Learning Reservoirs: Adapting to Disruptive Technologies
Authors: Saleri Nansen G. 
Published: June 2014

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering.

Introduction
Extrapolation of current and emerging reservoir-management trends in the industry points to a process that is increasingly multidisciplinary, integrated, technology-based, information-loaded, and real-time. None of these attributes by themselves or in total can, however, guarantee profitability or success in the coming decades. What, then, is the winning formula? If a two-hour flight from New York to London represents a straightforward goal for the next-generation airplane, what is our expectation for the reservoir-management process in the year 2010? In searching for the right path, this article proposes a "learning" reservoir as a model for a continual self-improving process.

Disruptive Technologies
The concept of disruptive technologies (DTs) was introduced at a plenary session, "Anticipating, Recognizing and Managing Disruptive Technologies," held by the MIT office of Corporate Relations on 9--10 May 2000. DTs are technologies that can significantly disrupt the conventional way of doing business, positively or negatively.1 Reservoir management, when viewed as a continual optimization process, has a vital need for DTs, because they can, when managed smartly, more than overcome challenges of increasingly complex field operations (e.g., maturing reservoirs or tight formations).
DTs can be classified into three major areas.Diagnostic.Information or knowledge management.Enhanced production or so-called "Q2 "technologies.
DTs span the spectrum from reservoir imaging and/or characterization tools, such as 3D and 4D seismic and seismic-while-drilling, to dynamic simulation models. Examples of information- or knowledge-management technologies include advanced communication systems (e.g., satellites), real-time visualization platforms, and Web-enabled data pools. Intelligent downhole completions and novel production or drilling technologies fall under the Q 2category.

Case Examples
The Shaybah field in Saudi Arabia, which went on stream in mid-1998, provides a powerful demonstration of DTs in two areas. Fig. 1 shows that in the diagnostic technology area, a comparison of the Shaybah full-field simulation-model performance statistics between 1996 and 2001 indicates a two-thirds reduction in overall evaluation turn-around time, in spite of an almost fivefold increase in model size. This trend is a result of the significant computational advances provided by massively parallel processing(MPP) simulation, using Saudi Aramco's simulator as reported by Dogru2 and Pavlas.3
The dramatic effect of horizontal well drilling on Shaybah development costs is an illustration of Q2 technologies (Fig. 2). The field was developed in the late 1990s, primarily by use of 1-km horizontal wells. Had it been developed with conventional wells, assuming 1980s technologies, drilling costs would have been six fold higher on a cost-per-barrel basis of initial oil production. Continuing optimization efforts toward deploying more advanced downhole configurations (including extended horizontals, multilateral wells, and intelligent completions) will further reduce unit production costs.
A similar example of Q2 technologies is provided by a comparison of two adjacent development areas (Area 1/ 1996 vs. Area 2/2001) of the Haradh field in South Ghawar (Fig. 3). On a cost-per-barrel comparison of initial oil production rate, Haradh 2 drilling costs are approximately 20%lower, in spite of a 2:1 reservoir quality disadvantage, as inferred by comparative production indices (PIs).
An example from the information-technology domain is provided by SaudiAramco's Web-based well-approval process that manages the chain of activities extending from selection to putting wells on production. The new process, oriented toward value addition across the entire chain, is run by a multidisciplinary team and uses an online information highway, which enables a seamless communication among the process stakeholders (reservoir, production, and drilling engineers; Earth scientists; and environmental and land-management specialists).
Perhaps the most important aspect of the online process is with respect to its role as a data bank and integrator for all drilling-related activities and, hence, as a learning platform for continual improvement. This is achieved through a control point and feedback process, which reports and grades performance metrics (actual vs. planned) in specific categories (e.g., costs, well productivity, and completion integrity). These metrics then are used for interfield and intrafield ranking (against time) of the drilling process. The online process is expected to generate annual savings in excess of U.S. $35million.

Learning Reservoirs
Reservoir management can be viewed as a continuous optimization exercise within the context of systems thinking, as proposed by Senge 4 for complex organizations. By the same token, reservoirs can be regarded as learning platforms for this continuous optimization process over the life cycle of a field, as shown in Fig. 4.