning of all the operations in the Oil & Gas environment. Recent and most advanced computing technologies such as IoT, Big Data analytics, cloud computing, Wireless Sensor Network technology, the use of sensors, transducers, actuators etc. and use of machine learning algorithms and methods for efficient and effective processing with minimum human intervention are necessary to generate most potential solution for driving the core processes of the Oil & Gas industrial environment

Romanian Journal of Petroleum & Gas Technology, 2021