bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning for Prediction of CO2 Minimum Miscibility Pressure
Authors: Shakeel Muzammil , Khan Mohammad Rasheed , Kalam Shams , Khan Rizwan Ahmed , Patil Shirish , Dar Usman Anjum 
Published: March 2023

Abstract
Minimum miscibility pressure (MMP) is defined as the minimum pressure at which the CO2 is dissolved in the oil phase inside the reservoir. Minimum miscibility pressure (MMP) plays a critical role in the CO2 injection process during miscible CO2 flooding. Experimentally, MMP is determined by slim-tube experiments, rising bubble method etc. However, experimental analysis is time consuming and can have high associated cost.
Therefore, application of Artificial Intelligence (AI) techniques can assist in predicting the MMP based on the available input data. This will save significant time and efforts and predicted the MMP results faster and convenient way. Some authors have worked with AI tools to predict MMP, but the model proposed in this paper has a relatively lower error. Thus, the proposed model in this study is an improved model for the prediction of MMP for miscible CO2 flooding applications.
A detailed optimization was carried out in this study for both ANN and ANFIS predictive tools. Single hidden layer with 12 neurons and ‘trainlm’ as training algorithm was found out after ANN optimization, whereas subtractive clustering with cluster radius of 0.3 was the optimum scenario for ANFIS technique. ANN prediction was overall better than ANFIS technique for the prediction of CO2 MMP.
Oil-Water Relative Permeability Prediction Using Machine Learning
Authors: Alarifi Sulaiman A. 
Published: March 2023

Abstract
Relative permeability is one of the most significant reservoir characteristics in the petroleum industry. It captures the fluids behavior inside the porous space within the reservoir. It considers the effective permeability of the fluids in the reservoir which ultimately lead to the understanding of the fluid behavior inside the pores. Also, using relative permeability curves, we can estimate the reservoir's oil or gas recovery. Furthermore, enhanced oil recovery techniques utilize relative permeability curves to evaluate their performance. The well-known practice to develop any relative permeability curve is by conducting core flooding experiments which are relatively time consuming especially if it is needed to be done on several wells with different core samples. Also, it would be costly data set to acquire since it requires special lab sets and conditions. Time and cost are the main factors making relative permeability a very hard to obtain information for any reservoir. Several models and empirical relations have been built to calculate and present relative permeability without going through the lab experiments, each model has its uncertainty.
This paper captures the approach to predict relative permeability curves (oil and water) from a set of data collected from one reservoir using machine learning. Data used is generated from special core analysis lab experiments (core flooding) of unsteady state oil and water relative permeability. Core flooding experiments represents several water saturations at which the core been flooded to, at every water saturation a water and an oil relative permeability value is obtained. To represent the reservoir efficiently and addressing several aspects of its 56 relative permeability curves (from 56 composites) have been collected from different wells in the same reservoir. Adding up to a total of more than 7,000 data sets (different water saturations).
Two models have been built, one for predicting the relative permeability of oil at several water saturations and the second model is for the relative permeability of water. Main input data are water saturations, connate water saturation, residual oil saturation, porosity, oil viscosity, water viscosity, (several basic core properties) and wettability. The outcome for each model is one, either oil or water relative permeability. The main added value of this work is creating a workflow and models to predict water and oil relative permeability using main reservoir data with high accuracy and without conducting any special core analysis.
Development of a Deterministic Total Organic Carbon (TOC) Predictor For Shale Reservoirs
Authors: Khan Mohammad Rasheed , Kalam Shams , Asad Abdul , A. Abu-khamsin Sidqi 
Published: March 2023

Abstract
Unconventional reservoirs like shale oil/gas are expected to play a major role in many unexplored regions, globally. Shale resource evaluation involves the estimation of Total Organic Carbon (TOC) which correlates to the prospective capability of generating and containing hydrocarbons. Direct measurement of TOC through geochemical analysis is often not feasible, and hence researchers have focused on indirect methods to estimate TOC using analytical and statistical techniques. Accordingly, this work proposes the application of artificial intelligence (AI) techniques to leverage routinely available well logs for the prediction of TOC. Multiple algorithms are developed and compared to rank the most optimum solution based on efficiency analysis.
Support Vector Regression (SVR), Random Forest (RF), and XGBoost algorithms are utilized to analyze the well-log data and develop intelligent models for shale TOC. A process-based approach is followed starting with systematic data analysis, which includes the selection of the most relevant input parameters, data cleaning, filtering, and data-dressing, to ensure optimized inputs into the AI models. The data utilized in this work is from major shale basins in Asia and North America. The AI models are then used to develop TOC predictor as a function of fundamental open-hole logs including sonic, gamma-ray, resistivity, and density. Furthermore, to strengthen AI input-output correlation mapping, a k-fold cross-validation methodology integrating with the exhaustive-grid search approach is adopted. This ensures the optimized hyperparameters of the intelligent algorithms developed in this work are selected. Finally, developed models are compared to geochemically derived TOC using a comprehensive error analysis schema.
The proposed models are teted for veracity by applying them on blind dataset. An error metrics schema composed of root-mean-squared-error, and coefficient of determination, is developed. This analysis ranks the respective AI models based on the highest performance efficiency and lowest prediction error. Consequently, it is concluded that the XGBoost and SVR-based TOC predictions are inaccurate yielding high deviations from the actual measured values in predictive mode. On the other hand, Random Forest TOC predictor optimized using k-fold validation produces high R2 values of more than 0.85 and reasonably low errors when compared to true values. The RF method overpowers other models by mapping complex non-linear interactions between TOC and various well logs.
Unsupervised Machine Learning for Sweet-Spot Identification Within an Unconventional Carbonate Mudstone
Authors: Chan Septriandi , Amao Abduljamiu , Humphrey John , Alzayer Yaser 
Published: March 2023

Abstract
Stratigraphic correlation in mudstone intervals is challenging as compared to coarser-grained sedimentary rocks because of the microscale heterogeneity and other constraints. Given critical mm- to cm-scale variability in mudstones, it is daunting to try to infer compositional variability from well logs and seismic data unless core data and laboratory analyses are available to calibrate the results. In this study, we propose a novel integrated approach combining sedimentological core description with geochemical data to establish chemofacies and chemostratigraphic zonation using a set of unsupervised statistical tools, i.e., Principal Component Analysis (PCA) and Hierarchical Clustering on Principal Components (HCPC).
These techniques can be applied to elemental data acquired using x-ray fluorescence measured from core or cuttings samples or spectroscopy logs to provide robust analysis for unconventional assessment regarding sweet-spot identification, sequence stratigraphic interpretations, and drilling and completion designs. Further, the identified zones can be used to characterize/correlate zones in nearby un-cored wells, with the data generated serving as an indispensable input for establishing a well-log data zonation using unsupervised machine learning algorithms.
De-Risking Upper Cretaceous A Formation Production by Joining Multiple Technoologies in 3D Space
Authors: Cazeneuve Eduardo Adrian , Bouzida Yasmina , Smirnov Vladimir , Singh Maniesh , Ahmed Jahan Zeb , Singh Nepal , Celma Rafael , Shehab Sami Sheikh Alawi 
Published: March 2023

Abstract
Realistic geological characterization is crucial for the development of carbonate reservoirs specially to overcome some challenges related to Early Water Break Through (EWBT) like those observed in the Upper Cretaceous ‘A’ Formation. The complexity of the depositional, diagenetic, and structural history created unpredictable connected pathway with the aquifer. Therefore, having suitable conceptual geological models tied to well data helps to better anticipate their occurrence and extension to plan for suitable well placement, a completion design, and optimize the production.
The objective in this case study was to assess the Deep Shear Wave Imaging (DSWI) technology that can bridge the scales between well Borehole Image (BHI) and conventional Seismic to identify sub-Seismic features up to 100ft away from the well bore. A statistical evaluation of the BHI was used as a reference for this DSWI assessment. Then the result was compared with Seismic coherency to assess the value of the combined data.
This cross-discipline integration in 3D from different data measurements with a variety of scales by order of magnitude difference between them (Well and Seismic), has allowed to build a conceptual model of the near wellbore region up to 90ft and predict five significant fault zones that are susceptible of connecting with aquifer. Dynamic data will be used in the second phase of the project such as (liquid rate, water breakthrough and tracers) to analyze their contribution to the EWBT and refine/enhance the interpretation.
The way forward is to apply a similar workflow to other wells in the field, to gain a solid understanding of the sub-seismic architecture of the subsurface and hence timely optimize the setup of Inflow Control Device (ICD) design used to mitigate EWBT.
Application of Image Processing Techniques in Deep-Learning Workflow to Predict CO2 Storage in Highly Heterogeneous Naturally Fractured Reservoirs: A Discrete Fracture Network Approach
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: March 2023

Abstract
Naturally fractured reservoirs (NFRs), such as fractured carbonate reservoirs, are commonly located worldwide and have the potential to be good sources of long-term storage of carbon dioxide (CO2). The numerical reservoir simulation models are an excellent source for evaluating the likelihood and comprehending the physics underlying behind the interaction of CO2 and brine in subsurface formations. For various reasons, including the rock's highly fractured and heterogeneous nature, the rapid spread of the CO2 plume in the fractured network, and the high capillary contrast between matrix and fractures, simulating fluid flow behavior in NFR reservoirs during CO2 injection is computationally expensive and cumbersome. This paper presents a deep-learning approach to capture the spatial and temporal dynamics of CO2 saturation plumes during the injection and monitoring periods of Geological Carbon Sequestration (GCS) sequestration in NFRs. To achieve our purpose, we have first built a base case physics-based numerical simulation model to simulate the process of CO2 injection in naturally fractured deep saline aquifers. A standalone package was coded to couple the discrete fracture network in a fully compositional numerical simulation model. Then the base case reservoir model was sampled using the Latin-Hypercube approach to account for a wide range of petrophysical, geological, reservoir, and decision parameters. These samples generated a massive physics-informed database of around 900 cases that provides a sufficient training dataset for the DL model. The performance of the DL model was improved by applying multiple filters, including the Median, Sato, Hessian, Sobel, and Meijering filters. The average absolute percentage error (AAPE), root mean square error (RMSE), Structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR), and coefficient of determination (R2) were used as error metrics to examine the performance of the surrogate DL models. The developed workflow showed superior performance by giving AAPE less than 5% and R2 more than 0.94 between ground truth and predicted values. The proposed DL-based surrogate model can be used as a quick assessment tool to evaluate the long-term feasibility of CO2 movement in a fracture carbonate medium.
Energy Transition in the Oil & Gas Industry – Emerging Technology Trends in the Upstream Sector
Authors: Buranaj Hoxha Bez , Nair Aravind 
Published: March 2023

Abstract
The energy transition is a popular concept, fettered by technology hype, political agendas, and financial influences that are inherently attached to the term ‘energy transition’. There are a lot of misconceptions and intricacies behind the strategies operators are undertaking in this energy paradigm shift. Therefore, clarity is needed on what is driving this paradigm shift: technology, policy, and cultural change.
The scope of this paper focuses on defining the different strategies operators are adopting in their operations that align with transforming business models in the energy transition pathway. Predominantly segmented on various emerging technologies being implemented during operations and processes in upstream sector. Technologies that were barely considered before are now being deployed. Furthermore, explanations and considerations will be offered on emerging technology trends implemented in field operations, from subsurface drilling to completion.
Lastly, the information and data presented herein are based on industry market intelligence supported by personal case studies and primary and secondary market research.
Anisotropic Poroelastodynamics Solution and Elastic Moduli Dispersion of a Naturally Fractured Rock
Authors: Liu Chao 
Published: March 2023

Abstract
In this work, the theory of anisotropic dual-porosity dual-permeability poroelastodynamics is used to simulate the responses of pore pressure, displacement, and stress of a fluid-saturated transversely isotropic naturally fractured cylindrical rock sample. The sample is subjected to a harmonic loading with a constant displacement amplitude at one end. These solutions are then use d to calculate the elastic moduli dispersion of the rock sample. A transversely isotropic water-saturated rock sample is selected as an example to demonstrate the simulation and the mechanisms of the dispersion due to the coupled motions of the rock matrix and fluids in pore spaces and fractures. The effects of material anisotropy on the poromechanical responses and the elastic modulid dispersion of the rock sample are presented. We also show excellent matches between the simulation and laboratory measurements of the dynamic Young's moduli of two shale, one clay, and three sedimentary rock samples.
A Method for EUR Forecasting in Unconventional Reservoirs Using Early Flowback Data
Authors: Mahmoud Ihab , Alsulaiman Nouf 
Published: March 2023

Abstract
Evaluating EUR and production forecasting in multi-frac horizontal wells completed in unconventional shale reservoirs during early, exploration and appraisal stages is very challenging. With the absence of suitable production facilities to handle produced fluids, production data are limited to short flow backs and extended production for some key wells to early production facility (EPF).
This method uses the flow capacity as an intrinsic parameter that captures and reflects the major drainage mechanism and recovery characteristics of the well within the unconventional reservoir. Thus, the flow capacity may serve as a reference parameter that can be estimated from early-time data. This parameter may have the ability to reflect the future production behavior of the well in terms of cumulative production through proportional comparison of flow capacity to EUR.
To show applicability of the workflow, it was applied to an existing unconventional project and showed consistent results. The workflow incorporates RTA analysis for several wells to create a single correlation that describes the relationship between Ac√k and EUR. Plotting gas flow capacity (Ac√k), estimated during the early 3-4 weeks of flowback, versus the estimated EUR from modeling techniques showed a strong correlation.
The proposed workflow is designed to estimate EUR for wells completed in unconventional reservoirs during the early phases of development, where there is no production facility to handle produced hydrocarbons and flow back period is limited to cleanup only. The advantages of the proposed workflow over the currently available methods are: (1) Incorporating several wells analyses to create a single correlation that describes the relationship between Ac√k and EUR (2) The ability to evaluate EUR from as-early-as 3-4 weeks of flowback data. The early evaluation of these wells will expedite critical completion and development decisions which will impact project economics.
Characterizing Movable and Non-Movable Zones in a Mature Carbonate Reservoir: A Novel Workflow Using Resistivity Logs
Authors: Al-Otaibi Saud , Aldajani Saud , Al-Sayyar Talal , Al-Marri Salem 
Published: March 2023

Abstract
Optimizing reservoir recovery depends on an in-depth understanding of natural geological complexity to predict reservoir behavior. Understanding the difference between producible oil and non-movable oil zones is important, which will aid in the refinement of the design of future wells.
During the mature life cycle of the Maastrichtian carbonate reservoir, it was observed that some wells would not perform optimally, while others would experience a significant drop in production. By analyzing petrophysical and production data, the reservoir was found to contain hydrocarbons consisting primarily of heavy oil and stringers of light oil. Based on reservoir characterization and after assessing the production profile to understand the hydrocarbon behavior, this study was performed to identify and distinguish movable oil zones from non-movable oil zones.
Conventionally, expensive intervention methods, such as running modular dynamics formation tester (MDT), nuclear magnetic resonance (NMR) logs, and production logging tools (PLT), are used to determine the oil viscosity (API) and identify contribution zones from the entire hydrocarbon interval. However, using these methods results in increased operational costs and reduced production.
This study proposes an alternative approach using resistivity logs to identify and distinguish between movable and non-movable hydrocarbon zones to improve reservoir management. The concept behind this method depends on the resistivity logs validated using MDT and PLT data. A shallow resistivity reading higher than a deep resistivity reading indicates that hydrocarbons were not flushed (unmoved) by invasion. Thus, the zone contains unproducible hydrocarbon reserves. The resistivity cut-off value was estimated based on the PLT and MDT data to identify movable oil intervals.
In all the wells analyzed, there was a good correlation among the calculated zone thickness, core data, sampling data, and mud logs. Dielectric logs were run in a couple of key wells, which enabled the Sxo estimation independent of resistivity. Additionally, the Sxo obtained supports the fluid interpretation. Productive zones were accurately identified for each well, and recompletions were made to produce from these bypassed opportunities.
The proposed method is robust with respect to environmental corrections, not contingent on MDT, NMR, and PLT knowledge, and can be carried out without halting production.
Novel Carbon Dioxide (CO2) Foamed Fracturing Fluid, an Innovative Technology to Minimize Carbon Footprint
Authors: Samuel Mathew M. , Al-Jalal Ziad , Nurlybayev Nurlan , Farouk Mohammad , Xiao Zhijun , Kang Kang Cheng , Huanming Li 
Published: March 2023

Abstract
Decarbonization is one of the greatest challenges that our nation is facing now and can be achieved by lowering the carbon footprint. This is believed to address the climate change (Miralles-Quirós et al.) and this task necessitates the development of technologies for energy production and storage, carbon capture and utilization in a very wider scale (UN FCCC, 2015). The present study is an attempt to utilize carbon dioxide in oilfield operation that does not require any equipment or infra structure other than what already present in a fracturing field location.
Hydraulic and acid fracturing treatments based on foamed fluid reduce the overall amount of liquid pumped, provide better fluid-loss control and diversion, and aid in post-treatment flowback. Foam fracturing causes less damage to formation, fracture face and proppant pack, improving the productivity of oil and gas well. Among nitrogen and carbon dioxide (CO2) gases used to create foams, use of CO2 is preferred over nitrogen, though it is operationally complex. Positive impact of low carbon is widely accepted and can be achieved either by producing less carbon dioxide, carbon capture and sequestration, or by its reuse. This paper will describe a novel and highly stable foam fracturing fluid based on a new Spiral Gel (S-Gel 38) polymer-foamer system that is developed to reuse the produced carbon dioxide and thus to lower carbon footprint.
An Integrated Ensemble Based Prediction Approach to Quantify and Propagate Uncertainties of Naturally Fractured Carbonate Reservoirs
Authors: Abd-Allah Mohammed , Abdelrahman Ahmed 
Published: March 2023

Abstract
Naturally fractured reservoirs are holding most of the hydrocarbon proven reserves worldwide. Field development planning and optimization of fractured reservoirs face significant challenges due to fracture system complexity, high reservoir heterogeneity and multiple recovery mechanisms. Fractured systems generally have multiple sets of fractures in different orientations, multiple apertures ranging from tiny fissures to large fracture conduits resulting in complicated fluid flow.
Reservoir modeling plays a key role in field optimization by examining reservoir recovery under various scenarios. Traditionally, modeling fractured reservoirs is a deterministic process using a single or few reservoirs model (high, mid, and low cases) without proper integration of seismic-to-simulation reservoir model uncertainties. The full span of possible reservoir models and prediction uncertainty isn't captured nor propagated to economics.
This paper presents an integrated forward modeling workflow using a Synthetic 3D model data mimicking a real North Sea field, where static and dynamic models are tightly connected to integrate the impact of uncertainties at different modeling stages (horizon uncertainties, fault uncertainties, petrophysical uncertainties, discrete fracture network (DFN) uncertainties to dynamic simulation).