Skip to Main Content
Skip Nav Destination
GEOREF RECORD

Evaporite facies recognition using unsupervised artificial neural network in the northern Arabia Plate

Si-Hai Zhang, Yin Xu and Mee Kee Teng
Evaporite facies recognition using unsupervised artificial neural network in the northern Arabia Plate
Petroleum Geoscience (February 2019) 26 (1): 70-80

Abstract

The carbonate-evaporite depositional combination of Late Jurassic age is the top member of four upward-shoaling carbonate-anhydrite cycles in Upper Kimmeridgian age. The weak depositional contrasts in carbonate ramp setting make the lateral seal configurations subtle and tough to recognize. Multiple attribute analysis based on Artificial Neural Network (ANN) can delineate the internal character of the reservoirs and seals in a consistent way. In order to recognize the sedimentary facies within this reservoir interval, multiple seismic attributes input to an unsupervised ANN. Unsupervised ANN is a powerful classification technique, which is implemented using a single layer perceptron network. The network is trained by comparing the neurons to the input vectors using competitive-learning techniques. Once a neuron migrates to the center of the class, the network stabilizes and training is finished. Without prior information, further sedimentary facies are recognized by integrating local geological knowledge. The depositional environments in the study area are well characterized by unsupervised ANN and are consistent with the drilled wells and the geological model. Lagoonal deposits, ramp crest shoal and proximal deposits are recognized within the study area. The sedimentary facies recognition helps define potential areas for favorable prospect definition and hence prospect ranking.


ISSN: 1354-0793
Serial Title: Petroleum Geoscience
Serial Volume: 26
Serial Issue: 1
Title: Evaporite facies recognition using unsupervised artificial neural network in the northern Arabia Plate
Affiliation: Saudi Aramco, Exploration and Petroleum Engineering Center, Advanced Research Center, Dhahran, Saudi Arabia
Pages: 70-80
Published: 20190201
Text Language: English
Publisher: Geological Society Publishing House for EAGE (European Association of Geoscientists & Engineers), London, United Kingdom
References: 47
Accession Number: 2019-014797
Categories: Economic geology, geology of energy sourcesStratigraphy
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. strat. col., 3 tables, geol. sketch maps
N28°00'00" - N28°00'00", E47°00'00" - E47°00'00"
Country of Publication: United Kingdom
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data from The Geological Society, London, London, United Kingdom
Update Code: 201910
Close Modal

or Create an Account

Close Modal
Close Modal