... Abstract Rock facies are typically identified either by core analysis to provide visually interpreted lithofacies, or determined indirectly based on suites of recorded well-log data, thereby generating electrofacies interpretations. Since the lithofacies cannot be obtained for all reservoir...

...Classification Methodology In this research, the proposed procedure is to model the observed lithofacies given the well-logs using supervised machine learning techniques of the Mishrif-cored well in a particular supergiant oil field in southern Iraq to estimate lithofacies distribution...

... classification (KNN) and Multi-Resolution Graph based Clustering (MRGC), and then the corresponding relationship between electrofacies and lithofacies is established by combining core analysis and cutting data. Since this method is based on continuous well logs, it has strong extensibility and is easy to learn...

... machine learning centroid different facies upstream oil & gas well log carbonate reservoir facies core description reservoir characterization porosity lithofacies artificial intelligence core data reservoir facies facies classification software Al-Mohammad , R. A. ( 2012...

... Abstract Modeling accurate lithofacies and petrophysical properties is a crucial step in the reservoir characterization as it affects reservoir heterogeneity, fluid flow modeling, and history matching, especially in complex geological structures. In this paper, the multiple-point facies...

... for accurate Lithofacies classification prediction to be incorporated with well log attributes into core permeability. Probabilistic Neural Networks and Generalized Boosted Regression Models were adopted for Efficient Lithofacies Classifications and Formation Permeability Estimation, respectively...

... as they incorporate nonlinear modelling. Conversely, the lowest TPC was by LDA as linear classifier and NBC as Bayesian one. However, the in-between approaches are MLR and TBM. machine learning bayesian inference lithofacies lithofacies classification prediction posterior distribution implementation...

... geological model that contains three-dimensional information about a geological heterogeneity and structure. The facies pattern was then considered for 3D Lithofacies modelling conditioning to upscaled discrete facies distribution for 20 wells in the reservoir. Based on core data analysis, three lithofacies...

... sandstone formation in South Rumaila Oil Field, located in Iraq. The explanatory variables are depth, neutron porosity, water saturation, shale volume. The multinomial response factor is the vertical discrete Lithofacies sequence that only encompasses of sand, shaly sand, and shale. The Lithofacies were...

... information equatorial guinea amplitude debris flow turbidity current reservoir characterization interpretation production month evaluation upstream oil & gas reservoir evaluation famakinwa slump discovery lithofacies zafiro reservoir zafiro field technology conference...

... results. The increased spatial resolution of the 3D data over our 2D data grid allowed us to image the detailed geomorphic shape of a deep water Pleistocene slope fan channel/levee system with multiple flattened horizon slices. Lithofacies maps of the lowstand slope fan and prograding wedge were made...

... management. This work was aimed at improving secondary recovery through: 1) detailed reservoir description, particularly the vertical and a real distribution of reservoir quality lithofacies, and 2) the influence of small-scale structural features on reservoir continuity. GEOLOGICSETTING Granite Point...

... out a detailed study for which they collected some 7000 sediment samples to analyze and describe the sediment~ and their distri- bution over Heald and Sabine Banks. They described three major components sand, carbonate, and silt and clay forming eleven major lithofacies, 7 <ELECTRICAL LOGGING SYSTEMS...