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Volume 26, Issue 1
  • ISSN: 1354-0793
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Abstract

The carbonate–evaporite depositional combination of Late Jurassic age, incorporates the most prolific oil-producing intervals in the world and forms many giant fields. The succession is the top member of four upwards-shoaling carbonate–anhydrite cycles of Upper Kimmeridgian age, and is overlain by the impermeable anhydrite in northern Arabia. The weak depositional contrasts in carbonate ramp settings make the lateral seal configurations subtle and tough to recognize. Multiple attribute analyses based on an artificial neural network (ANN) can delineate the internal character of the reservoir and seal in a consistent way.

In order to recognize the sedimentary facies and characterize stratigraphic traps within this reservoir interval, multiple seismic attributes were input to an unsupervised ANN. Unsupervised ANN offers a powerful means of classification, implemented here 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 centre of a class, the network stabilizes, training is finished and the neuron is assigned to a representative class. Without prior information, the unlabelled class is calibrated and analysed by lithofacies generated from log and core data. Further sedimentary facies are recognized by integrating local geological knowledge.

The depositional environments in the study area are well characterized by the unsupervised ANN, and the recognized sedimentary facies are consistent with the drilled wells and the resulting geological model. Lagoonal deposits of the inner-ramp, ramp-crest shoal and proximal deposits of the middle ramp are recognized within the study area. The widespread ramp crest with peloid and oolitic grainstones provides good reservoirs, whereas the lagoonal deposits distributed between the shoals have a greater abundance of tight limestone with low porosity and permeability, thereby forming a good lateral seal. The selected study area, covering the Rimthan Arch is considered a favorable area for the presence of stratigraphic traps. The sedimentary facies recognition helps to define potential areas for favourable prospect definition and hence prospect ranking.

© 2019 The Author(s). Published by The Geological Society of London for GSL and EAGE. All rights reserved
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2019-02-21
2024-04-20

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Evaporate facies recognition using an unsupervised artificial neural network in the northern Arabia Plate
Petroleum Geoscience 26, 70 (2020); https://doi.org/10.1144/petgeo2018-063
/content/journals/10.1144/petgeo2018-063
/content/journals/10.1144/petgeo2018-063
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