Decision Analytics, Value of Information, and Piloting in Unconventional Reservoirs

Bickel, J. Eric

doi:10.2118/169841-ms

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Several papers, such as those by Walls (2005) and Vilela, Oluyemi and Petrovski (2017), have discussed the use of utility theory in the VoI assessment in the oil and gas industry. In unconventional reservoirs, Bickel (2014) applied decision analytics and value of information methodologies. The flexibility of the VoI methodology for application in real time is shown by Kullawan et al (2014) in a geosteering application during a drilling operation.…”

Section: The Value Of Information Conceptmentioning

confidence: 99%

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Vilela

Oluyemi

Petrovski

2020

IJADS

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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 by following Boolean logic. However, human thinking is based on a more complex logic that includes the ability to process uncertainty. In value of information assessment, it is often desirable to make decisions based on multiple economic criteria which, if 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 at enriching the value of information methodology by integrating fuzzy logic into the decision-making 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 specified knowledge domain. 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. Sensitivity analysis is undertaken for several membership functions used in the case study to assess the impact that shifting, narrowing and stretching the membership relationship has on the value of information. The results of the sensitivity study show that, depending on the shifting, the membership functions lead to different decisions; additional sensitivities to the type of membership functions are investigated, including the functions' parameters.

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Section: The Value Of Information Conceptmentioning

confidence: 99%

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Vilela

Oluyemi

Petrovski

2020

IJADS

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“…Where n is the number of samples taken, or the amount of information in terms of the equivalent number of wells (Bickel 2014), and is the correlation coefficient if the i.i.d. assumption does not hold true.…”

Section: Distributions Of Distributionsmentioning

confidence: 99%

“…In order to perform this properly, Bayesian methods must be employed. Fulford (2013) and Bickel (2014) have demonstrated Bayesian methods to calculate the parameters of the revised distribution, 1 and 1 , for the normal-normal case using log values. Here we show the lognormal-lognormal case, and a second, adjusted approach to better mesh with human expectation that reduction in uncertainty leads to convergence around the mean.…”

Section: Revising Estimatesmentioning

confidence: 99%

Unconventional Risk and Uncertainty: Show Me What Success Looks Like

Fulford

2016

Day 2 Wed, May 18, 2016

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The translation of risk from conventional to unconventional resources moves the fulcrum from risk of finding, towards risk of commerciality. We are often certain a hydrocarbon producing system exists; but, uncertain in what manner (production profile) it will produce. This reality is incompatible with traditional methods of risking in which recoverable volumes are estimated from volumetric calculation, and risked volumes may be calculated as the product of unrisked volumes and some risk factor. Production profile is then determined after-the-fact. Unconventional resources are typically evaluated from the basis of a type well or production analog first, and then the recoverable volume is emergent from this production profile.Like the chicken-and-egg problem, uncertainty of recoverable volumes must be estimated in association with one or more production profiles. The threshold of commerciality can be calculated, and the chance of commercial success determined by the intersection of the threshold volume on the distribution of recoverable volume. This distribution is truncated and the outcomes above the threshold are rescaled to represent success outcomes. . . but, failure outcomes are disregarded. This paper presents a simple workflow using Frequentist and Bayesian approaches, respectively, for proper risking of uncertain recoverable volumes for an unconventional resource, taking into account the chance of false positives from appraisal well information. A subjective risk tolerance can be included to respect how aggressive or conservative a company may be in pursuit of a project. A method of re-casting production profiles is demonstrated as an improvement over the common method of scaling, or factoring, the initial production rate. The scaling approach may reach unrealistic values of initial production in an attempt to achieve the proper recoverable volumes.Productive yet uneconomic outcomes should be included in any thorough project evaluation. Failure to do so may overestimate the value of a given project. However, the chance of such outcomes occurring is often seen as a difficult-to-determine value. Quantifying the uncertainty in type well volumes allows evaluation of the imperfectness of information of early well results. Enhanced knowledge of the project risk, the chance of success, a view of the success case, and an understanding of false positive outcomes, can lead to higher quality decisions. of these plays having little-to-no risk, to the point that Љwell manufacturingЉ is a non-controversial term used to refer to development of these assets. If the rock is all the same, then an understanding of the reservoir should be a simple matter achieved by drilling a small number of wells. Exploration & Production companies (E&Ps) often tout a Љtype wellЉ as an indication of the viability of their assets. However, a type well -by definition -represents the mean of a population of wells. Not all individual wells must be commercial in order for the mean well to be commercial. Indeed, the well population is typically lo...

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“…Kullawan et al (2014) discussed an important application of VOI in a geosteering operation in which a large number of real-time operations are executed day to day, demonstrating the flexibility of the VOI methodology in adapting to challenging circumstances. Bickel (2014) applied decision analytics and value of information for unconventional reservoirs. Ferreira (2015) discussed a probabilistic approach to quantify the value of information associated with a 4D seismic acquisition project.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic applied to value of information assessment in oil and gas projects

2019

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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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Decision Analytics, Value of Information, and Piloting in Unconventional Reservoirs

Cited by 10 publications

References 3 publications

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry

Unconventional Risk and Uncertainty: Show Me What Success Looks Like

Fuzzy logic applied to value of information assessment in oil and gas projects

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