Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached following Boolean logic. However, human thinking logic is more complex and include the ability to process uncertainty. In addition, in value of information assessment, it is often desirable to make decisions based on multiple economic criteria, which, independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims to enrich the value of information methodology by integrating fuzzy logic into the decisionmaking process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors' knowledge, this is the first use of a fuzzy inference system in the domain of value of information. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation.
The concept of value of information (VOI) has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields. The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values, which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability. However, subsurface reservoir data are not always crisp; it can also be fuzzy and may correspond to various reservoir models to different degrees. The classical approach to VOI may not, therefore, lead to the best decision with regard to the need to acquire new data. Fuzzy logic, introduced in the 1960s as an alternative to the classical logic, is able to manage the uncertainty associated with the fuzziness of the data. In this paper, both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data. A case study, which is consistent with the future development of an oil reservoir, is used to compare the application of both approaches to the estimation of VOI. The results of the VOI process show that when the fuzzy nature of the data is included in the assessment, the value of the data decreases. In this case study, the results of the assessment using crisp data and fuzzy data change the decision from "acquire" the additional data (in the former) to "do not acquire" the additional data (in the latter). In general, different decisions are reached, depending on whether the fuzzy nature of the data is considered during the evaluation. The implications of these results are significant in a domain such as the oil and gas industry (where investments are huge). This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI, prior to implementing the assessment to select and define the right approach.Keywords Value of information · Fuzzy logic · Uncertainty and risk management · Oil and gas industry
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AbstractThe Intisar 'D' reservoir is a carbonate reef of Paleocene origin with no appreciable flow barrier. This is an under saturated reservoir with an oil of 40 °API and solution gas oil ratio of 595 SCF/STB. The reservoir, having 452 ft of net pay, is at a datum of 9,000 ftSS with an initial OOIP of 1.76 billion stock tank barrels and initial reservoir pressure of 4,257 psia. A series of very successful reservoir management strategies have been applied in order to optimize and increase the production capacity and reserves. So far 69.2 % (1.2 MMSTB) of OOIP has been recovered mainly due to implementation of an EOR project.cumulative oil production was 1,218 MMSTB, of which 823 MMSTB were driven due to gas injection as secondary and tertiary recovery methods while 395 MMSTB were produced due to waterflooding. This is one of the biggest EOR processes worldwide. This paper discusses the strategy used to produce this reservoir and also to compute the produced oil recovery from the different drive mechanism employed.
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