Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

<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>

Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation

<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>

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

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.

Integrated Semi-Supervised Clustering Method and Its Application in Rock-Typing in AA Reservoir

Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation. In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.

Unleashing the Potential of Relative Permeability Using Artificial Intelligence

One of the most important aspects of any dynamic model is relative permeability. To unlock the potential of large relative permeability data bases, the proposed workflow integrates data analysis, machine learning, and artificial intelligence (AI). The workflow allows for the automated generation of a clean database and a digital twin of relative permeability data. The workflow employs artificial intelligence to identify analogue data from nearby fields by extending the rock typing scheme across multiple fields for the same formation. We created a fully integrated and intelligent tool for extracting SCAL data from laboratory reports, then processing and modeling the data using AI and automation. After the endpoints and Corey coefficients have been extracted, the quality of the relative permeability samples is checked using an automated history match and simulation of core flood experiments. An AI model that has been trained is used to identify analogues for various rock types from other fields that produce from the same formations. Finally, based on the output of the AI model, the relative permeabilities are calculated using data from the same and analog fields. The workflow solution offers a solid and well-integrated methodology for creating a clean database for relative permeability. The workflow made it possible to create a digital twin of the relative permeability data using the Corey and LET methods in a systematic manner. The simulation runs were designed so that the pressure measurements are history matched with the adjustment and refinement of the relative permeability curve. The AI workflow enabled us to realize the full potential of the massive database of relative permeability samples from various fields. To ensure utilization in the dynamic model, high, mid, and low cases were created in a robust manner. The workflow solution employs artificial intelligence models to identify rock typing analogues from the same formation across multiple fields. The AI-generated analogues, combined with a robust workflow for quickly QC’ing the relative permeability data, allow for the creation of a fully integrated relative permeability database. The proposed solution is agile and scalable, and it can adapt to any data and be applied to any field.

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

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.

Carbonate Reservoir Characterization with High-Resolution Logging-While-Drilling Dual-Images in Oil-Based Mud

Fracture evaluation and vuggy feature understanding are of prime importance in carbonate reservoirs. Commonly the related features are extracted from high resolution borehole images in water-based mud environments. To reduce the formation damage from drilling fluids, many wells are drilled with oil-based muds (OBM) in carbonate reservoirs. There are no appropriate measurements to resolve the reservoir characterization in OBM with the existing technologies in horizontal wells—especially in real-time—to make decisions at an early stage. In this paper, we would like to introduce a workflow for geological characterization using a new dual-images logging while drilling tool in oil-based mud. This new tool provides high resolution resistivity and ultrasonic images at the same time. Structural features, such as bedding boundaries, faults, fractures can be identified efficiently from resistivity images; while detailed sedimentary features, for example, cross beddings, vugs, stylolite are easily characterized using ultrasonic images. Benefiting from the dual images, an innovative workflow was proposed to estimate the vug feature more accurately; and the fractures can be identified from images and classified based on tool measurement principles. One case study from the Middle East demonstrated the benefits of this new measurement. A near well structure model was constructed from bed boundaries picked from borehole images. The fractures were picked and classified confidently using the dual images. Additionally, fracture density statistics are available along the well trajectory. The vug features were extracted efficiently, which indicates the secondary porosity development information. Rock typing is achieved by combining fracture and vug analysis to provide zonation for completion and production stimulation. The dual-images provide the capability for geological characterization in carbonate reservoir in an oil-based mud environment. The image-based rock typing helps segment the drain-hole for completion and production stimulation. The reservoir mapping with rock typing provides detailed information for in-filling well design.

The Application of Computed Tomography Scanning and Nuclear Magnetic Resonance for Rock Typing of Polymineral Clastic Reservoirs

Summary The main objective of this study was to provide rock typing of the producing formation based on high-resolution computed tomography (CT) scanning and nuclear magnetic resonance (NMR) data in combination with routine core analyses results. The target formation is composed of a shallowing up sequence of clastic rocks. Siltstones in its base are gradually replaced by sandstones toward its top. Initially, only sandstones were considered as oil-bearing, while siltstones were considered as water-bearing based on saturation calculation by means of Archie’s equation (Archie 1942) with the same values of cementation and saturation exponent for the whole formation. However, follow-up well tests detected considerable oil inflow also from the base of the reservoir composed of siltstones. Therefore, better rock typing was needed to improve the initial saturation distribution calculation. An applied approach that was based on integrated analysis of rock microstructural characteristics and derived from the NMR and CT techniques and conventional properties used for reserves calculation appeared to be an effective tool for rock typing polymineral clastic reservoirs. Measuring porous network characteristics and conventional properties in the same core plug enables a confident correlation between all measured parameters. Consequently, rock typing of samples based on flow units’ microstructural characteristics derived from NMR and CT scanning has shown a very good consistency with each other. As a result, four rock types were distinguished within a formation, which were previously interpreted as a single rock type. The detailed rock typing of the reservoir allowed more accurate reserves calculation and involvement of additional intervals into the production. Besides porous media characterization, CT scanning proved to be an effective tool for detecting minerals, such as pyrite and carbonates, characterizing depositional environments. Increasing content of pyrite in siltstones, detected by CT scanning and X-ray fluorescence spectroscopy, indicates deeper and less oxic conditions, while the presence of carbonate shell debris indicates shallower, more oxic depositional settings. The NMR test results show that the NMR signal distribution is affected by both pore size distribution and mineralogical composition. An increase of pyrite content caused shifting of the T2 distribution to the lower values, while carbonate inclusions caused shifting of the T2 distribution to higher values relative to the other samples not affected by these mineral inclusions. Because NMR distribution is affected by multiple factors, applying Т2cutoff values alone for rock typing can lead to ambiguous interpretation. Applying CT scanning next to NMR data increases the reliability of rock typing. The proposed laboratory workflow, including a combination of nonhazardous and nondestructive tests, allowed reliable differentiation of the rock samples based on multiple parameters that were interpreted in relationship with each other. Because the designed laboratory test workflow enabled both justified separation of the samples by rock type and determination of parameters used for reserves calculation, it can be recommended for further application in polymineral clastic reservoirs. Because the proposed techniques are nondestructive, the same samples can be applied for multiple tests including special core analysis (or SCAL).