Core-log integration and application of machine learning technique for better reservoir characterisation of Eocene carbonates,Indian offshore

Rock types,pore structures and permeability are essential petrophysical outputs,and they contribute considerably to the highest degree of uncertainty in reservoir characterisation.These factors have a strong influence on exploration and field development decisions.Core analysis is the best approach for estimating permeability,assigning rock types and characterising pore networks.Wireline logs are the most often employed method for estimating the parameters at each data point of reservoirs since there are more un-cored wells than cored wells.Artificial intelligence,on the other hand,is gaining popularity in the geosciences due to the ever-increasing complexity and volume of available subsurface data.This is also obvious in the demand for faster and more accurate interpretations in order to identify reservoir characteristics in increasingly difficult and complicated petroliferous basins.Artificial Neural Networks and Self-Organizing Maps are examples of machine learning approaches that can be used in both supervised and unsupervised modes for modelling and prediction.Eocene carbonates of Mukta oilfield are the major pay rocks of strong geological heterogeneity in terms of their porosity and permeability relationship with pore structures.This paper outlines a novel method of rock fabric classification,pore structure characterization,flow unit classification and robust reservoir permeability modelling based on an integrated approach that incorporates core measurements,log data and machine learning techniques.The pore structure has been characterised by the combination of conventional core,capillary pressure and nuclear magnetic resonance data.Artificial neural network has added an adequate benefit in accurate permeability modelling by utilizing the concepts of rock classifications and hydraulic flow units.

Fluid substitution in NMR T_(2)distribution and resistivity independent saturation computation using synthetic capillary pressure data

Petrophysicists and reservoir engineers utilise the capillary pressure and saturation-height function for calculating the water saturation of any reservoir,at a given height above the free water level.The results have a big impact on reserve estimation.The majority of capillary pressure studies are carried out in labs with core data.Cores,on the other hand,are usually altered from their original state before being delivered to laboratories.Moreover,the accuracy of discrete sets of core data in describing entire reservoir parameters,is still up for debate.Prediction of the capillary pressure curve in reservoir condition is an important subject that is challenging to perform.The use of nuclear magnetic resonance(NMR)logs for oil and gas exploration has grown in popularity over the last few decades.However,most of the time the utilization of the data is limited for evaluating porosity-permeability,distributions and computation of irreducible water saturation.After the advent of fluid substitution methods,NMR T_(2)distributions may now be used to synthesize core equivalent capillary pressure curves.Using fluid substitution workflow,our study introduces a better approach for obtaining capillary pressure curves from the NMR T_(2)distribution.The available core data has been used to calculate calibration parameters for better saturation height modelling.The workflow introduces a novel approach in resistivity independent saturation computation.In the exploratory wells of our study area,computed water saturation derived from the saturation height function exhibits encouraging agreement with resistivity based water saturation from multi-mineral model.The NMR based saturation height modelling approach has been included in study area for the first time so far.

Understanding pore characteristics through core-based petrographic and petrophysical analysis in a heterogeneous carbonate reservoir:A case study from the Mumbai Offshore Basin,India

Carbonate rocks exhibit complex and heterogeneous pore structures;such heterogeneity is manifested by the occurrence of a wide variety of pore types with different sizes and geometries as a result of depositional and diagenetic processes.These complications substantially increase the uncertainty of predicted rock hydraulic parameters because samples with comparable porosities might have very different permeability values.In this study,small-scale characterisation of porosity and permeability in heterogeneous Eocene limestone samples from the Bassein Formation of the B-X structure of the MK Field in Mumbai Offshore Basin,India,was carried out,employing an integrated framework that in-corporates thin-section petrography,routine core analysis,mercury injection capillary pressure and nuclear magnetic resonance data.The pore characteristics of these carbonates range from poor to excellent.The studied samples exhibited large ranges of porosity,permeability and other associated petrophysical attributes.The pore types,as well as their orientations and connectivity,are the primary factors causing the heterogeneity.Because of the complexity of the pore networks,a simple lithofacies classification alone would have been insufficient to link porosity and permeability.The reservoir char-acteristics in the study area are strongly linked to the development and/or destruction of reservoir porosity-permeability during different phases of diagenesis.Twenty-four carbonate core samples from the limestone unit were studied and classified into microfacies and pore type classes,producing an accurate assessment of reservoir attributes.The comprehensive workflow incorporates the pore volume distributions and pore throat attributes for each rock type.Three carbonate microfacies were identified by petrographic analysis and their petrophysical characteristics,such as porosity,permeability,pore throat size,pore volume and fluid flow factors,were measured.The study demonstrates how macro-porosity,mesoporosity and microporosity are associated with various rock types and how they affect permeability and cementation exponents.The results of this study provide a comprehensive experi-mental framework for geological and geophysical interpretation that can be applied to identify potential reservoir facies and strengthen our understanding of heterogeneous carbonates.The framework can also be used to guide reservoir evaluation of similar heterogeneous formations in other areas.

Petrophysical insights into pore structure in complex carbonate reservoirs using NMR data

The study delves into pore structure attributes within the complex Eocene carbonate of an Indian offshore field,encompassing pore throat,radius and their characteristics.Nuclear Magnetic Resonance(NMR)experimental data reveals crucial insights into pore structures and fluid states.This study compares the NMR T_(2) distribution curve with capillary pressure data from the Mercury Injection Capillary Pressure(MICP)technique,deriving linear and nonlinear conversion coefficients to transform NMR T_(2) spectra into equivalent pore radius distribution.Pore radius-dependent porosity partitioning,linked to permeability and the distribution of irreducible water,is conducted utilizing NMR-derived data.Following the T_(2) cut-off analysis,a two-segment fractal analysis of NMR T_(2) distribution is also carried out.This analysis unveils associations between fractal dimensions and various petrophysical parameters,including permeability,porosity,T_(2)LM,irreducible water saturation and R5o.The NMR-derived pore radius distribution is mostly unimodal,occasionally slightly bimodal.Six different pore size classes(less than 0.05μm to more than 5μm)are analysed in relation to permeability,porosity and irreducible water.Small pores(<1μm)contribute more to irreducible water with low porosity and permeability.The fractal dimension of large pores correlates strongly with porosity,permeability,T_(2)LM,irreducible water and Rso suggesting significant impact on reservoir seepage capacity.In addition to porosity partitioning,the current study demonstrates effectiveness in modelling modified permeability and correlating it with in situ permeability when applied to field NMR log data from the study area.While numerous studies focus on sandstone,our study marks the pioneering attempt at a comprehensive analysis on complex carbonate reservoirs.