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

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.

2021 ◽  
Author(s):  
Mohamed Masoud ◽  
W. Scott Meddaugh ◽  
Masoud Eljaroshi ◽  
Khaled Elghanduri

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.


1999 ◽  
Vol 2 (02) ◽  
pp. 149-160 ◽  
Author(s):  
D.K. Davies ◽  
R.K. Vessell ◽  
J.B. Auman

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).


2007 ◽  
Vol 10 (06) ◽  
pp. 730-739 ◽  
Author(s):  
Genliang Guo ◽  
Marlon A. Diaz ◽  
Francisco Jose Paz ◽  
Joe Smalley ◽  
Eric A. Waninger

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]


2020 ◽  
Vol 123 (4) ◽  
pp. 479-492
Author(s):  
M. Opuwari ◽  
M. Amponsah-Dacosta ◽  
S. Mohammed ◽  
N. Egesi

Abstract The present study is focused on the comparison of petrophysical rock typing and zonation methods of the Valanginian age sandstone in the Pletmos Basin offshore South Africa, to produce a zonation scheme for the low-permeability shaly sandstone reservoirs from core porosity and permeability data. The Valanginian age sediments of the Lower Cretaceous consist predominantly of a shallow marine sequence with interbeds of siltstone, claystone, generally fine-grained sandstone, and glauconitic, with varying amounts of an argillaceous matrix. A core description report was used in conjunction with a gamma-ray log to group rock types into different facies based on texture and grain size. Three different facies were identified as facies 1, a moderately sorted fine to medium-grained glauconitic sandstone; facies 2, a fine to very fine-grained glauconitic sandstone, moderately sorted; facies 3, a very fine sandstone to siltstone, laminated, argillaceous and bioturbated. Three independent reservoir zonation methods (Winland r35 pore throat, Hydraulic Flow Unit, and Stratigraphic Modified Lorenz Plot) were applied to three wells (SW1, SW2, and SW3) for which wireline logs, core porosity, and permeability data are available. Results were analyzed and compared with facies used as a context for the identification of rock types and zones. The results revealed eleven zones, grouped as moderate, very-low, and tight zones. The moderate flow zone is the best reservoir quality rock composed of macroporous rock type, ranked, as good rock type associated with facies 1. Three very-low flow zones were identified, which are of a microporous rock type, ranked as poor quality rock, associated with facies 2. Eight tight zones were revealed, which are of nanoporous rock type ranked as an impervious rock. The tight flow zone is the most reduced rock quality associated with facies 3. This study has developed a zonation scheme that will be used to locate other flow zones as well as to investigate whether the units/zones identified extend to other parts of the field.


2020 ◽  
Vol 10 (8) ◽  
pp. 3157-3177 ◽  
Author(s):  
Sameer Noori Ali Al-Jawad ◽  
Muhammad Abd Ahmed ◽  
Afrah Hassan Saleh

Abstract The reservoir characterization and rock typing is a significant tool in performance and prediction of the reservoirs and understanding reservoir architecture, the present work is reservoir characterization and quality Analysis of Carbonate Rock-Types, Yamama carbonate reservoir within southern Iraq has been chosen. Yamama Formation has been affected by different digenesis processes, which impacted on the reservoir quality, where high positively affected were: dissolution and fractures have been improving porosity and permeability, and destructive affected were cementation and compaction, destroyed the porosity and permeability. Depositional reservoir rock types characterization has been identified depended on thin section analysis, where six main types of microfacies have been recognized were: packstone-grainstone, packstone, wackestone-packstone, wackestone, mudstone-wackestone, and mudstone. By using flow zone indicator, four groups have been defined within Yamama Formation, where the first type (FZI-1) represents the bad quality of the reservoir, the second type (FZI-2) is characterized by the intermediate quality of the reservoir, third type (FZI-3) is characterized by good reservoir quality, and the fourth type (FZI-4) is characterized by good reservoir quality. Six different rock types were identified by using cluster analysis technique, Rock type-1 represents the very good type and characterized by low water Saturation and high porosity, Rock type-2 represents the good rock type and characterized by low water saturation and medium–high porosity, Rock type-3 represents intermediate to good rock type and characterized by low-medium water saturation and medium porosity, Rock type-4 represents the intermediate rock type and characterized by medium water saturation and low–medium porosity, Rock type-5 represents intermediate to bad rock type and characterized by medium–high water saturation and medium–low porosity, and Rock type-6 represents bad rock type and characterized by high water saturation and low porosity. By using Lucia Rock class typing method, three types of rock type classes have been recognized, the first group is Grain-dominated Fabrics—grainstone, which represents a very good rock quality corresponds with (FZI-4) and classified as packstone-grainstone, the second group is Grain-dominated Fabrics—packstone, which corresponds with (FZI-3) and classified as packstone microfacies, the third group is Mud-dominated Fabrics—packstone, packstone, correspond with (FZI-1 and FZI-2) and classified as wackestone, mudstone-wackestone, and mudstone microfacies.


2020 ◽  
Vol 1 (1) ◽  
pp. 48-55
Author(s):  
Lena Maretha Salindeho

The carbonate reservoir is one of the reservoir characters found in hydrocarbon fields in Indonesia. Carbonate reservoirs have complex porosity and permeability relationships. So it is necessary to do a special reservoir character that is different from the siliciclastic reservoir. Efforts that can be made to assist the development of this hydrocarbon field are to analyze the reservoir character in more detail using the petrophysical rock type (PRT) approach. This approach is used by combining geological elements such as the depositional environment, the petrophysical properties of the rock, as well as the fluid flow in it which is reflected by capillary pressure and water saturation. Modeling with this method is expected to be a method that can increase hydrocarbon production optimally in Xena Field. The object of research from Xena Field is Zone A2 which is included in the Parigi Formation. The Parigi Formation is one of the main hydrocarbon-producing reservoirs. The data used in this study are routine core analysis (RCAL) rock data on JLB-07, JLB-08, JLB-02, JLB-23 wells, wire log data (gamma-ray log, resistivity log, density log, neutron log) of 30 wells, and 2D seismic data. The depositional facies are divided into 2 facies, namely the margin reef platform facies and the interior platform facies. Identification of rock type (RT) using the flow zone indicator (FZI) method. The rock type in this field can be divided into 4 rock types, namely RT 1, RT 2, RT 3, RT 4 with RT 1 being able to drain the best fluid and RT 4 to drain the worst fluid. Reservoir property modeling is controlled by facies and rock type (RT) models. The margin reef platform facies are associated with RT 1 and RT 2. The interior platform facies are associated with RT 2 and RT 3.


2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).


Author(s):  
M. Tanveer ◽  
Tarun Gupta ◽  
Miten Shah ◽  

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.


2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Djamel Ouzzane ◽  
Mohamed Sassi ◽  
TieJun Zhang

<p><a></a>Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block. The properties that classify rock types are lithology, permeability, and capillary pressure. Scientists and engineers determined these properties using conventional laboratory measurements, which embedded destructive methods to the sample or altered some of its properties (i.e., wettability, permeability, and porosity) because the measurements process includes sample crushing, fluid flow, or fluid saturation. Lately, Digital Rock Physics (DRT) has emerged to quantify these properties from micro-Computerized Tomography (uCT) and Magnetic Resonance Imaging (MRI) images. However, the literature did not attempt rock typing in a wholly digital context. We propose performing Digital Rock Typing (DRT) by: (1) integrating the latest DRP advances in a novel process that honors digital rock properties determination, while; (2) digitalizing the latest rock typing approaches in carbonate, and (3) introducing a novel carbonate rock typing process that utilizes computer vision capabilities to provide more insight about the heterogeneous carbonate rock texture.<br></p>


2021 ◽  
Author(s):  
Carlos Esteban Alfonso ◽  
Frédérique Fournier ◽  
Victor Alcobia

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.


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