Maximizing the Effectiveness of Integrated Reservoir Studies: Practical Approaches to Improving the Process and Results
Authors: Worthington Paul F. 
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
This paper focuses on pragmatic aspects of integrated reservoir studies and how traditional practices might be modified to add greater value without increasing overall costs. My starting point is Herbert Nunn&#x0027;s account of the earthquake that occurred in Santa Barbara, California, on 29 June 1925, the most destructive event in the history of that city.
&#x0022; About 3:30 o&#x0027;clock in the morning I was awakened by a strong odor of crude oil. . . . Our pressure gauge records showed that about 3:27 o&#x0027;clock a.m., there were slight tremors repeated at intervals up to 6:42 when the heaviest shock was recorded. It is also interesting to note that crude oil came up through the beach sands within 200 yards of my home, and this phenomenon was repeated along the beach at several points south.&#x0022; 1
This description shows that the subsurface behaves as a coupled system. If one part of that system is changed, in this case the stress regime, another part will also be altered, in this case the permeability distribution. Therefore, it seems reasonable that the process of interpretation of subsurface properties should also be treated as a coupled system, whereby changing one element of the interpretation will have ramifications for another component. This rationale determines the ethos of this paper.
The real benefits of integration in reservoir geoscience and engineering lie in the ability to optimize the coupling between the static and dynamic components of an integrated reservoir study.2Fig. 1 maps the process of reservoir evaluation. Data acquisition and analysis are represented through the traditional subdivisions of reservoir geoscience and engineering but practiced systemically in an iterative manner. These data form the basis for the static reservoir model that is used to evaluate the volume of hydrocarbons present in the reservoir. Therefore, the static model quantifies the hydrocarbon resources. The static model in turn forms the basis for the dynamic reservoir model that is then used to estimate hydrocarbon reserves (i.e., the volume of hydrocarbons that is commercially recoverable). The synthesis of the static model as a basis for the dynamic model has been termed reservoir characterization. As development progresses and more production data become available, both the static and dynamic models are refined conjunctively, so that ambiguities in the geoscience interpretation may be resolved through considerations of reservoir dynamics, and vice versa (Fig. 2).
A key element of this coupled process is the evolving relationship between measurements and interpretation. The digital revolution has led to a proliferation of measurements delivered by modern field tools and sensors. Recent computer applications have, therefore, focused more on the preinterpretation stage of data management. There has been less progress in developing new methods of interpretation. As a consequence, the gap between our ability to measure and our ability to interpret the measured data is as wide as ever.

Quality Assurance
Quality assurance is the benefit that accrues from the quality control of data, their measurement, and interpretation. This aspect of an integrated field study is especially important because the modern approach to reservoir studies is very much centered on the data that underpin the reservoir models. This is in contrast to studies of 15 years ago, which had to be conditioned to the unique formatting requirements of diverse software packages, with much repetitive data organization. The emergence of integrated applications systems has resulted in a diminishing need for discrete data sets as input to different applications. Indeed, a single relational database can suffice for most tasks.
Although data acquisition and management account for the bulk of field-study costs, often data handling is seen as uncharismatic and, therefore, it tends to receive a low priority. And yet, to be effective, data validation should begin before any data acquisition through considered planning of fit-for-purpose measurements. The most cost-effective and useful databases are those driven by a team of data mentors who have provided continuity and consistency of data acquisition, validation, and archiving over the life of the database.
The interpretation of quality-assured data itself requires validation. Two pragmatic aspects are discussed. The first is groundtruthing (i.e., tying back interpreted reservoir properties to definitive measurements). The second is closing the loop (i.e., making conceptually simple crosschecks on interpreted reservoir properties). Both aspects bring in issues of scale of measurement.

Groundtruthing.
Groundtruthing applies to all the key reservoir properties such as porosity, permeability, and fluid saturations. The tying back of interpreted porosity and permeability to core data is widely practiced, but the groundtruthing of fluid saturations is not. Yet the technology for obtaining water saturation from cores has been around for more than 20 years.3 Advances in low-invasion coring bits and the use of low-invasion gels have strengthened the technology base. Any fluid invasion of the core that may occur can be quantified by tagging the drilling mud with an appropriate tracer. It is not proposed that these data should be measured widely, but rather that they should be acquired at key wells and especially in reservoirs that show departures from textbook character. Once an interpretation procedure has been validated at a key well, it can be exported to other wells, where it should guide data acquisition. Dawe and Murdock4 presented an excellent example of groundtruthing porosity, f, water saturation, Sw, and hydrocarbon-filled porosity, f(1-Sw).