Carbonate Rock Type Matrix RocMat, The Ultimate Rock Properties Catalogue

Abstract

Rock typing is one of the most important steps in reservoir modeling, and it’s the main task in reservoir characterization. In carbonate, the rock typing work that’s been performed during the last two decades had a little progress in term of providing reliable estimation of reservoir behavior. However, the development of Conjunction Rock Properties Convergence, CROPC, a carbonate rock typing concept that provided an important and easy solution to the carbonate rock typing gaps, has a major breakthrough, even though, CROPC methodology was developed to capture the single pore network through the conjunction of Lithology, permeability, capillary pressure and water saturation. Therefore, the need to identify more complex carbonate pore network had led to the initiation of developing the Carbonate Rock Type Matrix RocMat, which will be detailed in this paper, as part of a Master of Science research project. In this novel concept the carbonate rocks were classified into homogeneous, single pore network, and heterogeneous rocks, dual and triple pore network with the utilization of the effective petrophysical properties of permeability, capillary pressure, saturation, porosity and height above free water level, all were classified in a conjunction matrix that honors these properties and at the same time enables generating sub groups as down scaling and estimation for unseen groups with infinite rock complexity capturing, at the same time it enables the ease to lump the groups and generates upscale-groups that make it easier for utilization by the geologist and reservoir engineers to achieve the objective of better reservoir performance prediction, the work was performed and then tested in two carbonate offshore fields data. This RocMat was structured to be the ultimate catalog for carbonate rock types.

Automated Artificial Intelligence Algorithm Adoption AAIAA

“…The geoscientist starts by analyzing the objectives. This research's objective targets supervised semantic segmentation of the rock uCT images with utmost accuracy and efficiency [38][39][40][41][42][43][44][45][46][47][48][49][50][51]. Then the geoscientist identifies the input data type that provides the bases for understanding the objective set and then chooses the suitable algorithms, based on accurate sets, that deliver the highest accuracy.…”

Deducing Optimal Classification Algorithm for Heterogeneous Fabric

“…The choice of the machine learning algorithm is critical, because heterogeneous embeds complex interlinks within its features. Our heterogeneous system is Cretaceous carbonate rock fabric [4][5][6][7][8][9][10][11][12][13] where we target digitally classifying [14] it based on its physical and chemical properties using machine learning. The literature suggested that a decision tree-based algorithm performs better [15] than an artificial neural network-based algorithm, like Convolutional Neural Network (CNN), that works better with homogeneous systems [16,17].…”

The Discovery of Calcite Intrinsic Wettability by the First-Ever Optical Illumination Inside Dark Fluid using IRIDW Apparatus

“…Each 3D geological grid block [1][2][3] requires a rock type [4][5][6][7][8] with physical and chemical properties [9,10] like lithology [11,12], porosity, permeability, capillary pressure [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], fluid saturation [31][32][33][34][35][36][37][38][39], relative permeability [40], and wettability [41,42]. Wettability is one of the main controlling attributes of fluid flow in porous media.…”