Soft computing-based computational intelligent for reservoir characterization

Nikravesh, Masoud

doi:10.1016/s0957-4174(03)00119-2

Cited by 74 publications

(27 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Artificial neural networks (ANNs) are computer models that mimic the functions of the human nervous system through some parallel structures comprised of non-linear processing nodes which are connected by weights [57]. These weights establish a relationship between the input and output of each node in the ANNs [58]. These systems process the data and then learn the relationships between the given data in a parallel and distributed pattern.…”

Section: Condition Of the Seismic Data Volumementioning

confidence: 99%

See 1 more Smart Citation

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

2017

View full text Add to dashboard Cite

This paper presents an integrated approach of discrete fracture network modelling for a naturally fractured buried-hill carbonate reservoir in the Jingbei Oilfield by using a 3D seismic survey, conventional well logs, and core data. The ant tracking attribute, extracted from 3D seismic data, is used to detect the faults and large-scale fractures. Fracture density and dip angle are evaluated by observing drilling cores of seven wells. The fracture density distribution in spatiality was predicted in four steps; firstly, the ant tracking attribute was extracted as a geophysical log; then an artificial neural network model was built by relating the fracture density with logs, e.g., acoustic, gamma ray, compensated neutron, density, and ant tracking; then 3D distribution models of acoustic, gamma ray, compensated neutron and density were generated by using a Gaussian random function simulation; and, finally, the fracture density distribution in 3D was predicted by using the generated artificial neural network model. Then, different methods were used to build the discrete fracture network model for different types of fractures of which large-scale fractures were modelled deterministically and small-scale fractures were modelled stochastically. The results show that the workflow presented in this study is effective for building discrete fracture network models for naturally fractured reservoirs.

show abstract

Section: Condition Of the Seismic Data Volumementioning

confidence: 99%

“…Hence, ANN is robust in capturing the complex relationships among different parameters [31]. ANN was also used in seismic data processing and interpretation, geophysical logging interpretation, reservoir mapping, and engineering [58].…”

Section: Condition Of the Seismic Data Volumementioning

confidence: 99%

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

2017

View full text Add to dashboard Cite

show abstract

“…This is due in part to the challenge, in petro-physics, of designing and building sensors to measure complex formations in hostile environments [7] and that uncertainty in data may be due to fuzziness rather than chance [53]. Fuzzy logic has the ability to deal with human error and uncertainty in systems to a greater extent than any methods discussed above [58]. This paper presented a fuzzy model for well selection, which is done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT).…”

Section: Discussionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

View full text Add to dashboard Cite

Abstract:The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.

show abstract

“…Artificial intelligence techniques based on soft computing, which include fuzzy and neural networks, have been highly exploited for solving variety of complex problems in various fields, where the physics involved is not fully known. Recently, a good number of works based on soft computing were reported in hydrocarbon exploration, seismic data processing, subsurface modeling and geophysical well-logging [3,19,20,26,32]. The conventional analysis methods need to be complimented with a number of emerging methodologies and soft computing techniques such as expert systems, artificial intelligence, neural network, fuzzy logic, genetic algorithm, probabilistic reasoning, and parallel processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Geoacoustic inversion using adaptive neuro-fuzzy inference system

Yegireddi

Kumar

2008

Comput Geosci

View full text Add to dashboard Cite

The geoacoustic parameters form significant input for underwater acoustic propagation studies and geoacoustic modeling. Conventional inversion techniques commonly used as indirect approach for extraction of geoacoustic parameters from acoustic or seismic data are computationally intensive and timeconsuming. In the present study, we have tried to exploit the advantage of soft computing techniques like, reasoning ability of fuzzy logic and learning abilities of neural networks, in inversion studies. The network model based on the combined approach called adaptive neuro-fuzzy inference system (ANFIS), is found to be very promising in inversion of the acoustic data. The network model once built is capable of invert a few thousand data sets instantaneously, to a reasonably good accuracy. In the case of conventional approaches, repetition of the entire inversion process with each new data set is required. A limited number of sensor's data are sufficient for simulation of the network model and provides an advantage to use short hydrophone array data. Inversion results of a few hundred test data sets, representing different geoacoustic environments, show the prediction error is much less than 0.01 g/cc, 10 m/s, 10 m and 0.1 against first layer's density, compressional sound speed, thickness and attenuation respectively for a three-layer geoacoustic model. However, the error is relatively large for the second-and third-layer parameters, which need to be improved. The model is efficient, robust and inexpensive.

show abstract

Soft computing-based computational intelligent for reservoir characterization

Cited by 74 publications

References 18 publications

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

Discrete Fracture Network Modelling in a Naturally Fractured Carbonate Reservoir in the Jingbei Oilfield, China

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Geoacoustic inversion using adaptive neuro-fuzzy inference system

Contact Info

Product

Resources

About