Career development

Discover a Career in Petroleum Reservoir Simulation

The author discusses reservoir management and simulation and what reservoir engineers contribute.

Abstract waves

Petroleum reservoir simulation is the application of software designed to model fluid flow in petroleum reservoirs. I first encountered reservoir simulation while working on a project to store solar energy in an aquifer. During the first 3 years of my career, I performed a model study of a relatively small oil reservoir, reviewed the status of naturally fractured reservoir simulators, conducted an analysis of multidimensional numerical dispersion, evaluated the feasibility of developing geopressured/geothermal reservoirs, and compared the relative merits of different chemical flood processes. I thus found that a career in reservoir simulation would provide interesting challenges in a wide range of applications.

The process of petroleum reservoir simulation is an aspect of reservoir management. Modern reservoir management can be broadly defined as a continuous process that optimizes the interaction between data and decision-making during the life cycle of a field. This definition covers the management of many types of reservoir systems, including hydrocarbon reservoirs, geothermal reservoirs, and reservoirs used for geological sequestration.

Hydrocarbon reservoir management includes recovery of conventional and unconventional oil and gas resources using a variety of processes including primary recovery, waterflooding, immiscible gas injection, and enhanced oil recovery. Oil and gas fields are considered conventional sources of fossil fuels. Unconventional sources of fossil fuels include coal gas, tight gas, shale gas, gas hydrates, shale oil, and tar sands. Unconventional sources are becoming a more important part of the global energy mix as the cost of development decreases and the price of oil increases.

Reservoir management concepts, tools, and principles are applicable to subsurface resources other than oil and gas. For example, the management of geothermal reservoirs and reservoirs used for geologic sequestration can be analyzed using reservoir simulation. Geologic sequestration involves the capture, separation, and long-term storage of greenhouse gases or other gas pollutants in a subsurface environment. Carbon dioxide injection in a coal seam can be both an enhanced gas recovery process and a geologic sequestration process.

Many disciplines contribute to the reservoir management process. In the case of a hydrocarbon reservoir, successful reservoir management requires understanding the structure of the reservoir, the distribution of fluids within the reservoir, drilling and maintaining wells that can produce fluids from the reservoir, transport and processing of produced fluids, refining and marketing the fluids, safely abandoning the reservoir when it can no longer produce, and mitigating the environmental impact of operations throughout the reservoir’s life cycle. Properly constituted asset management teams include personnel—often specialists—with the expertise needed to accomplish all these tasks. They must be able to communicate with one another and work together toward a common objective. Reservoir simulation helps integrate information from all the disciplines and provides quantitative reservoir performance forecasts.

According to the SPE competency matrix, reservoir engineers should be able to evaluate reservoir performance using reservoir simulation. At a minimum, the reservoir engineer should be able to understand and apply reservoir simulation to analyze reservoir performance and optimize reservoir development. A reservoir engineer should be able to use basic reservoir engineering principles, including flow through porous media, relative permeability, nodal analysis, and multiphase flow to evaluate single well applications and black oil or gas reservoirs. A higher level of competence is achieved when the reservoir engineer acquires knowledge of specialized simulation techniques (such as matrix solution methods, numerical analysis, vectorization, finite element/difference analysis, and parallel processing), and is able to find new opportunities, such as areas in the reservoir that are unswept or inefficiently drained, or identify new well locations with geological input.

In practice, careers in reservoir simulation often separate into one of two paths: software development or software applications. If you choose to specialize in reservoir simulator development, your career will benefit from knowledge of numerical methods and computer programming. A career in reservoir simulator applications is facilitated by a broad background in reservoir asset management team skills, involving an understanding of contributions from the geosciences, drilling, production, facilities, and economics.

Reservoir simulation studies are important when significant choices must be made. The choices can range from “business as usual” to major changes in investment strategy. By studying a range of scenarios, the reservoir simulation engineer is expected to provide decision makers with information that can help them decide how to economically commit limited resources to activities that can achieve management objectives. These objectives may involve the planning of a single well, or the development of a world-class reservoir.

Reservoir flow modeling is the most sophisticated methodology available for generating production profiles. A production profile presents fluid production as a function of time. By combining production profiles with hydrocarbon price forecasts, it is possible to create cash flow projections. The combination of a production profile from flow modeling and a price forecast from economic modeling yields economic forecasts that can be used by decision makers to compare the economic value of competing reservoir management concepts.

Reservoir management is most effective when as much relevant data as possible from all sources is collected and integrated into a reservoir management study. This requires the acquisition and management of data that can be expensive to acquire. As a consequence, reservoir simulation engineers need to estimate the value of the data by considering the cost of acquiring it in relation to the benefits that would result from its acquisition. They are involved in decisions that prioritize data needs based on project objectives, relevance, cost, and impact.

One of the critical tasks of reservoir management is the acquisition and maintenance of an up-to-date database. The reservoir simulation engineer can help coordinate activities for an asset management team by gathering the resources needed to determine the optimum plan for operating a field. Collecting data for a reservoir flow model is a good way to ensure that every important technical variable is considered as data is collected from the many disciplines that contribute to reservoir management. If model performance is especially sensitive to a particular parameter, then a plan should be made to reduce uncertainty in the parameter.

Different workflows exist for designing, implementing, and executing reservoir projects. A typical workflow needs to identify project opportunities; generate and evaluate alternatives; select and design the desired alternative; implement the alternative; operate the alternative over the life of the project, including abandonment; and then evaluate the success of the project so lessons can be learned and applied to future projects. By building and applying reservoir flow models—also known as dynamic models—the reservoir simulation engineer can play a significant role in comparing alternatives, selecting the optimum reservoir management plan, and assessing the success of the project as it is being implemented and operated.

Modern flow modeling recognizes two types of workflows: greenfield workflow and brownfield workflow. Greenfields include discovered, undeveloped fields, and fields that have been discovered and delineated but are undeveloped. Brownfields are fields with significant development history. Both types of workflows are intended to be systematic procedures for quantifying uncertainty.

Two brownfield workflows are in current industry use: deterministic reservoir forecasting and probabilistic reservoir forecasting. In deterministic reservoir forecasting, a single reservoir realization is selected and matched to historical performance.

In probabilistic reservoir forecasting, a statistically significant collection, or ensemble, of reservoir realizations is prepared. Dynamic models are run for each possible realization and the results of the dynamic model runs are then compared to the reservoir’s historical performance. Modern reservoir simulation engineers need to understand the application of probability and statistics in flow model workflows.

Students graduating today can expect their careers to last to 2040 or beyond. Global demand for energy and societal demand for sustainable development have expanded the range of applications that come under the purview of reservoir simulation. Many companies recognize that the global energy mix is undergoing a transition from an energy portfolio dominated by fossil fuels to an energy portfolio that includes a wide range of sources. A career in petroleum reservoir simulation gives you knowledge and skills that can be applied in today’s oil and gas industry, and tomorrow’s energy industry. 

John R. Fanchi is a professor in the department of engineering at Texas Christian University, Fort Worth, Texas. He teaches courses in energy and engineering. Previously, Fanchi taught at the Colorado School of Mines, and worked in the technology centers of four energy companies. Fanchi is the author of several books, including Integrated Reservoir Asset Management (Elsevier, 2010), Energy in the 21st Century, 2nd Edition (World Scientific, 2011), Principles of Applied Reservoir Simulation, 3rd Edition (Elsevier, 2006), and Math Refresher for Scientists and Engineers, 3rd Edition (Wiley, 2006). He co-edited the General Engineering volume of the Petroleum Engineering Handbook (SPE, 2006, Volume 1). Fanchi earned a PhD in physics from the University of Houston.