Reservoir architecture and porosity distribution, Pegasus Field, West Texas — An integrated sequence stratigraphic‐seismic attribute study using neural networks

Schuelke, J.S.; Quirein, John; Sag, J.F.; Altany, D.A.; Hunt, P.E.

doi:10.1190/1.1886093

Cited by 22 publications

(3 citation statements)

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“…Several different neural network-based methods to handle and better utilize seismic attributes have been introduced, e.g. Schuelke et al, 1997 andde Groot, 2004. Neural network-based methods, has many advantages.…”

Section: Why Neural Network and Meta Attribute?mentioning

confidence: 99%

A neural networks based seismic object detection technique

Aminzadeh

deGroot²

2005

SEG Technical Program Expanded Abstracts 2005

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Seismic technology has historically focused on resolving the structures. To a lesser degree, presence or absence of reservoir has also been a focus. This paper shows the use of seismic data to detect several other "seismic objects". The technique to be referred to as "meta attribute", uses the combination of "artificial intelligence" of neural networks and the "natural intelligence" of an interpreter. Examples of many geologic features and reservoir properties detected using this technique will be provided. They include hydrocarbon probability, lithofacies, chimney, faults and salt.

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Section: Why Neural Network and Meta Attribute?mentioning

confidence: 99%

A neural networks based seismic object detection technique

Aminzadeh

deGroot²

2005

SEG Technical Program Expanded Abstracts 2005

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“…Literature study reveals that machine learning algorithms exhibit superior performance in terms of high accuracy and low error value compared to conventional statistical methods (Nikravesh and Aminzadeh, 2001). Some of the important articles with special emphasis on RC using 3D seismic data include Boadu, (1997) ;Nikravesh, (1998a,b); Nikravesh et al, (1998); Chawathe et al, (1997); Yoshioka et al, (1996); Schuelke et al, (1997); Monson and Pita, (1997); Aminzadeh and Chatterjee, (1985); Finol and Jing (2002); Hou et al, (2008); Kadkhodaie-Ilkhchi et al, (2009); Al-Dousari and Garrouch (2013); Na 'imi, et al, (2014); Iturrarán-Viveros and Parra (2014).…”

Section: Introductionmentioning

confidence: 99%

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

Verma

Chaki

Routray

et al. 2014

Journal of Applied Geophysics

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In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. In this paper, we opt for ANN and three different categories of Adaptive Neuro-Fuzzy Inference System (ANFIS) based on clustering of the available datasets. A comparative analysis of these three different NF models (i.e., Sugeno-type fuzzy inference systems using a grid partition on the data (Model 1), using subtractive clustering (Model 2), and using Fuzzy c-means (FCM) clustering (Model 3)) and ANN suggests that Model 3 has outperformed its counterparts in terms of performance evaluators on the present dataset. Performance of the selected algorithms is evaluated in terms of correlation coefficients (CC), root mean square error (RMSE), absolute error mean (AEM) and scatter index (SI) between target and predicted sand fraction values. The achieved estimation accuracy may diverge minutely depending on geological characteristics of a particular study area. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.

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“…Their application has been documented in numerous published papers dealing with estimating lithology (ROGERS et al, 1995;BENAOUDA et al, 1999;SAGGAF and NEBRIJA, 2000), porosity and permeability (ROGERS et al, 1995;HUANG et al, 1996;SCHUELKE et al, 1997;WONG et al, 2000;ALI and CHAWATHE, 2000;LIGTENBERG and WANSINK, 2003), fractured reservoir characterization (ZELLOU and OUENES, 2003), and other reservoir rock properties (BALDWIN et al, 1990;ACCARAIN and DESBRANDES, 1993;SAGGAF and NEBRIJA, 2003).…”

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confidence: 99%

Using Artificial Neural Networks to Predict the Presence of Overpressured Zones in the Anadarko Basin, Oklahoma

Crânganu

2007

Pure appl. geophys.

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Many sedimentary basins throughout the world exhibit areas with abnormal pore-fluid pressures (higher or lower than normal or hydrostatic pressure). Predicting pore pressure and other parameters (depth, extension, magnitude, etc.) in such areas are challenging tasks. The compressional acoustic (sonic) log (DT) is often used as a predictor because it responds to changes in porosity or compaction produced by abnormal pore-fluid pressures. Unfortunately, the sonic log is not commonly recorded in most oil and/or gas wells. We propose using an artificial neural network to synthesize sonic logs by identifying the mathematical dependency between DT and the commonly available logs, such as normalized gamma ray (GR) and deep resistivity logs (REID). The artificial neural network process can be divided into three steps: (1) Supervised training of the neural network; (2) confirmation and validation of the model by blind-testing the results in wells that contain both the predictor (GR, REID) and the target values (DT) used in the supervised training; and 3) applying the predictive model to all wells containing the required predictor data and verifying the accuracy of the synthetic DT data by comparing the backpredicted synthetic predictor curves (GRNN, REIDNN) to the recorded predictor curves used in training (GR, REID). Artificial neural networks offer significant advantages over traditional deterministic methods. They do not require a precise mathematical model equation that describes the dependency between the predictor values and the target values and, unlike linear regression techniques, neural network methods do not overpredict mean values and thereby preserve original data variability. One of their most important advantages is that their predictions can be validated and confirmed through back-prediction of the input data. This procedure was applied to predict the presence of overpressured zones in the Anadarko Basin, Oklahoma. The results are promising and encouraging.

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Reservoir architecture and porosity distribution, Pegasus Field, West Texas — An integrated sequence stratigraphic‐seismic attribute study using neural networks

Cited by 22 publications

References 0 publications

A neural networks based seismic object detection technique

A neural networks based seismic object detection technique

Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

Using Artificial Neural Networks to Predict the Presence of Overpressured Zones in the Anadarko Basin, Oklahoma

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