Permeability In Un-cored Wells Research Articles

Permeability Prediction from Well Log Data Using Artificial Neural Networks: A Case Study of the Hawaz Formation in D-Field, Libya

Permeability is an important parameter in the characterization of any hydrocarbon reservoir. Yet, despite its vital importance, it is one of the most difficult and controversial petrophysical properties to calculate accurately due to nonlinearity and uncertainty in the dataset. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in a field, while the majority of wells are logged. The aim of this paper is to design an artificial neural network (ANN) model to predict formation permeability using a dataset from the Hawaz Formation in the D-field NC-186 concession, East Murzuq Basin, Libya. In this study, a back-propagation neural network (BP-ANN) model was built using the Python programming language. A total of 950 core horizontal permeability measurements and their corresponding well log data were collected from four wells to build the model. Traditional statistical analysis of the porosity/permeability relationship in cored well data yielded no reliable correlations for predicting permeability in uncored wells with R2 less than 15%. A supervised BP-ANN model was trained successfully and was successfully able to predict the permeability of the formations. Despite the presence of high reservoir heterogeneity, the permeability profile predicted by the ANN model using well log data agrees well with core permeability, which clarifies the applicability of this method.

Selection of Optimum Permeability Estimation Approach in a Heterogeneous Carbonate Reservoir

Determination of permeability is an essential component of reservoir characterization process which is among the key input parameters into a flow simulation models. Permeability modeling in carbonate reservoirs is still a challenge in the world. Permeability is directly determined in the laboratory from core analysis. Alternatively, it can be determined by analyzing well test or well logs. Due to high cost associated with coring and some technical problems, few wells in any given field are cored whereas most wells have wire-line logs.
 In this study detailed core analysis data including core porosity and core permeability supplemented by well logs and well test data to predict a continuous log derived permeability in un-cored wells in a heterogeneous carbonate reservoir in south west of Iran. The Mishrif reservoir in the studied field consists of limestone and interbedded shale. The field has 3 wells that have recovered cores. Permeability prediction was applied by several methods including: fuzzy logic, neural networks, clustering, empirical methods and regression analysis. These different methods were used to determine the optimal approach for utilizing in the field under study. To test the permeability prediction, the techniques were calibrated in 2 cored wells and blind tested in remaining cored well to see how well estimated permeability fitted the actual core permeability. Among all permeability modeling methods applied in the field, it turned out that electrofacies method and after that artificial neural network have the highest degree of association. Fuzzy logic and regression techniques are average in modeling permeability and empirical methods are not capable for predicting permeability in studied heterogeneous carbonate reservoir. The core analysis from 3 cored-wells was applied to determine permeability in 51 un-cored wells.

Porosity and permeability prediction using pore geometry structure method on tight carbonate reservoir

Porosity and Permeability is an essential petrophysical parameter of hydrocarbon reservoirs for oil and gas production. It’s can be immediately measured using cores taken from the reservoir in the laboratory and deal with the high cost. Many empirical, statistical, and intelligent approaches were suggested to predict permeability in un-cored wells based on wireline logs. The main objective of this study is to predict the porosity and permeability values in a tight carbonate reservoir. In this study, the calculation of permeability was done using the Schlumberger, East, Morris Biggs Oil, Morris Biggs Gas, and PGS (Pore Geometry Structure) methods based on core, logs, and CT-Scans data. The determination of porosity values from CT-Scan performed on 20 core plugs from two data cores, each core plugs was plotted as many as 15 points. The output is the CT-Porosity value that will be used for the distribution of predictions of PGS permeability. Based on the result, porosity and permeability range value from 5 – 11%; 0.015 – 24.5 mD and presents a poor to fair reservoir quality.

Comparative Permeability Estimation Method and Identification of Rock Types using Cluster Analysis from Well Logs and Core Analysis Data in Tertiary Carbonate Reservoir-Khabaz Oil Field

Characterization of the heterogonous reservoir is complex representation and evaluation of petrophysical properties and application of the relationships between porosity-permeability within the framework of hydraulic flow units is used to estimate permeability in un-cored wells. Techniques of flow unit or hydraulic flow unit (HFU) divided the reservoir into zones laterally and vertically which can be managed and control fluid flow within flow unit and considerably is entirely different with other flow units through reservoir. Each flow unit can be distinguished by applying the relationships of flow zone indicator (FZI) method. Supporting the relationship between porosity and permeability by using flow zone indictor is carried out for evaluating the reservoir quality and identification of flow unit used in reservoir zonation. In this study, flow zone indicator has been used to identify five layers belonging to Tertiary reservoirs. Consequently, the porosity-permeability cross plot has been done depending on FZI values as groups and for each group denoted to reservoir rock types. On the other hand, extending rock type identification in un-cored wells should apply a cluster analysis approach by using well logs data. Reservoir zonation has been achieved by cluster analysis approach and for each group known as cluster which variation and different with others. Five clusters generated in this study and permeability estimated depend on these groups in un-cored wells by using well log data that gives good results compared with different empirical methods.

Application of hydraulic flow units\u2019 approach for improving reservoir characterization and predicting permeability

It is highly essential to characterize the reservoir accurately as possible to implement enhanced oil recovery and development scenarios. Determining complex variations in pore geometry aids in identifying rock types with similar fluid flow properties. Traditional classification of rock types is based on geologic observations and empirical relations between porosity and permeability. However, for a given porosity, permeability can vary by several orders of magnitude. This reveals that porosity only cannot interpret the permeability variation. The proposed approach to define the existence of different rock types in the reservoir is the “hydraulic flow units’ approach.” The approach aims to enhance the reservoir characterization and predict permeability in un-cored wells to reduce the cost of drilling core samples. The approach involves dividing the reservoir into hydraulic flow units (HFUs). It introduces the concept of reservoir quality index and flow zone indicator (FZI) to identify the HFUs. Each unit is imprinted by certain FZI and said to have similar geological and petro-physical properties. Validation of results was done by integrating the HFUs with core description results—particularly samples’ grain sizes—to ascertain the accuracy of the approach. FZI was then integrated with well logs using multiple regression analysis in order to train the logs to recognize the hydraulic flow units in case of absence of core. A regression model was developed for each flow unit, from which FZI can be estimated from logs only. FZI was then correlated with permeability to compute permeability. Results showed that four HFUs exist in the reservoir. Four categories of grain sizes were identified from core analysis. This emphasized the accuracy of the proposed technique. Besides, integration between core data and well-logging ones showed high degree of correlation between well logs and FZI. Expanding this correlation aids in predicting permeability in un-cored well.

Permeability Prediction By Classical and Flow Zone Indictor (FZI) Methods for an Iraqi Gas Field

The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

Predicting Permeability in Un-Cored Intervals/Wells Using Hydraulic Flow Unit Approach

Abstract Prediction of permeability in un-cored intervals/wells is necessary to developing an effective reservoir characterization program and providing information on various depositional and diagenetic controls on pore geometry. Permeability can be obtained from well tests, core analysis, and well logs. Usually using well log data to derive estimates of permeability is the cheapest source. In this paper, routine core analysis and well log data of an actually existing sandstone reservoir in the Gulf of Suez (Egypt) were used to develop permeability correlations using a Hydraulic Flow Unit Approach (HFU). The core data were statistically treated for identification of Hydraulic Flow Units. This involves the calculations of Reservoir Quality Index (RQI), Flow Zone Indicator (FZI), and Pore Level Heterogeneity Index (PLHI). Results showed that six HFUs were identified from core data and each unit has its own FZI. A correlation between FZI calculated from core data and that obtained from well log data was developed for estimating permeability in un-cored intervals/wells. Application of the Pore Level Heterogeneity Index concept to the data determined the existence of six rock types in the studied reservoir. Introduction Estimating permeability in un-cored intervals/wells is a generic problem common to all reservoir engineers. Most of the field-scale reservoir characterization studies require detailed knowledge of petrophysical properties at the drilled wells. Since most wells are not cored, an accurate calculation method of permeability in uncored wells from log data is needed for achieving a realistic reservoir description. Several investigators(1–4) have proposed traditional approaches for permeability estimation. The approaches were based on linear regression or empirical inferences that use correlations among various well log responses. The empirically inferred models are locally applied because there may be large differences in depositional characteristics at other locations. The regression models assume that a linear relationship between logarithmic permeability and porosity exists. Another linear relationship between core porosity and log porosity was also proposed by Canas et al.(5). Both methods ignore the scatter of the data around the fitted line and attribute any scatter to measurement errors. A more reasonable technique is to employ the concept of Hydraulic Flow Units (HFUs) in predicting permeability and reservoir Description(6–12). The term Hydraulic Flow Unit is defined as a representative element volume of the total reservoir rock within which geological and petrophysical properties that affect fluid flow are internally consistent and predictably different from properties of other rock HFUs. Generally, permeability calculations using HFUs offer improved estimations over traditional regression-based averaged relationships. The extent of improvement depends mainly on the geological characteristics of the formation, with larger enhancements achieved in more heterogeneous deposits. In the present study, 510 measurements of porosity and permeability of different sandstone rocks, obtained from an oil reservoir existing offshore of Egypt (Gulf of Suez) were used to develop correlations for in situ permeability estimations. Statistical examination of the developed correlations shows a good agreement between the measured and calculated data.

The Role of Electrofacies, Lithofacies, and Hydraulic Flow Units in Permeability Prediction From Well Logs: A Comparative Analysis Using Classification Trees

Summary Predicting permeability from well logs typically involves classification of the well-log response into relatively homogeneous subgroups based on electrofacies, Lithofacies, or hydraulic flow units (HFUs). The electrofacies-based classification involves identifying clusters in the well-log response that reflect "similar" minerals and lithofacies within the logged interval. This statistical procedure is straightforward and inexpensive. However, identification of lithofacies and HFUs relies on core-data analysis and can be expensive and time-consuming. To date, no systematic study has been performed to investigate the relative merits of the three methods in terms of their ability to predict permeability in uncored wells. The purpose of this paper is three-fold. First, we examine the interrelationship between the three approaches using a powerful and yet intuitive statistical tool called "classification-tree analysis." The tree-based method is an exploratory technique that allows for a straightforward determination of the relative importance of the well logs in identifying electrofacies, lithofacies, and HFUs. Second, we use the tree-based method to propose an approach to account for missing well logs during permeability predictions. This is a common problem encountered during field applications. Our approach follows directly from the hierarchical decision tree that visually and quantitatively illustrates the relationship between the data groupings and the individual well-log response. Finally, we demonstrate the power and utility of our approach via field applications involving permeability predictions in a highly complex carbonate reservoir, the Salt Creek Field Unit (SCFU) in west Texas. The intuitive and visual nature of the tree-classifier approach also makes it a powerful tool for communication between geologists and engineers.

Carbonate Reservoir Characterization

Technology Today Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. During the past 15 years, methods and technology for predicting the performance of carbonate reservoirs have improved dramatically. This advance occurred in response to the realization that more than half of the oil that could be swept by waterflooding is not contacted and remains in the reservoir. Attempts to simulate this performance and locate the remaining oil by use of simple reservoir models and newly developed flow-simulation computer programs have failed mainly because of the extreme heterogeneity that characterizes carbonate reservoirs. It is, in fact, the extreme geologic and petrophysical heterogeneity typical of carbonate reservoirs that distinguishes carbonate from siliciclastic reservoirs. Research programs focused on understanding the nature of the heterogeneity and developing methods to characterize carbonate reservoirs, together with improvements in computer capability and simulation programs, have led to more reliable predictions of reservoir performance and to methods for locating volumes of unswept oil in reservoirs under waterflood. Reservoir characterization encompasses the understanding and methods used to characterize reservoir heterogeneity. It can be defined as the construction of realistic 3D images of petrophysical properties used to predict reservoir performance, and it is a multidisciplinary, integrated task involving expertise in reservoir geology, geophysics, petrophysics, well logging, geostatistics, and reservoir engineering. Three-dimensional images are obtained from geological models constructed with core, wireline-log, and geophysical data. Petrophysical properties, obtained from core, wireline-log, and production data, are distributed within the geological model by linking petrophysical properties to geologic fabrics and by use of advanced geostatistical and geophysical methods. Finally, the model is put into a numerical simulator for testing and predicting future performance. The extreme petrophysical heterogeneity found in carbonate reservoirs is clearly demonstrated by the wide variability observed in porosity-permeability crossplots of core-analysis data. Research has shown that basic rock fabrics dominate control of petrophysical heterogeneity; within a rock-fabric facies, porosity and permeability have little spatial correlation and are widely variable at the scale of inches and feet. Permeability, in particular, can vary by a factor of 10 or more at the small scale and is nearly randomly distributed (Fig. 1).1,2 This result suggests that much of the variability observed in core-analysis data is spatial noise and can be averaged within rock-fabric facies for the purposes of constructing a reservoir model. Only rock fabrics, not pore-throat size, permeability/porosity ratio, or flow-zone indicators, have vertical and lateral continuity. Therefore, rock-fabric facies are the basic elements for characterizing a carbonate reservoir.3 Rock fabrics are geologic descriptors that characterize pore size according to particle size and sorting, interparticle porosity, and various types of vuggy porosity. The main limestone rock fabrics are grainstone, grain-dominated packstone, and mud-dominated fabrics. Dolostone rock fabrics are similar but require a description of dolomite crystal size in mud-dominated dolostones as an added control on pore size. These basic fabrics are modified by the amount of fabric-selective vuggy pore space,4 and many rock fabrics can be linked directly to depositional facies. However, some rock fabrics cannot be linked to depositional facies because extensive modifications to the fabric have occurred since deposition through a geologic process known as diagenesis. Constructing models of reservoirs comprising complex fabrics, such as fracture and karst fabrics, is difficult and is the subject of current research. Each rock fabric has a specific porosity-permeability transform, and the vertical stacking of rock-fabric facies, together with interparticle porosity, provides the basis for estimating permeability in uncored wells. The wireline-log problem is determining interparticle porosity, as well as total porosity and rock fabric, for input into a general porosity-permeability transform. Porosity, acoustic velocity, resistivity, and saturation logs are used for this task, and calibrating wireline-log responses to core descriptions of rock fabrics is key to developing useful algorithms.5 Because rock-fabric and petrophysical data obtained from cores and wireline logs are one-dimensional, a geological framework is required to distribute the data in 3D space. In the past, geological models were constructed by identifying depositional facies from core data, then distributing those facies by use of depositional models on the basis of modern carbonate depositional processes. However, reservoirs described in this manner do not contain sufficient detail to capture basic reservoir heterogeneities, and facies correlations from well to well often are highly uncertain. Development of sequence stratigraphic methods greatly enhanced the accuracy of well-to-well correlations and provided the means of capturing basic scales of reservoir heterogeneity.

Carbonate reservoir characterization

Technology Today Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. During the past 15 years, methods and technology for predicting the performance of carbonate reservoirs have improved dramatically. This advance occurred in response to the realization that more than half of the oil that could be swept by waterflooding is not contacted and remains in the reservoir. Attempts to simulate this performance and locate the remaining oil by use of simple reservoir models and newly developed flow-simulation computer programs have failed mainly because of the extreme heterogeneity that characterizes carbonate reservoirs. It is, in fact, the extreme geologic and petrophysical heterogeneity typical of carbonate reservoirs that distinguishes carbonate from siliciclastic reservoirs. Research programs focused on understanding the nature of the heterogeneity and developing methods to characterize carbonate reservoirs, together with improvements in computer capability and simulation programs, have led to more reliable predictions of reservoir performance and to methods for locating volumes of unswept oil in reservoirs under waterflood. Reservoir characterization encompasses the understanding and methods used to characterize reservoir heterogeneity. It can be defined as the construction of realistic 3D images of petrophysical properties used to predict reservoir performance, and it is a multidisciplinary, integrated task involving expertise in reservoir geology, geophysics, petrophysics, well logging, geostatistics, and reservoir engineering. Three-dimensional images are obtained from geological models constructed with core, wireline-log, and geophysical data. Petrophysical properties, obtained from core, wireline-log, and production data, are distributed within the geological model by linking petrophysical properties to geologic fabrics and by use of advanced geostatistical and geophysical methods. Finally, the model is put into a numerical simulator for testing and predicting future performance. The extreme petrophysical heterogeneity found in carbonate reservoirs is clearly demonstrated by the wide variability observed in porosity-permeability crossplots of core-analysis data. Research has shown that basic rock fabrics dominate control of petrophysical heterogeneity; within a rock-fabric facies, porosity and permeability have little spatial correlation and are widely variable at the scale of inches and feet. Permeability, in particular, can vary by a factor of 10 or more at the small scale and is nearly randomly distributed (Fig. 1).1,2 This result suggests that much of the variability observed in core-analysis data is spatial noise and can be averaged within rock-fabric facies for the purposes of constructing a reservoir model. Only rock fabrics, not pore-throat size, permeability/porosity ratio, or flow-zone indicators, have vertical and lateral continuity. Therefore, rock-fabric facies are the basic elements for characterizing a carbonate reservoir.3 Rock fabrics are geologic descriptors that characterize pore size according to particle size and sorting, interparticle porosity, and various types of vuggy porosity. The main limestone rock fabrics are grainstone, grain-dominated packstone, and mud-dominated fabrics. Dolostone rock fabrics are similar but require a description of dolomite crystal size in mud-dominated dolostones as an added control on pore size. These basic fabrics are modified by the amount of fabric-selective vuggy pore space,4 and many rock fabrics can be linked directly to depositional facies. However, some rock fabrics cannot be linked to depositional facies because extensive modifications to the fabric have occurred since deposition through a geologic process known as diagenesis. Constructing models of reservoirs comprising complex fabrics, such as fracture and karst fabrics, is difficult and is the subject of current research. Each rock fabric has a specific porosity-permeability transform, and the vertical stacking of rock-fabric facies, together with interparticle porosity, provides the basis for estimating permeability in uncored wells. The wireline-log problem is determining interparticle porosity, as well as total porosity and rock fabric, for input into a general porosity-permeability transform. Porosity, acoustic velocity, resistivity, and saturation logs are used for this task, and calibrating wireline-log responses to core descriptions of rock fabrics is key to developing useful algorithms.5 Because rock-fabric and petrophysical data obtained from cores and wireline logs are one-dimensional, a geological framework is required to distribute the data in 3D space. In the past, geological models were constructed by identifying depositional facies from core data, then distributing those facies by use of depositional models on the basis of modern carbonate depositional processes. However, reservoirs described in this manner do not contain sufficient detail to capture basic reservoir heterogeneities, and facies correlations from well to well often are highly uncertain. Development of sequence stratigraphic methods greatly enhanced the accuracy of well-to-well correlations and provided the means of capturing basic scales of reservoir heterogeneity.