A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation

Yu, Tina; Wilkinson, Dave

doi:10.1007/978-3-540-75771-9_9

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…We constructed the decision model using a co-evolutionary system [9]. In order to construct the model that contains similar knowledge as that used by the stratigrapher to classify 5 different depositional types, Step 5 calculates the attribute values listed in Table I for every depositional unit identified by the stratigrapher.…”

Section: Methodology and Workflowmentioning

confidence: 99%

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Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments

Wilkinson

Clark

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Predicting oil recovery efficiency of deepwater reservoirs is a challenging task. One approach to characterize and predict the producibility of a reservoir is by analyzing its depositional information. In a deposition-based stratigraphic interpretation framework, one critical step is the identification and labeling of the stratigraphic components in the reservoir according to their depositional information. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, this research developed a methodology to automate this process using various computational intelligent techniques. Using a well log data set, we demonstrated that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.

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Section: Methodology and Workflowmentioning

confidence: 99%

“…An alternative approach is to train the 5 classifiers simultaneously. The co-evolutionary system described in [9] was developed based on this concept.…”

Section: Co-evolution Of Classifiersmentioning

confidence: 99%

Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments

Wilkinson

Clark

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…We chose trapezoidal-shaped MF based on our previous experiences [12] and [13], where we found trapezoidal-shaped MF is suitable for the transformation of various types of well log, such as porosity, density and sonic log.…”

Section: Transform Vsh Data To Fuzzy Symbolsmentioning

confidence: 99%

Computational intelligence for deepwater reservoir depositional environments interpretation

Wilkinson

Clark

et al. 2011

Journal of Natural Gas Science and Engineering

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Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterize a deepwater reservoir and to predict its producibility is by analyzing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deepwater reservoir characterization. In this framework, one critical task is the identification and labeling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.

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“…Data mining is a research area concerned with extracting non-trivial information contained in a database, and has also been applied to time series forecasting. Among data mining techniques, mainly neural networks [9,19,37,44] and fuzzy rule based systems [3,23,25,29,48] have been applied to this kind of problem.…”

Section: Introductionmentioning

confidence: 99%

CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market

Pérez-Godoy

Pérez

Rivera

et al. 2010

HIS

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This paper presents the adaptation of CO 2 RBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In order to calculate the application probability of the evolutive operators over a certain Radial Basis Function, a Fuzzy Rule Based System has been used. The olive oil time series have been analyzed using CO 2 RBFN. The results obtained have been compared with Auto-Regressive Integrated Moving Average (ARIMA) models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm, and a radial basis function network trained using an LMS algorithm. The experimentation shows the high efficiency achieved by these methods, especially the data mining methods, which have slightly outperformed the ARIMA methodology.

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A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation

Cited by 5 publications

References 11 publications

Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments

Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments

Computational intelligence for deepwater reservoir depositional environments interpretation

CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market

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