Enhanced and Rock Typing-Based Reservoir Characterization of the Palaeocene Harash Carbonate Reservoir-Zelten Field-Sirte Basin-Libya

2021 ◽  
Author(s):  
Mohamed Masoud ◽  
W. Scott Meddaugh ◽  
Masoud Eljaroshi ◽  
Khaled Elghanduri
Keyword(s):  
Rock Type ◽  
Carbonate Reservoir ◽  
Classification Model ◽  
Reservoir Rocks ◽  
The Core ◽  
Core Samples ◽  
Rock Types ◽  
Rock Typing ◽  
Open Hole ◽  
Type Classification

Abstract The Harash Formation was previously known as the Ruaga A and is considered to be one of the most productive reservoirs in the Zelten field in terms of reservoir quality, areal extent, and hydrocarbon quantity. To date, nearly 70 wells were drilled targeting the Harash reservoir. A few wells initially naturally produced but most had to be stimulated which reflected the field drilling and development plan. The Harash reservoir rock typing identification was essential in understanding the reservoir geology implementation of reservoir development drilling program, the construction of representative reservoir models, hydrocarbons volumetric calculations, and historical pressure-production matching in the flow modelling processes. The objectives of this study are to predict the permeability at un-cored wells and unsampled locations, to classify the reservoir rocks into main rock typing, and to build robust reservoir properties models in which static petrophysical properties and fluid properties are assigned for identified rock type and assessed the existed vertical and lateral heterogeneity within the Palaeocene Harash carbonate reservoir. Initially, an objective-based workflow was developed by generating a training dataset from open hole logs and core samples which were conventionally and specially analyzed of six wells. The developed dataset was used to predict permeability at cored wells through a K-mod model that applies Neural Network Analysis (NNA) and Declustring (DC) algorithms to generate representative permeability and electro-facies. Equal statistical weights were given to log responses without analytical supervision taking into account the significant log response variations. The core data was grouped on petrophysical basis to compute pore throat size aiming at deriving and enlarging the interpretation process from the core to log domain using Indexation and Probabilities of Self-Organized Maps (IPSOM) classification model to develop a reliable representation of rock type classification at the well scale. Permeability and rock typing derived from the open-hole logs and core samples analysis are the main K-mod and IPSOM classification model outputs. The results were propagated to more than 70 un-cored wells. Rock typing techniques were also conducted to classify the Harash reservoir rocks in a consistent manner. Depositional rock typing using a stratigraphic modified Lorenz plot and electro-facies suggest three different rock types that are probably linked to three flow zones. The defined rock types are dominated by specifc reservoir parameters. Electro-facies enables subdivision of the formation into petrophysical groups in which properties were assigned to and were characterized by dynamic behavior and the rock-fluid interaction. Capillary pressure and relative permeability data proved the complexity in rock capillarity. Subsequently, Swc is really rock typing dependent. The use of a consistent representative petrophysical rock type classification led to a significant improvement of geological and flow models.

Improved Prediction of Reservoir Behavior Through Integration of Quantitative Geological and Petrophysical Data

1999 ◽  
Vol 2 (02) ◽  
pp. 149-160 ◽  
Author(s):  
D.K. Davies ◽  
R.K. Vessell ◽  
J.B. Auman
Keyword(s):  
Body Size ◽  
Reservoir Characterization ◽  
Rock Type ◽  
Carbonate Reservoir ◽  
High Porosity ◽  
Geometrical Characteristics ◽  
Rock Types ◽  
Flow Units ◽  
Reservoir Description ◽  
Pore Body

Summary This paper presents a cost effective, quantitative methodology for reservoir characterization that results in improved prediction of permeability, production and injection behavior during primary and enhanced recovery operations. The method is based fundamentally on the identification of rock types (intervals of rock with unique pore geometry). This approach uses image analysis of core material to quantitatively identify various pore geometries. When combined with more traditional petrophysical measurements, such as porosity, permeability and capillary pressure, intervals of rock with various pore geometries (rock types) can be recognized from conventional wireline logs in noncored wells or intervals. This allows for calculation of rock type and improved estimation of permeability and saturation. Based on geological input, the reservoirs can then be divided into flow units (hydrodynamically continuous layers) and grid blocks for simulation. Results are presented of detailed studies in two, distinctly different, complex reservoirs: a low porosity carbonate reservoir and a high porosity sandstone reservoir. When combined with production data, the improved characterization and predictability of performance obtained using this unique technique have provided a means of targeting the highest quality development drilling locations, improving pattern design, rapidly recognizing conformance and formation damage problems, identifying bypassed pay intervals, and improving assessments of present and future value. Introduction This paper presents a technique for improved prediction of permeability and flow unit distribution that can be used in reservoirs of widely differing lithologies and differing porosity characteristics. The technique focuses on the use and integration of pore geometrical data and wireline log data to predict permeability and define hydraulic flow units in complex reservoirs. The two studies presented here include a low porosity, complex carbonate reservoir and a high porosity, heterogeneous sandstone reservoir. These reservoir classes represent end-members in the spectrum of hydrocarbon reservoirs. Additionally, these reservoirs are often difficult to characterize (due to their geological complexity) and frequently contain significant volumes of remaining reserves.1 The two reservoir studies are funded by the U.S. Department of Energy as part of the Class II and Class III Oil Programs for shallow shelf carbonate (SSC) reservoirs and slope/basin clastic (SBC) reservoirs. The technique described in this paper has also been used to characterize a wide range of other carbonate and sandstone reservoirs including tight gas sands (Wilcox, Vicksburg, and Cotton Valley Formations, Texas), moderate porosity sandstones (Middle Magdalena Valley, Colombia and San Jorge Basin, Argentina), and high porosity reservoirs (Offshore Gulf Coast and Middle East). The techniques used for reservoir description in this paper meet three basic requirements that are important in mature, heterogeneous fields.The reservoir descriptions are log-based. Flow units are identified using wireline logs because few wells have cores. Integration of data from analysis of cores is an essential component of the log models.Accurate values of permeability are derived from logs. In complex reservoirs, values of porosity and saturation derived from routine log analysis often do not accurately identify productivity. It is therefore necessary to develop a log model that will allow the prediction of another producibility parameter. In these studies we have derived foot-by-foot values of permeability for cored and non-cored intervals in all wells with suitable wireline logs.Use only the existing databases. No new wells will be drilled to aid reservoir description. Methodology Techniques of reservoir description used in these studies are based on the identification of rock types (intervals of rock with unique petrophysical properties). Rock types are identified on the basis of measured pore geometrical characteristics, principally pore body size (average diameter), pore body shape, aspect ratio (size of pore body: size of pore throat) and coordination number (number of throats per pore). This involves the detailed analysis of small rock samples taken from existing cores (conventional cores and sidewall cores). The rock type information is used to develop the vertical layering profile in cored intervals. Integration of rock type data with wireline log data allows field-wide extrapolation of the reservoir model from cored to non-cored wells. Emphasis is placed on measurement of pore geometrical characteristics using a scanning electron microscope specially equipped for automated image analysis procedures.2–4 A knowledge of pore geometrical characteristics is of fundamental importance to reservoir characterization because the displacement of hydrocarbons is controlled at the pore level; the petrophysical properties of rocks are controlled by the pore geometry.5–8 The specific procedure includes the following steps.Routine measurement of porosity and permeability.Detailed macroscopic core description to identify vertical changes in texture and lithology for all cores.Detailed thin section and scanning electron microscope analyses (secondary electron imaging mode) of 100 to 150 small rock samples taken from the same locations as the plugs used in routine core analysis. In the SBC reservoir, x-ray diffraction analysis is also used. The combination of thin section and x-ray analyses provides direct measurement of the shale volume, clay volume, grain size, sorting and mineral composition for the core samples analyzed.Rock types are identified for each rock sample using measured data on pore body size, pore throat size and pore interconnectivity (coordination number and pore arrangement).

Rock Typing as an Effective Tool for Permeability and Water-Saturation Modeling: A Case Study in a Clastic Reservoir in the Oriente Basin

2007 ◽  
Vol 10 (06) ◽  
pp. 730-739 ◽  
Author(s):  
Genliang Guo ◽  
Marlon A. Diaz ◽  
Francisco Jose Paz ◽  
Joe Smalley ◽  
Eric A. Waninger
Keyword(s):  
Relative Permeability ◽  
Reservoir Simulation ◽  
Water Saturation ◽  
Rock Type ◽  
Permeability Model ◽  
Initial Water ◽  
Rock Types ◽  
Rock Typing ◽  
Relative Permeability Curves ◽  
Clastic Reservoirs

Summary In clastic reservoirs in the Oriente basin, South America, the rock-quality index (RQI) and flow-zone indicator (FZI) have proved to be effective techniques for rock-type classifications. It has long been recognized that excellent permeability/porosity relationships can be obtained once the conventional core data are grouped according to their rock types. Furthermore, it was also observed from this study that the capillary pressure curves, as well as the relative permeability curves, show close relationships with the defined rock types in the basin. These results lead us to believe that if the rock type is defined properly, then a realistic permeability model, a unique set of relative permeability curves, and a consistent J function can be developed for a given rock type. The primary purpose of this paper is to demonstrate the procedure for implementing this technique in our reservoir modeling. First, conventional core data were used to define the rock types for the cored intervals. The wireline log measurements at the cored depths were extracted, normalized, and subsequently analyzed together with the calculated rock types. A mathematical model was then built to predict the rock type in uncored intervals and in uncored wells. This allows the generation of a synthetic rock-type log for all wells with modern log suites. Geostatistical techniques can then be used to populate the rock type throughout a reservoir. After rock type and porosity are populated properly, the permeability can be estimated by use of the unique permeability/porosity relationship for a given rock type. The initial water saturation for a reservoir can be estimated subsequently by use of the corresponding rock-type, porosity, and permeability models as well as the rock-type-based J functions. We observed that a global permeability multiplier became unnecessary in our reservoir-simulation models when the permeability model is constructed with this technique. Consistent initial-water-saturation models (i.e., calculated and log-measured water saturations are in excellent agreement) can be obtained when the proper J function is used for a given rock type. As a result, the uncertainty associated with volumetric calculations is greatly reduced as a more accurate initial-water-saturation model is used. The true dynamic characteristics (i.e., the flow capacity) of the reservoir are captured in the reservoir-simulation model when a more reliable permeability model is used. Introduction Rock typing is a process of classifying reservoir rocks into distinct units, each of which was deposited under similar geological conditions and has undergone similar diagenetic alterations (Gunter et al. 1997). When properly classified, a given rock type is imprinted by a unique permeability/porosity relationship, capillary pressure profile (or J function), and set of relative permeability curves (Gunter et al. 1997; Hartmann and Farina 2004; Amaefule et al. 1993). As a result, when properly applied, rock typing can lead to the accurate estimation of formation permeability in uncored intervals and in uncored wells; reliable generation of initial-water-saturation profile; and subsequently, the consistent and realistic simulation of reservoir dynamic behavior and production performance. Of the various quantitative rock-typing techniques (Gunter et al. 1997; Hartmann and Farina 2004; Amaefule et al. 1993; Porras and Campos 2001; Jennings and Lucia 2001; Rincones et al. 2000; Soto et al. 2001) presented in the literature, two techniques (RQI/FZI and Winland's R35) appear to be used more widely than the others for clastic reservoirs (Gunter et al. 1997, Amaefule et al. 1993). In the RQI/FZI approach (Amaefule et al. 1993), rock types are classified with the following three equations: [equations]

scholarly journals Integrated rock typing and pore facies analyses in a heterogeneous carbonate for saturation height modelling, a case study from Fahliyan Formation, the Persian Gulf

2021 ◽  
Vol 11 (4) ◽  
pp. 1577-1595
Author(s):  
Rasoul Ranjbar-Karami ◽  
Parisa Tavoosi Iraj ◽  
Hamzeh Mehrabi
Keyword(s):  
Capillary Pressure ◽  
Water Saturation ◽  
High Energy ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Initial Water ◽  
Rock Types ◽  
Rock Typing ◽  
Pore Types ◽  
Fitting Parameters

AbstractKnowledge of initial fluids saturation has great importance in hydrocarbon reservoir analysis and modelling. Distribution of initial water saturation (Swi) in 3D models dictates the original oil in place (STOIIP), which consequently influences reserve estimation and dynamic modelling. Calculation of initial water saturation in heterogeneous carbonate reservoirs always is a challenging task, because these reservoirs have complex depositional and diagenetic history with a complex pore network. This paper aims to model the initial water saturation in a pore facies framework, in a heterogeneous carbonate reservoir. Petrographic studies were accomplished to define depositional facies, diagenetic features and pore types. Accordingly, isolated pores are dominant in the upper parts, while the lower intervals contain more interconnected interparticle pore types. Generally, in the upper and middle parts of the reservoir, diagenetic alterations such as cementation and compaction decreased the primary reservoir potential. However, in the lower interval, which mainly includes high-energy shoal facies, high reservoir quality was formed by primary interparticle pores and secondary dissolution moulds and vugs. Using huge number of primary drainage mercury injection capillary pressure tests, we evaluate the ability of FZI, r35Winland, r35Pittman, FZI* and Lucia’s petrophysical classes in definition of rock types. Results show that recently introduced rock typing method is an efficient way to classify samples into petrophysical rock types with same pore characteristics. Moreover, as in this study MICP data were available from every one meter of reservoir interval, results show that using FZI* method much more representative sample can be selected for SCAL laboratory tests, in case of limitation in number of SCAL tests samples. Integration of petrographic analyses with routine (RCAL) and special (SCAL) core data resulted in recognition of four pore facies in the studied reservoir. Finally, in order to model initial water saturation, capillary pressure data were averaged in each pore facies which was defined by FZI* method and using a nonlinear curve fitting approach, fitting parameters (M and C) were extracted. Finally, relationship between fitting parameters and porosity in core samples was used to model initial water saturation in wells and between wells. As permeability prediction and reservoir rock typing are challenging tasks, findings of this study help to model initial water saturation using log-derived porosity.

scholarly journals Experimental investigation on potential use of drilling parameters to quantify rock strength

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
C. R. Lakshminarayana ◽  
Anup Kumar Tripathi ◽  
Samir Kumar Pal
Keyword(s):  
Experimental Investigation ◽  
Standard Procedure ◽  
Blast Hole ◽  
Acceptable Error ◽  
The Core ◽  
Core Samples ◽  
Rock Types ◽  
Drilling Parameters ◽  
Mining Projects

AbstractThe uniaxial compressive strength (UCS) represents the strength of the rock. It frequently requires during the introductory phase of mining projects such as tunneling, rock excavation, blast hole designs, etc. Usually, the determination of UCS of rocks is carrying out in a concerned laboratory. The main drawback of determining the UCS in a laboratory requires at least five core samples of high-grade quality. Many problems and limitations are associated with removing the core, and also preparing the test specimen for UCS is tedious, time-consuming, and expensive. Therefore an attempt is made to develop an efficient indirect method to estimate the UCS of rocks without using the core samples. In this experimental investigation, the drilling response, such as thrust, is gathered by drill tool dynamometer considering the different drill operating parameters. The prediction model is developed with a regression technique using the measured thrust and calculated torque. The prediction capacity and validation of the model are carried out using the standard procedure. The experimental results show that the model could explain the variance in UCS up to 93.60%. RMSE and MAPE values in terms of percentage are 3.49% and 11.27%, respectively. Besides, the model's validation is checked for sandstone and limestone having the UCS 28 MPa and 35 MPa, respectively, and yielded the best prediction results with an error of 8.51% and 8.01% suggesting that the developed model could predict the UCS of sedimentary rock types within acceptable error limit, and reasonably. The correlation of UCS of rocks and drilling specific energy is also tested and found that linear relationship between them with an R2 value of 92.10%.

Rock Type Classification Models Interpretability Using Shapley Values

2021 ◽  
Author(s):  
Anton Georgievich Voskresenskiy ◽  
Nikita Vladimirovich Bukhanov ◽  
Maria Alexandrovna Kuntsevich ◽  
Oksana Anatolievna Popova ◽  
Alexey Sergeevich Goncharov
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Causal Inference ◽  
Rock Type ◽  
Well Logs ◽  
Well Log ◽  
Classification Models ◽  
Rock Types ◽  
Shapley Values ◽  
Type Classification

Abstract We propose a methodology to improve rock type classification using machine learning (ML) techniques and to reveal causal inferences between reservoir quality and well log measurements. Rock type classification is an essential step in accurate reservoir modeling and forecasting. Machine learning approaches allow to automate rock type classification based on different well logs and core data. In order to choose the best model which does not progradate uncertainty further into the workflow it is important to interpret machine learning results. Feature importance and feature selection methods are usually employed for that. We propose an extension to existing approaches - model agnostic sensitivity algorithm based on Shapley values. The paper describes a full workflow to rock type prediction using well log data: from data preparation, model building, feature selection to causal inference analysis. We made ML models that classify rock types using well logs (sonic, gamma, density, photoelectric and resistivity) from 21 wells as predictors and conduct a causal inference analysis between reservoir quality and well logs responses using Shapley values (a concept from a game theory). As a result of feature selection, we obtained predictors which are statistically significant and at the same time relevant in causal relation context. Macro F1-score of the best obtained models for both cases is 0.79 and 0.85 respectively. It was found that the ML models can infer domain knowledge, which allows us to confirm the adequacy of the built ML model for rock types prediction. Our insight was to recognize the need to properly account for the underlying causal structure between the features and rock types in order to derive meaningful and relevant predictors that carry a significant amount of information contributing to the final outcome. Also, we demonstrate the robustness of revealed patterns by applying the Shapley values methodology to a number of ML models and show consistency in order of the most important predictors. Our analysis shows that machine learning classifiers gaining high accuracy tend to mimic physical principles behind different logging tools, in particular: the longer the travel time of an acoustic wave the higher probability that media is represented by reservoir rock and vice versa. On the contrary lower values of natural radioactivity and density of rock highlight the presence of a reservoir. The article presents causal inference analysis of ML classification models using Shapley values on 2 real-world reservoirs. The rock class labels from core data are used to train a supervised machine learning algorithm to predict classes from well log response. The aim of supervised learning is to label a small portion of a dataset and allow the algorithm to automate the rest. Such data-driven analysis may optimize well logging, coring, and core analysis programs. This algorithm can be extended to any other reservoir to improve rock type prediction. The novelty of the paper is that such analysis reveals the nature of decisions made by the ML model and allows to apply truly robust and reliable petrophysics-consistent ML models for rock type classification.

Rock typing in the Upper Devonian-Lower Mississippian Woodford Shale Formation, Oklahoma, USA

2018 ◽  
Vol 6 (1) ◽  
pp. SC55-SC66 ◽  
Author(s):  
Ishank Gupta ◽  
Chandra Rai ◽  
Carl Sondergeld ◽  
Deepak Devegowda
Keyword(s):  
Gamma Ray ◽  
Rock Type ◽  
Clustering Algorithms ◽  
Support Vector ◽  
Carbonate Content ◽  
High Porosity ◽  
Woodford Shale ◽  
Rock Types ◽  
Rock Typing

Most U.S. shale plays are spatially extensive with regions of different thermal maturity and varying production prospects. With increasing understanding of the heterogeneity, microstructure, and anisotropy of shales, efforts are now directed to identifying the sweet spots and optimum completion zones in any shale play. Rock typing is a step in this direction. We have developed an integrated workflow for rock typing using laboratory-petrophysical measurements on core samples and well logs. A total of seven wells with core data were considered for rock typing in the Woodford Shale. The integrated workflow has been applied in the Woodford Shale in a series of steps. In the first step, unsupervised clustering algorithms such as [Formula: see text]-means and self-organizing maps were used to define the rock types. Rock type 1 is generally characterized by high porosity and total organic carbon (TOC). Rock type 2 had intermediate values of porosity and TOC and thus, moderate source potential and storage. Rock type 3 had the highest carbonate content, poor storage, and source rock potential. In the next step, a classification algorithm, support vector machines (SVM), was used to extend the rock types from the cores to the logs. A logging suite with gamma ray, resistivity, neutron porosity, and density logs was used for extending the rock types. These logs were used because they are commonly available and adequate to differentiate different rock types. The rock types were populated in the uncored sections of the seven cored wells and additionally in 12 wells (taken from Drilling Info) using a trained SVM model. Additional wells were taken to have sufficient data for production correlation. In the final step, a rock-type ratio (RTR) was defined based on the fraction of rock type 1 over the gross thickness. RTR was found to positively correlate with normalized oil equivalent production.

scholarly journals Numerical Simulation Modeling of Carbonate Reservoir Based on Rock Type

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Peiqing Lian ◽  
Cuiyu Ma ◽  
Bingyu Ji ◽  
Taizhong Duan ◽  
Xuequn Tan
Keyword(s):  
History Matching ◽  
Water Saturation ◽  
Rock Type ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Column Height ◽  
Fluid Distribution ◽  
Type Model ◽  
Type Curves ◽  
Rock Types

There are many types of carbonate reservoir rock spaces with complex shapes, and their primary pore structure changes dramatically. In order to describe the heterogeneity of K carbonate reservoir, equations of porosity, permeability and pore throat radii under different mercury injection saturations are fitted, and it shows that 30% is the best percentile. R30 method is presented for rock typing, and six rock types are divided according to R30 value of plugs. The porosity-permeability relationship is established for each rock type, and their relevant flow characteristics of each rock type have been studied. Logs are utilized to predict rock types of noncored wells, and a three-dimensional (3D) rock type model has been established based on the well rock type curves and the sedimentary facies constraint. Based on the relationship between J function and water saturation, the formula of water saturation, porosity, permeability, and oil column height can be obtained by multiple regressions for each rock type. Then, the water saturation is calculated for each grid, and a 3D water saturation model is established. The model can reflect the formation heterogeneity and the fluid distribution, and its accuracy is verified by the history matching.

scholarly journals Carbonate reservoir rock typing algorithm based on integrated approach (IRT)

2021 ◽  
Vol 6 (4) ◽  
pp. 62-70
Author(s):  
Mariia A. Kuntsevich ◽  
Sergey V. Kuznetsov ◽  
Igor V. Perevozkin
Keyword(s):  
Water Saturation ◽  
Integrated Approach ◽  
Learning Technologies ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Static Properties ◽  
Reservoir Properties ◽  
Optimal Method ◽  
Rock Types ◽  
Rock Typing

The goal of carbonate rock typing is a realistic distribution of well data in a 3D model and the distribution of the corresponding rock types, on which the volume of hydrocarbon reserves and the dynamic characteristics of the flow will depend. Common rock typing approaches for carbonate rocks are based on texture, pore classification, electrofacies, or flow unit localization (FZI) and are often misleading because they based on sedimentation processes or mathematical justification. As a result, the identified rock types may poorly reflect the real distribution of reservoir rock characteristics. Materials and methods. The approach described in the work allows to eliminate such effects by identifying integrated rock types that control the static properties and dynamic behavior of the reservoir, while optimally linking with geological characteristics (diagenetic transformations, sedimentation features, as well as their union effect) and petrophysical characteristics (reservoir properties, relationship between the porosity and permeability, water saturation, radius of pore channels and others). The integrated algorithm consists of 8 steps, allowing the output to obtain rock-types in the maximum possible way connecting together all the characteristics of the rock, available initial information. The first test in the Middle East field confirmed the applicability of this technique. Results. The result of the work was the creation of a software product (certificate of state registration of the computer program “Lucia”, registration number 2021612075 dated 02/11/2021), which allows automating the process of identifying rock types in order to quickly select the most optimal method, as well as the possibility of their integration. As part of the product, machine learning technologies were introduced to predict rock types based on well logs in intervals not covered by coring studies, as well as in wells in which there is no coring.

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