Permeability prediction using hydraulic flow units and electrofacies analysis

It is essential to characterize fluid flow in porous media to have a better understanding of petrophysical properties.Many approaches were developed to determine reservoir permeability among which the integrated analysis of hydraulic flow unit(HFU)and electrofacies(EF)is considered to be useful one.However,the application of HFU and EF analysis has not been totally understood with a limited data to develop correlation for less distance offset wells.In this study,an attempt was made to show the application of integrating HFU and EF for reliable estimation of permeability using core and wireline log data in one of the gas fields in Pakistan.The results obtained indicate that the integrated approach proposed in this study can be used,especially in less distance offset wells when a limited number of data are available for petrophysical characterization.

Permeability Prediction Using Hydraulic Flow Units: Baltim North Gas Field, Nile Delta, Egypt

168 core samples data of two production wells in the Baltim North field were used to identify the complex discrepancies in reservoir pore geometry which governing the Abu Madi reservoir fluid flow properties. Permeability prediction from well logs is significant goal when the core data is rarely available in most cases because of its expensive cost. The hydraulic flow unit approach was used to classify reservoir rocks according to its pore aperture size in the cored wells. The predicted permeability was calculated from core porosity and core permeability relationship for each flow unit. The difference between Neutron porosity and Density porosity was recognized to distinguish different hydraulic flow units. The higher difference indicates higher quality flow unit and vice versa. For model’s verification the predicted permeability was plotted against the laboratory measured permeability in all studied wells and shows highly matching.

Pore size determination using normalized J-function for different hydraulic flow units

Pore size determination of hydrocarbon reservoirs is one of the main challenging areas in reservoir studies.Precise estimation of this parameter leads to enhance the reservoir simulation,process evaluation,and further forecasting of reservoir behavior.Hence,it is of great importance to estimate the pore size of reservoir rocks with an appropriate accuracy.In the present study,a modified J-function was developed and applied to determine the pore radius in one of the hydrocarbon reservoir rocks located in the Middle East.The capillary pressure data vs.water saturation(PceSw)as well as routine reservoir core analysis include porosity(4)and permeability(k)were used to develop the J-function.First,the normalized porosity(4z),the rock quality index(RQI),and the flow zone indicator(FZI)concepts were used to categorize all data into discrete hydraulic flow units(HFU)containing unique pore geometry and bedding characteristics.Thereafter,the modified J-function was used to normalize all capillary pressure curves corresponding to each of predetermined HFU.The results showed that the reservoir rock was classified into five separate rock types with the definite HFU and reservoir pore geometry.Eventually,the pore radius for each of these HFUs was determined using a developed equation obtained by normalized J-function corresponding to each HFU.The proposed equation is a function of reservoir rock characteristics including 4z,FZI,lithology index(J*),and pore size distribution index(3).This methodology used,the reservoir under study was classified into five discrete HFU with unique equations for permeability,normalized J-function and pore size.The proposed technique is able to apply on any reservoir to determine the pore size of the reservoir rock,specially the one with high range of heterogeneity in the reservoir rock properties.

Implementation of a Petrographical and Petrophysical Workflow Protocol for Studying the Impact of Heterogeneity on the Rock Typing and Reservoir Quality of Reefal Limestone:A Case Study on the Nullipore Carbonates in the Gulf of Suez

This study focuses on the heterogeneity of the middle Miocene syn-rift Belayim nullipore(reefal)marine sequences in the Gulf of Suez and its impacts on reservoir quality.The sequences consist of coralline algal reef limestones with a highly complex dual-porosity system of primary and secondary porosities of widely varying percentages.To achieve a precise mathematical modeling of these reservoir sequences,a workflow protocol was applied to separate these sequences into a number of hydraulic flow units(HFUs)and reservoir rock types(RRTs).This has been achieved by conducting a conventional core analysis on the nullipore marine sequence.To illustrate the heterogeneity of the nullipore reservoir,the Dykstra-Parsons coefficient(V)has been estimated(V=0.91),indicating an extremely heterogeneous reservoir.A slight to high anisotropy(λ_(k))has been assigned for the studied nullipore sequences.A stratigraphic modified Lorenz plot(SMLP)was applied to define the optimum number of HFUs and barriers/baffles in each of the studied wells.Integrating the permeability-porosity,reservoir quality index-normalized porosity index(RQI-NPI)and the RQI-flow zone indicator(RQIFZI)plots,the discrete rock types(DRT)and the R35 techniques enable the discrimination of the reservoir sequences into 4 RRTs/HFUs.The RRT4 packstone samples are characterized by the best reservoir properties(moderate permeability anisotropy,with a good-to-fair reservoir quality index),whereas the RRT1 mudstone samples have the lowest flow and storage capacities,as well as the tightest reservoir quality.

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.

A new integrated approach to resolve the saturation profile using high-resolution facies in heterogenous reservoirs

The saturation calculation in complex reservoirs remains a major challenge to the oil and gas industry.In simple formations,a tendency towards simple saturation models such as Archie or Simandoux for clean and shaly reservoirs respectively is always preferable.These models were found to be working effectively in homogeneous formations within which the porosity and permeability are linked in the light of a simple facies scheme.Where the rocks show some degrees of heterogeneity,the well-logs are usually affected by different factors.This adversely results in a compromised or averaged log profiles that may affect the saturation calculations.Four wells drilled across a shaly sand of high heterogeneity have been studied in the Perth Basin,Western Australia.The aim is to resolve the hydrocarbon saturation and explain the high productivity results,despite the high water saturation,obtained through a conducted formation well test across the interested reservoir zones.A new integration technique between a suite of conventional and advanced logging tools together with the capillary pressure measurements has been carried out to generate a high-resolution reservoir saturation profile,that is lithofacies dependent.Three different independent methods were used in the studied wells to calculate the saturation and to reduce the uncertainty of the final estimated profiles.The methods are the resistivity-based saturation,the NMR-based irreducible saturation,and a new application through saturation height modeling.Furthermore,through the workflow,an effective calibration for the magnetic resonance T2 cutoff has been applied that is supported by the excellent reservoir production behavior from such complex reservoir.The methodology will help resolve the saturation calculation as one of the most challenging reservoir parameters,particularly where the resistivity logs are affected in complicated shaly sand environments.The effectiveness of the workflow shines the possibility to predict high resolution facies and saturation profiles in the lack of resistivity logs.A further possibility can complete the analysis on real time basis,which can certainly provide facies and saturation profiles extended to the uncored wells.Application of this methodology in the uncored wells has shown very encouraging results in various well trajectories,either vertical,deviated or horizontal long boreholes.