A Fuzzy Symbolic Representation for Intelligent Reservoir Well Logs Interpretation

Journal of Natural Gas Science and Engineering

Clark

et al. 2011

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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.

Section: Methodology and Workflowmentioning

confidence: 99%

“…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

Journal of Natural Gas Science and Engineering

Clark

et al. 2011

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“…Since we adopted 4 symbols to represent Vsh data, 4 trapezoidalshaped membership functions were designed, one for each of the 4 symbols. The details are given in [8].…”

Section: Transform Log Data Into Fuzzy Symbolsmentioning

confidence: 99%

Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments

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

Clark

et al. 2008

Self Cite

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.

“…Additionally, the numerical data are transformed into fuzzy symbols (Yu and Wilkinson, 2007). Fuzzy symbol representation has advantages over its numerical counter-part in that it is easier for computers to manipulate and to carry out the interpretation task.…”

Section: T Yu and D Wilkinson: A Co-evolutionary Fuzzy System For Rmentioning

confidence: 99%

A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation

Evolutionary Computation in Practice

2008

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Well log data are routinely used for stratigraphic interpretation of the earth's subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology.