Special Core Analysis Research Articles

Heterogeneous Reservoir Petrophysical Property and Controlling Factors in Semi-Restricted Depositional Setting: A Case Study of Yamama Formation, X Oilfield, Middle East

The Early Cretaceous Yamama Formation of X oilfield, deposited in a semi-restricted setting, holds considerable oil reserves. However, the reservoir is extremely heterogeneous and is poorly studied. Integrating outcrops, cores, cast thin sections, regular or special core analysis, wireline logging data from six wells, and seismic data, this study provides an improved understanding of reservoir petrophysical characteristics and geological controlling factors including sedimentation, diagenesis, and sequence. The results showed that eight lithologies are developed in the Yamama Formation, of which packstone and wackstone are dominant. The physical properties span a wide range, with porosity mainly distributed between 10% and 25%, and the permeability mainly distributed between 0.1 mD and 1 mD. Nine types of pores are developed, with moldic pores, micropores, and skeletal pores being the most developed. The reservoir has six types of microstructures, of which the poorly sorted with mega-throat represent the best reservoir. The Yamama Formation was mainly deposited in a lagoon, along with five other facies, such as supratidal flat, patchy reef, back shoal, shoal, and open shelf. Six types of diageneses are developed, with dissolution during the penecontemporaneous stage being the most beneficial to the reservoir and cementation being the most destructive. Three sequences were recognized in the Yamama Formation. It concluded that the hydrodynamics in semi-restricted depositional setting is weak overall and does not have the potential to develop large-scale high-quality reservoirs. A wide range of bioclasts were selectively dissolved to form a large number of secondary pores. Sediments rich in Algae, Bacinella, and peloids tend to form moldic pores, skeletal pores, and intergranular pores, respectively, which are prone to be favorable reservoirs. Controlled by the coupling of sedimentation and diagenesis driven by sequence, the reservoir is extremely heterogenous.

Depositional facies controls on reservoir rock typing and heterogeneity: Devonian siliciclastic succession, south central Ghadames basin, Libya

The Devonian sedimentary succession hosts the most potential reservoir targets in Ghadames Basin, Northwest Libya. This succession consists of a complex pattern of siliciclastic facies deposited during successive episodes of sea level fall and rise, thereby leading to a large scale heterogeneity in sandstone facies connectivity and distribution. The present study integrates core sedimentology, wireline logs, and routine and special core analysis to establish a framework for predicting the distribution of the best Devonian reservoir rock types (RRTs). A lowstand amalgamated fluvial sandstone bars only exist in the Lower Devonian Tadrart Formation; the other Devonian rock units consist of transgressive and highstand tidally-influenced estuarine and deltaic facies. The pore system characteristics are controlled by initial depositional attributes where open, megapores with entry displacement pressures <10 psi occur in the quartzose, high energy fluvial sandstone bars, delta distributary channels and mouth bars. These facies are classified as RRTI and are mostly associated with lowstand and late highstand facies. In contrast, the other RRTs display tight pore system with elevated irreducible water content >20%. RRTI rocks account for greater than 90% of the fluid flow capacity in the Devonian reservoirs. A link between the facies depositional setting, RRT distribution, vertical amalgamation and lateral compartmentalization of flow conduits, barriers and baffles zones has been proposed, thereby promoting the predictability of the best reservoir targets.

An NMR-based model for determining irreducible water saturation in carbonate gas reservoirs

Unambiguously determining irreducible water saturation Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({S}_{\\rm{wirr}}\\right)$$\\end{document} poses a formidable challenge, given the availability of multiple independent methods. Traditional approaches often depend on semi-experimental relationships derived from simplified assumptions. These methods, originally designed for oil sandstone reservoirs, result in varying Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} values when employed in carbonate gas reservoirs. Nuclear magnetic resonance (NMR) is the most advanced technique for determining Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document}. While highly accurate, the NMR-based method necessitates the laboratory measurement of the transverse relaxation time T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({T}_{2}\\right)$$\\end{document} cutoff. Laboratory-based T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{2}$$\\end{document} cutoff determination is resource-intensive and time-consuming. This research aims to develop a robust model for determining Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} in carbonate gas reservoirs by utilizing NMR well logging measurements and special core analysis (SCAL) tests. Various T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{2}$$\\end{document} cutoff values were initially employed to compute bound water saturation Sbw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({S}_{{\ ext{bw}}}\\right)$$\\end{document} at different depths to achieve this. Subsequently, the data points T2,Sbw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left({T}_{2}, {S}_{{\ ext{bw}}}\\right)$$\\end{document} were graphed on a scatter plot to unveil the relationship between Sbw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{bw}}}$$\\end{document} and T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{2}$$\\end{document}. The scatter plot illustrates an exponential decrease in Sbw\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{bw}$$\\end{document} with increasing T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{2}$$\\end{document}, forming the basis for the Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} model derived from this relationship. Finally, the parameters of the Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} model were fine-tuned using SCAL tests. Notably, this Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} model not only accurately yields Swirr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${S}_{{\ ext{wirr}}}$$\\end{document} at each depth but also offers a dependable determination of the optimal T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{2}$$\\end{document} cutoff for the reservoir interval.

Integrated Formation Evaluation for Site-Specific Evaluation, Optimization, and Permitting of Carbon Storage Projects

Participation in over 80 carbon capture and sequestration (CCS) projects spanning 25 years has led to the evolution of a recommended well-based appraisal workflow for CO2 sequestration in saline aquifers. Interpretation methods are expressly adapted for CCS applications to resolve key reservoir parameters, constrain field-scale modeling, provide answers required for the permitting process, and de-risk unique CCS evaluation challenges, such as Storage capacity Injectivity Containment. A challenge complicating all of the above is the eventual impact of three-way interaction among rock matrix, brine, and (impure) CO2 streams. Most logging, sampling, and laboratory techniques are adapted from established domains such as enhanced oil recovery, underground gas storage, and unconventional reservoir evaluation, though some CCS-specific innovation is also needed. Storage evaluation begins with established methods for lithology, porosity, permeability, and pressure, while special core analysis (SCAL) determines CO2 storage efficiency and relative permeability. Containment evaluation spans multiple disciplines and methods: the petrophysicist’s task to quantify seal capacity relies heavily on laboratory analysis, while geologists leverage downhole imaging tools to verify caprock structural/tectonic integrity. Geomechanics engineers define safe injection pressure via mechanical earth models (MEMs) built on advanced acoustic logs calibrated by core geomechanics, wellbore failure observations, and in-situ stress tests. The impact of rock-brine-CO2 interactions is studied via custom SCAL experiments and/or pore-scale digital rock simulations that rigorously represent chemical and thermal processes. Wireline formation tester samples provide representative formation brine as feedstock for SCAL. Water samples also enable operators to prove injection within regulatory limits while establishing baselines for future monitoring programs. Examples applied to recent CCS projects in North America are presented. All of the above data need to be integrated into a CCS model predicting the CO2 plume behavior across the area of interest and within multiple horizons.

Artificial Intelligence Unleashes Potential of Relative Permeability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207855, “Unleashing the Potential of Relative Permeability Using Artificial Intelligence,” by Abdur Rahman Shah, SPE, Schlumberger; Kassem Ghorayeb, SPE, American University of Beirut; and Hussein Mustapha, Schlumberger, et al. The paper has not been peer reviewed. _ To unlock the potential of large relative permeability (Kr) databases, the work flow proposed in the complete paper integrates data analysis, machine learning (ML), and artificial intelligence (AI). The work flow allows for the automated generation of a clean database and a digital twin of Kr data, using AI to identify analog data from nearby fields by extending the rock-typing scheme across multiple fields for the same formation. Introduction Accurate Kr curves are critical because they can improve reservoir characterization, reduce uncertainty in history matching and production forecasting, and provide robust and reliable field development plans. However, preparing special core analysis (SCAL) data, particularly Kr curves, as an input for reservoir simulation traditionally has been a highly manual, time-consuming, labor-intensive process. Furthermore, no automated tools are available currently that perform a full quality review of Kr data using analytical or numerical methods. As a result, the quality and representativeness of Kr data used in reservoir simulation models is frequently inadequately checked. Furthermore, no tools exist to generate Kr curves based on analog field data in fields where Kr data is either missing or scarce. Integrated Work Flow The solution discussed in this paper aims to address challenges and automate processes using a combination of conventional algorithms and ML. It begins with a thorough quality check and cleanup of Kr laboratory data before using analog data to build Kr curves for a specific field or reservoir. The latter is especially important considering the challenges that arise when attempting to build on analog field data in the presence of large, corporate, regional, or country-scale databases. The approach classifies rocks using ML-proxy rock typing, which is subsequently used to find Kr equivalents. After that, these analogs can be used to fill up any data gaps for reservoir simulation in any discipline.

Wireless Acquisition for Resistivity Index in Centrifuge – WiRI: A Comparative Study of Three Pc-RI Methods

Technological improvements and innovations are made to offer solutions with superior efficiency in terms of cost, quality, speed, or all of them. In the special core analysis (SCAL) field, the conventional resistivity index measurement (the porous plate technique) is a cost-effective method that provides good-quality results but is very time consuming. For this purpose, several methods were developed to reduce the time taken to acquire resistivity measurements. In 2017, we proposed the ultra-fast capillary pressure and resistivity index measurements (UFPCRI) combining centrifugation, nuclear magnetic resonance (NMR) imaging, and resistivity profiling. Since 2021, the wireless resistivity index (WiRI) method allows the acquisition of capillary pressure and resistivity index in a matter of days. This method is based on a new in-house system to acquire wirelessly resistivity indexes along a rock sample during centrifugation. The determination of the resistivity vs. saturation curve and the n exponent of Archie’s law is done thanks to an optimization algorithm. In this paper, we present the results obtained from multiple simulations and experiments for WiRI, UFPCRI, and porous plate to discuss the advantages and drawbacks of each method in terms of reliability and experimental duration. Six rock samples are studied. A comparison of the three methods regarding Archie’s n exponent, resistivity indexes, and capillary pressure curves is performed.

Seismic reservoir characterization of the syn-rift lower Miocene Rudeis Formation in the July oilfield, Gulf of Suez basin, Egypt: Implication for reservoir quality assessment

The Lower Miocene Rudeis Formation is among the most potential oil-bearing sedimentary sequences in the Gulf of Suez (GOS), representing the main producer of Egyptian oil. This study concerns primarily with revealing the reservoir characteristics and facies distribution, as well as the implication of the predominant diagenetic factors on the quality of the Lower Miocene syn-rift Rudeis reservoir in the July oilfield. Therefore, this study integrates 2D seismic, and wireline logs, as well as conventional and special core analyses to determine the reservoir characteristics and facies distribution. The interpreted data elucidate four different seismic facies in the Rudeis Formation with a net pay thickness ranging from 29 to 233 m in the studied wells of 16.3% average porosity, 62.9% average oil saturation, and 9.3% average shale volume. Four seismic facies (SF1-SF4) were interpreted and correlated with the Rudeis clastic deep-water turbiditic succession. The 3D static property models revealed that the best reservoir facies in terms of low shale content and water saturation, as well as high porosity, coincide with the submarine channels and fans containing coarse-grained, clean sandstones. From the core analysis, the Lower part of the Rudeis sequence is subdivided into five reservoir rock types (RRTs). The RRT1 is characterized by the best reservoir quality; it is composed of quartz arenites, sublithic arenites, and subfeldspathic arenites. In contrast, the RRT5 represents the lowest reservoir quality; it consists of very coarse siltstone, laminated glauconitic siltstone, and highly compacted dolomitic and calcareous quartz arenites. The poor reservoir characteristics of the RRT5 are attributed to tight compaction, cementation, and early dolomitization phase, whereas the highest quality of the RRT1 is related to fracturing and dissolution processes. The RRT1 rocks are mostly associated with the submarine channel and fan facies, whereas the poor quality RRT5 sediments were accumulated as hemipelagic/background. This integrated workflow study can be applied to further case studies in the Middle East, Africa, and the other analogues all over the world.

Effect of Heterogeneity on Capillary Pressure and Relative Permeability Curves in Carbonate Reservoirs. A Case Study for Mishrif Formation in West Qurna/1 Oilfield, Iraq

The special core analysis tests were accomplished on a set of core plugs for Mishrif Formation (mA, mB1, and mB2cde/mC units) in West Qurna/1 oilfield, southern Iraq. Oil relative permeability (Kro) data and the Corey-type fit of the data as functions of the brine saturation at the core outlet face for individual samples in the water-oil imbibition process to estimate relative permeability measurements by the centrifuge method were utilized. Identical correlations for oil and water relative permeabilities were extracted by steady-state and unsteady-state methods. For the mA samples, the gas-water capillary pressure curves were within a narrow range (almost identical) indicating that mA is a homogeneous unit. Kro curves for three mB2ab plugs were practically identical, that is referring to the homogeneity in the upper portion of the unit. The mB2 unit has a more solid‐phase concentration than other units. In addition, the general trend of low residual oil saturation is related to the raising in porosity but no reliable correlation between the residual oil saturation to water drive (Sorw) and Klinkenberg-corrected permeability (Kinf). Based on the correlation between the effective oil permeability at the initial water saturation [ko(Swi)] and (Kinf/f)1/2 for the high-permeability lithofacies mB1 plugs, ko(Swi) is approximately equal to or exceeds Kinf. While ko(Swi) was below Kinf for the other samples. New good empirical equations were obtained for effective gas permeability at final water saturation versus Kinf, as well as, for Kro and Krw versus saturation for all lithofacies.

PRELIMINARY CORE PREPARATION FOR SPECIAL CORE ANALYSIS-GUIDELINES

The application of core analysis to provide data for reservoir evaluation is not a new science but has seen continued research activity over along period. Core analysis is complex containing many stages between the reservoir and the final measurements and interpretation. To ensure the data produced is valid, every step along this phase must be handled with equal attention. Recently developed techniques of measuring certain special core analysis of rock formation are being perfected. The objective of this paper is to provide information that will be of practical value to geologists and engineers involved with preliminary core preparation program. Following is a guideline for preliminary special core preparation. The step by step outline presented below is a suggested guideline and, most certainly, modifications and or changes may be made. However, this guideline has been found to be very successful for laboratory works.

Towards Multiscale Digital Rocks: Application of a Sub-Resolution Production Model to a multiscale Sandstone

Many digital rock methodologies use a direct simulation approach, where only resolved pores are accounted for. This approach limits the types of rocks that can be analyzed, excluding some types of carbonates, unconventionals, and complex sandstones from the digital rock analysis. This is due to the challenge for single scale imaging to capture the full range of relevant pore sizes present in multiscale rocks. In this paper, a physical model is presented, within the context of an established direct simulation approach, to predict the production of hydrocarbons including the contribution of sub-resolution pores. The direct simulation component of the model employs a multiphase lattice Boltzmann method to simulate multiphase fluid flow displacement in resolved pores. In the production model, the amount of hydrocarbons present in the sub-resolution pores is identified and a physical description of the production behavior is provided. This allows a relative permeability curve to be predicted for rocks where mobile hydrocarbons are present in pores smaller than the image resolution. This simplified model for the oil movement in the unresolved pore space is based on a physical interpretation of different regions marked by simulation resolution limits in a USBM wettability test curve. The proposed methodology is applied to high-resolution microCT images of a sandstone that contains pores at multiple scales, some resolved and some not resolved. To allow for benchmarking, experimental routine and special core analysis data was also obtained. Good agreement to experimental results is observed, specifically in absolute and relative permeability. The presented multiscale model has the potential to extend the classes of reservoir rocks eligible for digital rock analysis and paves the way for further advancements in the modelling of multiscale rocks, particularly unconventionals and carbonates.

Toward low-salinity waterflooding predictive capability development in carbonates for fast screening of oil-brine-rock candidates

Despite numerous studies on the low-salinity waterflooding (LSWF) technique, a robust predictive capability has not yet been established. Predictive capability development is essential to reduce costs and time during the early screening or prioritization of candidate fields for LSWF in the opportunity realization process (ORP). This study aims at proposing a set of screening methods that offer the best LSWF predictive capability for various crude oil-brine-carbonate rock systems. In this regard, the robustness of different methods for predicting oil recovery factor (RF) from core-scale spontaneous imbibition (SI) experiments in carbonate rocks was investigated here. The relevant small-scale screening methods were divided into two distinct categories. The first category includes methods based on rock/fluid interactions: wettability assessment based on flotation and contact angle measurement, and geochemical assessment to identify mineral dissolution (and pH change). The second group includes methods based on probing fluid/fluid interactions: oil-brine interfacial tension (IFT) and viscoelasticity, and micro-dispersion formation. The most common types of carbonate rocks, including dolomite, limestone, and chalk, were used to comprehensively evaluate the response of each carbonate rock type to brines of different salinities. Each rock type could be distinguished with its particular microscopic texture, pore-size, grain type, and carbonate mineral structure. According to the SI results, oil recovery strongly depends not only on brine salinity but also on the rock type. The oil RF for Indiana limestone was found to be higher than that for other rock types but showed less sensitivity to the degree of dilution. Additionally, RF from dolomite and chalk plugs showed the most and the least sensitivity to the degree of brine dilution, respectively. We introduced a brine efficiency index based on the SI results to compare the results for different rock types and salinities. The results of the systematic tests performed here showed that variation of IFT and viscoelasticity (expressed as phase angle) with brine salinity reduction was neither significant nor monotonic, and did not correlate with the brine efficiency index. Also, minor/negligible mineral dissolution was observed upon brine-rock contact. On the other hand, wettability results based on contact angle measurement and flotation, as well as microdispersion results strongly correlated with the brine efficiency index over a wide range of brine salinities, which indicates these techniques can quickly predict the potency of a system toward low salinity effect (LSE). It is hypothesized, therefore, that the techniques which involve rock explicitly would be more robust because they capture the rock-type dependency of LSE too. The findings from this study can be used for quick screening of the candidate formations for LSWF, before performing the costly and time-intensive special core analysis (SCAL) experiments. The data provided by this research can also enrich the SCAL data bank toward developing robust data-driven predictive models.

Machine Learning Brings Vast Core-Analysis Legacy Data to Life

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31419, “Bringing Huge Core-Analysis Legacy Data to Life Using Machine Learning,” by Siti N.F. Zulkipli, SPE, Benard Ralphie, and Jamari M. Shah, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2022 Offshore Technology Conference. Reproduced by permission. _ Among the sources of subsurface data, rock and fluid analyses stand out as the best means of directly measuring subsurface properties. The implication of modeling this data into an organized data store means better assessment of economic viability and producibility in frontier basins and the capability to identify bypassed pay in old wells that may not have rock material. The complete paper presents agile technologies that integrate data management, data-quality assessment, and predictive machine learning (ML) to maximize company asset value with legacy core data. Introduction The complete paper integrates data gathering, data filtering, the connecting of scattered data, and the building of useful knowledge models using legacy core data from various operating assets. This integration is achieved by a data quality check (QC) work flow and ML to improve the definition of reservoir rock properties that affect field development and asset management. ML and deep-learning capabilities have been implemented to predict critical reservoir properties based on qualified legacy data. The work flow is aimed at bridging gaps in areas with limited core data coverage, reducing subsurface uncertainty and risk, and accelerating project delivery. In addition, data-prediction accuracy can be improved with ML applications as new data become available. Phase I: Data Preparation and Verification In this stage, more than 60,000 data points are gathered from 327 wells and grouped into different categories such as routine core analysis, special core analysis, geology, and rock mechanics. Leveraging on best practices in laboratory core analysis and subject-matter expert (SME) expertise, 88 QC rules are generated to delineate the data, identify outliers, and classify the data into good quality (labeled as 0), data requiring further QC (1), and rejected data (2). The generated QC rules target various rock properties such as porosity, permeability, capillary pressure, electrical properties, relative permeability, and rock mechanics. QC-rule implementation is an iterative process in which, after the initial screening, the rules are further optimized and enhanced to assist data classification based on practical experiences. These QC rules are needed because some observed data errors are related to data tabulation and incorrect naming. A total of 1,826 relevant variables, generated by different vendors, was extracted during QC-rule creation. These variables had to be merged in the QC process, which reduced the number of variables to 240.

Integrated Reservoir Characterization Using Unsupervised Learning on Nuclear Magnetic Resonance (NMR) T1-T2 Logs

A novel interpretation workflow was developed using an automated unsupervised learning algorithm on nuclear magnetic resonance (NMR) T1-T2 log data to quantify fluid-filled porosity and saturation, producible oil volumes, and to characterize matrix pore sizes and formation wettability. Core porosity and saturation measurements, scanning electron microscope images (SEM), Rock-Eval pyrolysis, wettability measurements, and mercury injection capillary pressure (MICP) tests are compared with the NMR interpretation for calibration and validation. Understanding in-situ fluid types and volumetrics is key for reservoir characterization. The traditional static formation evaluation model based on triple-combo logs (density, neutron, resistivity, and gamma ray) has been widely used to characterize formations to provide cost-effective answers of lithology, total porosity, and water saturation. Nevertheless, the dynamic result from production often shows quite a different water cut than total water saturation because the static model cannot distinguish immobile hydrocarbons from producible oil. NMR log data show unique signatures of formation fluids, such as gas, immobile hydrocarbon, producible oil, T1-T2 immobile, and free water. The NMR data also provide a method to interpret fluid and matrix properties, including fluid viscosity, pore geometry, and fluid-pore interaction. However, due to the downhole environment and the resolution limitation of the logging tool, the signatures of the fluids are not always well separated. It is challenging to visually separate the signal contributions of different formation fluids on T1-T2 maps. An automated unsupervised learning algorithm based on non-negative matrix factorization (NMF) and hierarchical clustering (Venkataramanan et al., 2018) is implemented in the new workflow to separate T1-T2 signatures of different pore fluids, enabling fluid typing and providing quantitative fluid-filled porosities and associated saturations. T1-T2 signatures of separated fluids are used to characterize fluid mobility, pore sizes, and formation wettability. The new approach is successfully applied to multiple wells for a field case study to characterize the saturation and producibility of hydrocarbon and water, which routine petrophysical models are unable to distinguish. Results are corroborated with dynamic production data showing high free water and high residual oil. This is also validated by routine and special core analyses. Integration of NMR, MICP, and SEM gives pore-body and pore-throat-size distributions with body-to-throat ratio (BTR), increasing the precision of estimated formation permeability. A high T1/T2 ratio of the oil suggests that the formation is partially oil-wet. The wettability results from NMR are consistent with the core wettability test and production results. Understanding which portion of a reservoir contains mobile fluids impacts target zone selection and reserves estimation.

Comprehensive approach to carbonate rocks typing based on core laboratory experiments data

The paper presents results of the comprehensive approach to identification of complicated carbonate rocks types. The subject of research is the Mishrif formation core from one of the Mesopotamian oil-and-gas province field wells. At the first stage the 3 lithological petrophysical rocks types were identified based on core nuclear magnetic resonance experiments taking into account structural features, which are determined with description data of thin sections. At the second stage the 5 petrophysical rocks types were identified based on routine and special core analyses taking into account secondary changes influence, which is determined by thin sections description data. The identified petrophysical rocks types were described in detail, linear discriminant functions were deduced. These functions allow predicting the identified petrophysical types with the suggested parameters. Comparison of the lithological petrophysical and petrophysical types has shown that the pore space structure of the studied deposits depended on two factors: the first is structural features and the second is postdepositional alterations.

Identification and characterization of microfractures in carbonate samples

Based on the rock typing method of pore geometry and structure (PGS), rock samples from carbonate reservoir A and carbonate reservoir B were classified using data of routine and special core analysis and thin section images, and microfractures in the carbonate reservoir samples were identified and characterized. Establishment of rock types demonstrates that microfractures have developed in all rock types in carbonate reservoir A, but only partially in certain rock types in carbonate reservoir B with porosity of 1%–11%, less vuggy, and hardness of medium hard to hard. The cut-off porosity was determined for each type of rock to distinguish samples with and without conductive microfractures. The impact of conductive microfractures on improving permeability was analyzed. On the basis of relationship of permeability and original initial water saturation, the permeability equation was derived by certain special core analysis data with conductive microfractures selected by PGS equation, and the permeability of samples with conductive microfractures has been successfully predicted.

PRELIMINARY CORE PREPARATION FOR SPECIAL CORE ANALYSIS-GUIDELINES

The application of core analysis to provide data for reservoir evaluation is not a new science but has seen continued research activity over along period. Core analysis is complex containing many stages between the reservoir and the final measurements and interpretation. To ensure the data produced is valid, every step along this phase must be handled with equal attention. Recently developed techniques of measuring certain special core analysis of rock formation are being perfected. The objective of this paper is to provide information that will be of practical value to geologists and engineers involved with preliminary core preparation program. Following is a guideline for preliminary special core preparation. The step by step outline presented below is a suggested guideline and, most certainly, modifications and or changes may be made. However, this guideline has been found to be very successful for laboratory works.

AN EXPERIMENTAL ANALYSIS IN EVALUATING ORDERING CRITERIA FOR COMPOSITE CORE ARRANGEMENT IN SPECIAL CORE ANALYSIS

Composite cores of core plugs from different heights of an exploration well core are often used for routine and special core analysis to evaluate an oil reservoir. The question is whether or not the order of the core plugs in the composite core make a difference to the absolute and relative permeability measurements? The seminal work by Huppler (1969), proposed ordering individual core samples harmonically in a composite core in order to match the overall permeability. Langaas (1998) proposed ordering the core samples in decreasing permeability for effective relative permeability measurements, based on theoretical framework of North Sea sandstones. In this paper we tested the methods proposed by Huppler and Langaas experimentally. Core samples with permeability ranging from 50 mD to 2 Darcy were used for this work. The orientation of the core samples was tested for permeability and relative permeability and compared with the theoretical model developed by Langaas. In addition to the experimental work, simulations were performed with the three different composite cores and the representative recovery factor was compared to provide the appropriate composite core arrangement.

RESERVOIR QUALITY REDUCTION CAUSED BY CLAY INDUCED DUCTILITY

Ductile components including rock fragments, clay, and matrix material are often consideredas the most important factors that control quality of shaly sandstones, which are usually characterized by low porosity and permeability. In presence of ductile components, the degree of quality reduction is affected mainly by distribution, occurences, and amounts of the ductile components. Therefore, the presence of ductile components affects rock petrophysics, and consequently has effect on fluid movements in reservoir during production. This paper presents results of the study on plug samples taken from five wells from various fields in Cooper Basin (Tirrawarra Sand), South Australia. The samples were studied petrographically using x-ray diffraction, optical petrography, and scanning electrone microscope. The results were integrated with routine and special core analysis data. The overall results show that the distribution and the amount of ductile components have significantly reduced porosity, hence the quality of the reservoir. However, the effect is different for different types of porosity, and this certainly is valuable addition to our understanding over permeability behavior of shaly sandstones.

Displacement Efficiency in Tight Sandstone Based on Fractional Flow Curve Using Relative Permeability Data

In tight gas sandstone, relative permeability is an essential special core analysis dynamic test that can be used to estimate injectivity, secondary recovery, production rate, reservoir simulation, residual gas saturation, and effective water management. Having about 65% of hydraulic fracturing fluid not to flow back and stay in the reservoir results in having the tight sandstone gas reservoir to involve multi-phase flow, namely water and gas. During the hydraulic fracturing job both imbibition and forcibly imbibition processes take place while during fracturing fluid cleanup and gas production drainage flow becomes dominant.&#x0D; The steady state flooding process was used to measure the relative permeability curves for tight sandstone core sample collected from Travis Peak Formation at a depth of 8707 ft. The measurement process involved the performance of a series of steady state experiments with different gas-water injection ratio. The fractional flow curve has been plotted, based on the measured relative permeability, and used to calculate the displacement efficiency for the flow through such type of tight porous media. The measurement showed relatively high irreducible water saturation (31%) and low residual gas saturation (6%). The measured gas relative permeability decreased slowly at a constant rate with increased wetting fluid saturation. The obtained fractional flow curve does not follow the s-shape behavior observed in conventional reservoir. The obtained results showed that displacement efficiency can be enhanced by increasing water viscosity.

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).