Integrating Rock Typing Methods Including Empirical, Deterministic, Statistical, Probabilistic, Predictive Techniques and New Applications for Practical Reservoir Characterization

2021 ◽  
Author(s):  
Gary William William Gunter ◽  
Mohamed Yacine Yacine Sahar ◽  
David F. Allen ◽  
Eduardo Jose Viro ◽  
Shahin Negabahn ◽  
...  

Abstract This paper discusses integrating common methods and applications for "Rock Typing" (also known as Petrophysical Rock Typing-PRT) including empirical, deterministic, statistical, probalistic and automatic/predictive approaches. Many industry asset teams apply one or more of these methods when creating static reservoir models, using dynamic reservoir simulations, completing petrophysical studies for saturation height models and determining reservoir volumetrics as part of reservoir characterization studies. Our intention is to provide guidance and important information on how and when to use the various methods, so people can make an informed selection. This discussion is important as many disciplines apply these PRT techniques without understanding the pros, cons and limitations of the different methods. An important tool is comparing PRT results from multiple methods. The topics and workflows that are covered focus on various PRT techniques and workflows. We will use case-studies to illustrate the key features and make important comparisons. Key results include comparing pros and cons, how to use and combine multiple PRT techniques and verify results. This paper includes these techniques and workflows;MICP, core analysis and pore throat calibration.Core-Log Integration focused on PRT analysis.Winland, Pittman, Aguilera and Hartmann et.al Gameboard methods.K-Phi ratio, Flow Zone Indicators and Rock Quality Index methods.Classic, Modified and Stratigraphic Lorenz methods.IPSOM and HRA Probabilistic methods.Case Study – Super Plot and Advanced Automatic PRT Method.Special Topics – Carbonate Methods, NMR and Single Well Vertical Line. Practical approaches based on case studies show how PRT analysis can be applied in mature fields to identify by-passed hydrocarbon zones and zones that have a high probability of producing water using open hole, cased hole and production logs. Traditional Rock Typing (PRT) analysis can be applied as a single well technique or as a multi-well method so operations teams can identify additional business opportunities (remedial workovers, infill drilling locations or exploitation targets) and compare reservoir performance with intrinsic rock properties. New applications and additional topics cover single, multiple well approaches and new emerging PRT techniques (including NMR well logs and machine learning). We recommend how to merge classic facies with PRT analysis for 3-D applications including populating a 3D volume.

2011 ◽  
Author(s):  
Sachin Kumar Sharma ◽  
Alexis Vincent Carrillat ◽  
Torsten Friedel
Keyword(s):  

2014 ◽  
Author(s):  
John Bonar ◽  
D.J. Snyder ◽  
Brittany Dale Miller ◽  
Lonnie Jeffers

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):  
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.


2021 ◽  
Author(s):  
Abdul Bari ◽  
Mohammad Rasheed Khan ◽  
M. Sohaib Tanveer ◽  
Muhammad Hammad ◽  
Asad Mumtaz Adhami ◽  
...  

Abstract In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.


2021 ◽  
Author(s):  
Alexander Kolomytsev ◽  
◽  
Yulia Pronyaeva Pronyaeva ◽  

Most conventional log interpretation technics use the radial model, which was developed for vertical wells and work well in them. But applying this model to horizontal wells can result in false conclusions. The reasons for this are property changes in vertical direction and different depth of investigation (DOI) of logging tools. DOI area probably can include a response from different layers with different properties. All of this complicates petrophysical modeling. The 3D approach for high angle well evaluation (HAWE) is forward modeling in 3D. For this modeling, it is necessary to identify the geological concept near the horizontal well section using multiscale data. The accuracy of modeling depends on the details of the accepted geological model based on the data of borehole images, logs, geosteering inversion, and seismic data. 3D modeling can be applied to improve the accuracy of reservoir characterization, well placement, and completion. The radial model is often useless for HAWE because LWD tools have different DOI and the invasion zone was not formed. But the difference between volumetric and azimuthal measurements is important for comprehensive interpretation because various formations have different properties in vertical directions. Resistivity tools have the biggest DOI. It is important to understand and be able to determine the reason for changes in log response: a change in the properties of the current layer or approaching the layers with other properties. For this, it is necessary to know the distance to the boundaries of formations with various properties and, therefore, to understand the geological structure of the discovered deposits, and such information on the scale of well logs can be obtained either by modeling or by using extra deep resistivity inversion (mapping). The largest amount of multidisciplinary information is needed for modeling purposes - from images and logs to mapping and seismic data. Case studies include successful examples from Western Siberia clastic formations. In frame of the cases, different tasks have been solved: developed geological concept, updated petrophysical properties for STOIIP and completion, and provided solutions during geosteering. Multiscale modeling, which includes seismic, geosteering mapping data, LWD, and imagers, has been used for all cases.


2021 ◽  
Author(s):  
Fadzlin Hasani Kasim ◽  
Budi Priyatna Kantaatmadja ◽  
Wan Nur Wan M Zainudin ◽  
Amita Ali ◽  
Hasnol Hady Ismail ◽  
...  

Abstract Predicting the spatial distribution of rock properties is the key to a successful reservoir evaluation for hydrocarbon potential. However, a reservoir with a complex environmental setting (e.g. shallow marine) becomes more challenging to be characterized due to variations of clay, grain size, compaction, cementation, and other diagenetic effects. The assumption of increasing permeability value with an increase of porosity may not be always the case in such an environment. This study aims to investigate factors controlling the porosity and permeability relationships at Lower J Reservoir of J20, J25, and J30, Malay Basin. Porosity permeability values from routine core analysis were plotted accordingly in four different sets which are: lithofacies based, stratigraphic members based, quartz volume-based, and grain-sized based, to investigate the trend in relating porosity and permeability distribution. Based on petrographical studies, the effect of grain sorting, mineral type, and diagenetic event on reservoir properties was investigated and characterized. The clay type and its morphology were analyzed using X-ray Diffractometer (XRD) and Spectral electron microscopy. Results from porosity and permeability cross-plot show that lithofacies type play a significant control on reservoir quality. It shows that most of the S1 and S2 located at top of the plot while lower grade lithofacies of S41, S42, and S43 distributed at the middle and lower zone of the plot. However, there are certain points of best and lower quality lithofacies not located in the theoretical area. The detailed analysis of petrographic studies shows that the diagenetic effect of cementation and clay coating destroys porosity while mineral dissolution improved porosity. A porosity permeability plot based on stratigraphic members showed that J20 points located at the top indicating less compaction effect to reservoir properties. J25 and J30 points were observed randomly distributed located at the middle and bottom zone suggesting that compaction has less effect on both J25 and J30 sands. Lithofacies description that was done by visual analysis through cores only may not correlate-able with rock properties. This is possibly due to the diagenetic effect which controls porosity and permeability cannot visually be seen at the core. By incorporating petrographical analysis results, the relationship between porosity, permeability, and lithofacies can be further improved for better reservoir characterization. The study might change the conventional concept that lower quality lithofacies does not have economic hydrocarbon potential and unlock more hydrocarbon-bearing reserves especially in these types of environmental settings.


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