Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Abstract

Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

Keywords: Asmari reservoir; Electrofacies; Lithofacies; MRGC and ANN clustering; Sandstone reserve; Zoning.

Figure 1

Location of Mansouri oilfield in the Dezful embayment, SW of Iran (create and edit by Autodesk Map 6)^,,.

Figure 2

General stratigraphic column of Masouri oilfield with depicting study area.

Figure 3

Multi graph-based data clustering via multiscale community detection^,.

Figure 4

(a) Structure or topology of a typical forward neural network, (b) FFBPANN structure made by “Lianbo Hu” to predict pore pressure^,.

Figure 5

Reservoir zonation sequences of the Asmari Formation based on lithological alteration in the studied well.

Figure 6

Initial petrophysical assessment based on lithology zoning segmentation 1.

Figure 7

Initial petrophysical assessment based on lithology zoning segmentation 2.

Figure 8

Initial petrophysical assessment based on lithology zoning segmentation 3.

Figure 9

Initial petrophysical assessment based on lithology zoning segmentation 4.

Figure 10

Initial petrophysical assessment based on lithology zoning segmentation 5.

Figure 11

Frequency diagram of model input logs.

Figure 12

Cross-over diagram of model input logs relative to each other.

Figure 13

Facies produced by MRGC method.

Figure 14

Classification of facies in the well sequence and final evaluation results using MRGC method.

Figure 15

Classification of facies in the well sequence and final evaluation results using the ANN method.

Figure 16

Comparing zoning results of the Geolog, MRGC, and ANN clustering techniques in the Asmari Formation.

Figure 17

(a) Quartz arenite microfacies without cement, (b) quartz arenite microfacies with sulfate and dolomite cement, (c) sublitharenite petrofacies, (d) siltstone petrofacies of Well No. A, Mansouri oilfield.

Figure 18

Drilling sample thin sections of the Asmari Formation in Mansouri field includes (A) without cement sandstorms due to migrating hydrocarbons before cementing, (B) dissolution in sandstones, (C) dolomite cement in sandstones, (D) dolomite dissolution in sandstones.