bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Approach for Building Composite Cores for Corefloods in Complex Carbonate Rocks
Authors: Cinar Yildiray , Zayer Ahmed , Dawood Naseem , Krinis Dimitris 
Published: December 2021

Abstract
Carbonate reservoir rocks are composed of complex pore structures and networks, forming a wide range of sedimentary facies. Considering this complexity, we present a novel approach for a better selection of coreflood composites. In this approach, reservoir plugs undergo a thorough filtration process by completing several lab tests before they get classified into reservoir rock types. Those tests include conventional core analysis (CCA), liquid permeability, plug computed tomography (CT), nuclear magnetic resonance (NMR), end-trim mercury injection capillary pressure (MICP), X-ray diffraction (XRD), thin-section analysis (TS), scanning electron microscopy (SEM), and drainage capillary pressure (Pc). We recommend starting with a large pool of plugs and narrowing down the selection as they complete different stages of the screening process. The CT scans help to exclude plugs exhibiting composite-like behavior or containing vugs and fractures that potentially influence coreflood results. After that, the plugs are categorized into separate groups representing the available reservoir rock types. Then, we look into each rock type and determine whether the selected plugs share similar pore-structures, rock texture, and mineral content. The end-trim MICP is usually helpful in clustering plugs having similar pore-throat size distributions. Nevertheless, it also poses a challenge because it may not represent the whole plug, especially for heterogeneous carbonates. In such a case, we recommend harnessing the NMR capabilities to verify the pore-size distribution. After pore-size distribution verification, plugs are further screened for textural and mineral similarity using the petrographic data (XRD, TS, and SEM). Finally, we evaluate the similarity of brine permeability (Kb), irreducible water saturation (Swir) from Pc, and effective oil permeability data at Swir (Koe, after wettability restoration for unpreserved plugs) before finalizing the composite selection.
The paper demonstrates significant aspects of applying the proposed approach to carbonate reservoir rock samples. It integrates geology, petrophysics, and reservoir engineering elements when deciding the best possible composite for coreflood experiments. By practicing this workflow, we also observe considerable differences in rock types depending on the data source, suggesting that careful use of end-trim data for carbonates is advisable compared to more representative full-plug MICP and NMR test results. In addition, we generally observe that Kb and Koe are usually lower than the Klinkenberg permeability with a varying degree that is plug-specific, highlighting the benefit of incorporating these measurements as additional criteria in coreflood composite selection for carbonate reservoirs.
Accurate Carbonate Pore System Characterization by Nuclear Magnetic Resonance and Micro-CT Techniques
Authors: Gao Jun , Kwak Hyung T. , AlSinan Marwah 
Published: December 2021

Abstract
Carbonate reservoir rocks usually have complex pore systems of broad size distributions, which determine many aspects of oil exploitation, from petrophysical properties to oil/water displacements. An accurate and complete description of these pore systems remains a challenge. A single technique often gives one measurement of complicated microscopic pore space. The new techniques (i.e., micro-CT and NMR) are utilized together with conventional methods (e.g., MICP, BET) to capture a more accurate and complete picture of pore structures.
MICP measures the pore throat while the NMR T2 mainly measures the pore body. Micro-CT provides a 3D image of a limited sample size. Recently, NMR DDIF (decay due to diffusion in the internal field) for direct pore body size is extended from high to low magnetic field, which overcomes many limitations in pore system characterization. This study obtains pore throat size distributions from in-situ centrifuge capillary pressure and pore body size distributions from low field DDIF measurement and verifies them with micro-CT and BET/T2 in different types of carbonate rocks.
The pore throat size distribution of the conventional sample is obtained from in-situ centrifuge capillary pressure. The major features of both macro and micro pore throat size distributions are captured. Pore size distributions are directly obtained from glass beads and carbonate rocks without calibration. Combined analysis of the pore size distribution from two methods reveals the underlying causes of their different petrophysical properties. The pore throat size distribution from in-situ centrifuge capillary pressure and pore size distribution from NMR DDIF can be employed to obtain a better understanding of conventional carbonate pore systems.
Artificial Intelligence and Data Analytics for Virtual Flow Metering
Authors: Gryzlov Anton , Mironova Liliya , Safonov Sergey , Arsalan Muhammad 
Published: December 2021

Abstract
Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain.
Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm.
In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.
Thermal Effect on Formation Stability Due to Heterogeneity
Authors: Batarseh Sameeh , San Roman Alerigi Damian , Al Harith Abdullah , Assiri Wisam 
Published: December 2021

Abstract
This study evaluates physical and chemical changes induced by high thermal gradients on the formation and their impact to the stability. The heat sources that effect the formation’s stability are varied, including drilling (due to drilling bit friction), perforation, electromagnetic heating (laser or microwave), and thermal recovery or stimulation (steam, resistive heating, combustion, microwave, etc.). This study uses an integrated approach to characterize rock heterogeneity and mapping heat propagation from different heat sources. The information obtained from the study is vital to accurately design and enhance well completion and stimulation
This is an integrated analysis approach combining different advanced characterization and visualization techniques to map heat propagation in the formation. Advanced statistical analysis is also used to determine the key parameters and build fundamental prediction algorithms. Characterization on the samples was performed before, during, and after the exposure to thermal sources; it comprised thin-section, high speed infrared thermography (IR), differential thermal analysis and thermogravimetric analyzer (DTA/TGA), scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), uniaxial stress, and autoscan (provide hardness, composition, velocity, and spectral absorption). The results are integrated, and machine learning is used to derive a predictive algorithm of heat propagation and mapping in the formation with reference to the key formation variables and heterogeneity distribution.
Rock heterogeneity affects the rate and patterns of heat propagation into the formation. Within the rock sample, minerals, laminations, and cementations lead to a heterogeneous, and sometimes anisotropic, distribution of thermal properties (thermal conductivity, heat capacity, diffusivity, etc.). These properties are also affected by the rock structure (porosity, micro-cracks, and fractures) and saturation distribution. The results showed the impact of heat on the mechanical properties of the rocks are due to clays dehydration, mineral dissociations, and micro cracks. High speed thermal imaging provides a unique visualization of heat propagation in heterogeneous rocks. Statistical analysis identified key parameters and their impact on thermal propagation; the output was used to build a machine learning algorithm to predict heat distributions in core samples and near-wellbore.
Characterizing rock properties and understanding how heterogeneity modifies heat propagation in rocks enables the design of optimal completion and stimulation strategies. This paper discusses how advanced characterization and analysis, combined with novel algorithms, can improve this understanding, and unleash innovation and optimization. The data and information gathered are critical to develop numerical models for field-scale applications.
An Innovative Methodology for Estimating Rock Mechanical Properties from Weight or Volume Fractions of Mineralogy and its Application to Middle East Reservoirs
Authors: Prasad Umesh , Hanif Amer , McGlynn Ian , Walles Frank , Abouzaid Ahmed , Hamid Osman 
Published: December 2021

Abstract
The influences of mineralogy on rock mechanical properties have profound application in oil and gas exploration and production processes, including hydraulic fracturing operations. In conventional resources, the rock mechanical properties are predominantly controlled by porosity; however, in unconventional tight formations, the importance of mineralogy as a function of rock mechanical properties has not been fully investigated.
In unconventional tight formations, mechanical properties are often derived from mineralogy weight fraction together with the best estimate of porosity, assumption of fluid types, the extent of pore fillings, and fluid properties. These properties are then adjusted for their volumetric fractions and subsequently calibrated with acoustics or geomechanical lab measurements. A new method is presented that utilizes mineralogy weight fractions (determined from well logs or laboratory measurements). This process uses public domain information of minerals using Voigt and Reuss averaging algorithms as upper and lower bounds, respectively. An average of these bounds (also known as Hill average) provides a representative value for these parameters. Further, based on isotropic conditions, all the elastic properties are calculated.
A typical output consisting of bulk-, shear-, and Young's - modulus, together with Poisson's ratio obtained from traditional methods of volume fractions and this new method using weight fractions is discussed and analyzed along with the sensitivity and the trends for individual rock properties. Furthermore, corresponding strengths, hardness, and fracture toughness could also be estimated using well known public domain algorithms.
Data from carbonate reservoirs has been discussed in this work. This method shows how to estimate grain compressibility that can be challenging to be measured in the lab for unconventional tight rock samples. In low-porosity samples, the relative influence of porosity is negligible compared to the mineralogy composition. This approach reduces several assumptions and uncertainties associated with accurate porosity determination in tight rocks as it does not require the amount of pore fluids and fluid properties in calculations. The grain-compressibility and bulk-compressibility (measured by hydrostatic tests in the laboratory on core plugs or calculated from density and cross-dipole log) are used to calculate poroelastic Biot's coefficient, as this coefficient will be used to calculate in-situ principal effective stresses (overburden, minimum horizontal, and maximum horizontal stresses), which are, together with rock properties and pore pressure, constitutes the geomechanical model. The geomechanical model is used for drilling, completions, and hydraulic fracture modeling, including wellbore stability, and reservoir integrity analyses.
Leveraging Acoustic Logging for Identification & Quantification of Induced Fractures
Authors: Asif Adnan Bin , Alaliwat Mustafa , Hansen Jon , Sheshtawy Mohamed 
Published: December 2021

Abstract
The main objective of the acoustic logging in 15K openhole multistage fracturing completions (OH MSFs) is to identify the fracture initiation points behind pipe and contributing fractures to gas production. The technique will also help to understand the integrity of the OH packers. A well was identified to be a candidate for assessment through such technique. The selected well was one of the early 15K OH MSF completions in the region that was successfully implemented with the goal of hydrocarbon production at sustained commercial rates from a gas formation.
The candidate well was drilled horizontally to achieve maximum contact in a tight gas sandstone formation. Similar wells in the region have seen many challenges of formation breakdown due to high formation stresses. The objective of this work is to use the acoustic data to better characterize fracture properties. The deployment of acoustic log technology can provide information of fractures initiation, contribution for the production and the reliability of the isolation packers between the stages.
The candidate well was completed with five stages open-hole fracturing completion. As the well is in an open hole environment, a typical PLT survey provides the contribution of individual port in the cumulative production but provides limited or no information of contributing fractures behind the pipe. The technique of acoustic logging helped to determine the fracture initiation points in different stages. If fractures can be characterized more accurately, then flow paths and flow behaviors in the reservoir can be better delineated.
The use of acoustic logging has helped to better understand the factors influencing fracture initiation in tight gas sandstone reservoirs; resulting in a better understanding of fractures density and decisions on future openhole length, number of fracturing stages, packers and frac ports placement.
Integration of Cutting Spectroscopy Analysis and Open-Hole Logs to Increase Evaluation Certainty of Complex Clastic Formations – Advantages and Limitations
Authors: Alqunais Ali , Bradford Charles , Qubaisi Khalid 
Published: December 2021

Abstract
This paper presents an approach by integrating advanced cutting analysis, such as x-ray fluorescence (XRF), and open-hole logs for enhanced formation evaluation of complex clastic formations in near real-time. To verify the methodology, results of surface cuttings analyses are compared to and validated with downhole elemental spectroscopy measurements.
In general, when the formation contains clays, the minimum logging requirement to evaluate clastic formations is a triple combo (density, neutron and resistivity) with spectral gamma ray (SGR) logs. In addition to correcting the impact of the drilling fluid additives and properties such as the presence of k-formate in mud, SGR logs become very crucial to differentiate clay types present in the formation. In the absence of SGR, advanced cuttings measurements can be utilized to provide elemental data of major elements including SGR components from the cuttings in near real-time. A comparison was made to evaluate the cuttings analysis as a replacement for SGR. As a part of this work and to validate the petrophysical evaluation results, downhole wireline SGR and elemental spectroscopy data were acquired and compared to the analysis using advanced cutting measurements.
This work was conducted in a siliciclastic formation containing abrasive sandstones of mixed clean quartz and clay minerals. The analysis of cuttings XRF was integrated with basic downhole logs to quantify the clay typing required for representative formation evaluation and well geosteering. Limitations of this approach are identified in drilling complex clastic formations including cutting sampling frequency and effects of drilling including drilling fluid contamination, mud additives, drilling parameters and drilling driving mechanism. Controlling these factors has led to good results from cuttings measurements. The advanced cuttings XRF analysis was benchmarked with wireline SGR and elemental spectroscopy logs.
This approach of using cuttings XRF analysis and basic open-hole logs is a valid option for geosteering in a complex clastic mineralogy formation and providing a near real-time formation evaluation in the absence of spectral gamma ray or elemental spectroscopy. XRF has been proven to provide near real-time analysis with improved reliability across bad hole, wider spectrum of elements and eliminate critical operations risk. Recommendations to optimize the parameters for reliable measurements will be discussed in this paper.
Nanoparticle Tracers in Reservoir-On-A-chip by Surface-Enhanced Raman Scattering - Fluorescence SERS-SEF Imaging Technology
Authors: Chang Sehoon , Eichmann Shannon L. , Wang Wei 
Published: December 2021

Abstract
Nanoparticles or nanocomposite fluids are injected into oil reservoirs for reservoir tracing or to improve injectivity or recovery of oil. Effective application of nanoparticles in fluid flooding still needs to be investigated. Dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles have been developed as nanoparticle reservoir tracers. This presentation discusses their transport and detectability in porous media, providing valuable information for understanding the role of nanoparticles in EOR process. The dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles are synthesized composed of Ag or Au metal cores, specific dye molecules, and a SiO2 shell materials. To optimize maximum signal enhancement of both phenomena such as SERS and SEF, the distance between core metal nanoparticles and dye molecules are precisely controlled. The synthesized composite nanoparticles barcoded with dye molecules are detectable by both fluorescence and Raman spectroscopies due to the SERS-SEF phenomena. Both fluorescence and Raman microscopic images of dye embedded surfaceenhanced Raman scattering (SERS) surface-enhanced fluorescence (SEF) composite nanoparticles in water phase successfully were collected within microfluidic reservoir-on-a-chip. The reservoir-on-a-chip utilized in this study fabricated based on reservoir rock geometry and coated with calcium carbonate. The synthesized SERS-SEF composite nanoparticles in water solution have been flooded into the microfluidic reservoir-on-a-chip and imaged for probing interfacial behavior of fluids such as liquid-liquid interfaces and studying the behavior of nanoparticles at liquid-rock interfaces. The precise synthesis method to produce the composite nanoparticles has been developed for the embedded dye molecules to generate noticeably enhanced detectability due to the strong SERS phenomenon. In conclusion, SERS-SEF nanoparticles barcoded with the fingerprinted Raman and fluorescence signals can provide a possible pathway toward SERS-SEF nanoprobe as various barcoded tracers to understand fluid behavior in porous media. Composite nanoparticle synthesis and its detection in flow technologies have been developed for visualization of the fluid flow behavior in porous media representing reservoir rock geometry. The results of the high-resolution nanoparticle fluid imaging data in reservoir-on-a-chip can be applied to understand mechanism of nanoparticle fluid assisted chemical enhanced oil recovery.
Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs
Authors: Katterbauer Klemens , Marsala Alberto , Zhang Yanhui , Hoteit Ibrahim 
Published: December 2021

Abstract
Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner.
In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach.
We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.