bluuebunny/crossref_metadata_2025_split huggingface dataset:
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: June 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.
Estimation of Permeability, Skin, and Inflow Profile in Multilayered Systems from Temperature Transient Data Using a Coupled Nonisothermal, Transient Reservoir and Wellbore Model
Authors: Alan Cihan , Cinar Murat , Onur Mustafa 
Published: June 2023

Abstract
The objective of this paper is to investigate the estimation of layer permeability, skin, and inflow profile from observations of production-logging-tool (PLT) and/or distributed temperature sensing (DTS) for a multilayered system where the layers communicate only through the wellbore. To achieve this objective, we develop a thermal, transient coupled reservoir/wellbore simulator that numerically solves transient mass, momentum, and energy conservation equations simultaneously for both reservoir and wellbore. The simulator accounts for the Joule-Thomson (J-T), adiabatic expansion, conduction, and convection effects for predicting the flow profiles across the wellbore. A comparison of the developed model with a commercial simulator is provided for the single-phase fluid flow of oil or geothermal brine from partially penetrating vertical or inclined wells with distinct fluid and formation properties. A sensitivity study on transient pressure, rate, and temperature profiles to identify the effect of the layer petrophysical properties and the layer thermophysical parameters is also conducted through synthetically generated test data sets from the developed simulator. In addition, nonlinear parameter estimation with the use of both profiles is shown to be useful to reveal permeability and skin information about individual layers. The results show that temperature transient data are more reflective of the properties of the near wellbore region, while wellbore pressures are determined more by average reservoir parameters. The simulator proves practical for designing a PLT test provided that limitations such as single-phase fluid flow having vertical or inclined well equipped with a thorough fluid characterization (EOS) are met. Such design tests may provide a good source for cross-checking PLT flow profiles and validating the fluid contributions from layers that are open to flow. It is often that the spinner of the field PLT tool does not operate properly at very low flow rates. Also, the spinner may fail to calculate and construct PLT plots accurately at very high flow rates. To the best of our knowledge, this is the first study that presents a coupled transient reservoir/wellbore model for predicting layer permeability, skin, and inflow profile of a well from observations of pressure, temperate, and/or rate data from production-logging-tools (PLTs) and/or distributed temperature sensing (DTS) fiber optic cables.
Flow of Viscoelastic Polymer Solutions in Porous Media: Influence of Molecular Weight and Dispersity
Authors: Langanke Nils , Leblanc Thierry , Fadili Ali , Hincapie Rafael E. , Ganzer Leonhard 
Published: June 2023

Abstract
The properties of polymeric materials are commonly modified by adjusting the dispersity of the molecular weight distribution, since polymer properties are dominated by intermolecular interactions. We utilized this approach to alter the rheological behavior of polymer solutions for application sub-surface and other porous media flow. We correlate the molecular weight distributions with screen factor measurements and in-situ rheological behavior. Aqueous solutions were prepared using mixtures of partially hydrolyzed polyacrylamide (HPAM) having different molecular weights. The behaviour of the solutions was studied in single-phase flooding experiments using Bentheimer and Berea outcrops, as well as a glass-silicon-glass microfluidic device that mimics porous media. The in-situ rheological behavior determined from flooding experiments was monitored by differential pressure measurements. To improve data accuracy, the core flooding experimental set-up was equipped with multiple pressure sensors along the core. Polymer solutions of same shear viscosity but significantly different dispersities were utilized for the investigation. Elongational viscosities were determined by screen factor measurements. We show that the apparent viscosity during polymer injection is significantly altered for polymer solutions of same average molecular weight but different dispersity. Namely, the onset of shear thickening occurs at lower equivalent shear rates when dispersity is high. Furthermore, the flow of polymer solutions in porous media was correlated to screen factor measurements. This effect of the dispersity of the molecular weight distribution can be used to optimize polymer solution applications in porous materials.
Scenario Based Optimization Methodology for Field Development Planning
Authors: Litvak Michael , Rosenzweig Jacob , Marblestone Grant , Matringe Sebastien , Wang Pengju 
Published: June 2023

Abstract
An innovative optimization methodology for field development planning is presented. A new mixed integer optimizer is described. The optimization tool's "user-friendly" plug-in in a commercial reservoir characterization and simulation package is developed, and methodology applications in exploration projects are outlined.
An effective methodology is developed to optimize well placement and facility options in oil fields with multiple reservoirs. The optimized field development plan is selected for individual reservoirs from various well placements, well trajectories, injection strategies, and facility scenarios significantly impacting field oil recovery. Multiple subsurface models representing uncertainties in subsurface descriptions are applied in the optimization process. An effective mixed integer optimizer is developed. The optimizer is based on sequential cycles of a) selection of "promising" scenarios changing one decision variable per simulation and b) evaluations of combinations of the "promising" scenarios using Latin Hypercube sampling.
The optimization workflow is implemented as a user-friendly plug-in to a commercial package, which allows one to a) define locations and trajectories of potential wells, b) define well placement and facility scenarios, c) run optimization workflows, and d) evaluate optimization results.
The developed optimization methodology is successfully applied in several exploration projects. Effectiveness and significant benefits from the optimization applications are demonstrated.
This paper can bring significant benefits to the state of knowledge in the petroleum industry by a) describing the novel methodology for optimizing field development scenarios that have significant impacts on oil recovery, b) applying the new optimizer, c) implementing the optimization plug-in in a commercial package.
An Analytical Method for Estimation of Thermal Fracturing Initiation During CO2 Injection in Depleted Gas Reservoirs
Authors: Huijskes Thijs , de Kok Joop 
Published: June 2023

Abstract
During CO2 injection, cooling and pressure buildup in the near-wellbore may lead to fracturing of the reservoir rock. These fractures affect the injectivity and can have a significant impact on flow assurance. This paper presents the derivation of a quick and simple method to evaluate the onset of thermal fracturing.
This paper starts with geomechanics theory and expressions of minimum horizontal stress in depleted reservoirs. It describes the relations between thermo-poro-elastic stress and the criterion for initiation of (thermal) fracturing. The thermal stress part uses a simplified analytical solution of the stresses around the wellbore taking into account differences due to near-wellbore cooling and far-field virgin temperature conditions. A similar methodology is used to accommodate for pressure difference between the near-wellbore area and the far-field. A set of geomechanical and reservoir parameters are used to set-up diagnostic plots for evaluation. Consequences of the difference between the depletion stress path and injection stress path are discussed. Finally, results are compared to numerical reservoir simulation results.
A proposed thermal stress correction factor, which accounts for differences between a simple analytical solution and a full-field evaluation, is checked and adjusted by comparing analytical and numerical results. First indications show that this geometrical factor and the derived equivalent cold zone radius holds for many cases. The sensitivity of thermal fracturing for several reservoir and rock parameters is discussed. The analytical method found is quick, simple and generates equivalent results to the numerical simulator and is therefore assumed to be accurate for estimation of the moment of fracture initiation.
The resulting diagnostic plots present a quick and simple alternative to geomechanical simulation for evaluating the possibility and moment of fracture initiation. This method can help in early-stage feasibility work and determine if more detailed modelling is needed.
Applying Machine Learning to Minimize Wireline Formation Testing Failures in Thin Laminated Sands
Authors: Fayzullin Maksim , Schmidt Dennis , Deckers Marie 
Published: June 2023

Abstract
This paper demonstrates how supervised machine learning (ML) aids planning and acquisition of wireline formation testing (WFT) in thin laminated sands. Available well data was used to train a set of algorithms to identify intervals where tests are likely to fail. The trained model aims to prevent WFT failures what in turn results in reduced rig downtime, increased efficiency of the logging contracts and improved reservoir characterization.
Wireline formation testing is essential to acquire rock and fluid characteristics in multilayered reservoirs setting up the base for the upcoming decisions. This becomes significantly complicated in thin laminated sands with a thick hydrocarbon column, requiring hundred(s) of points to meet given objectives. The percentage of failed tests can be much higher than those of being successful. In this context an automated advisory system, based on the abundant historic WFT dataset, can mitigate personal biases of the subsurface team and boost the share of successful tests in future wells.
A combined set of wireline logs served as features to predict WFT outcome in two classification approaches. Binary classification predicts likelihood of having a good test or a failure, whereas multi-classification further details failure types into 5 categories. The overall dataset comprised more than 500 points (testing attempts) within the concession to train various ML models, using variety of preprocessing and hyper parameters. Their accuracy and area under curve (AUC) were used as the ranking criteria. The performance mostly depends on the number of classes to be predicted, the number of input features and the number of data points available for training. Less classes to predict and more input features result generally in better model metrics. The final selected model attained a maximum accuracy of 0.75 in two exploration wells in the adjacent concessions, i.e. correctly predicting 75 outcomes out of 100 wireline formation tests. A log interpretation suite accesses the deployed model via a cloud endpoint for the upcoming infill wells.
The approach could improve wireline formation testing in other reservoirs or regions prone to WFT failures, where accumulated data is sufficient for machine learning applications. This could result in tangible savings during well operations.
Modified Cam-Clay Model Parameters for Well-Cement
Authors: Soustelle Vincent , Moghadam Al , Corina Anisa Noor 
Published: June 2023

Abstract
Geothermal heat is expected to become an important sustainable energy source for the Netherlands. Geothermal wells are expected to last for decades and provide an acceptable return on investment. The well cement in geothermal wells experiences a unique stress condition due to the injection/production of cold/hot water in a doublet. The impact of these cyclical loads on cement has yet to be discovered. Critical state constitutive models such as the Modified cam-clay (MCC) are most suitable to describe cement's mechanical behavior. In this work, we present a new experimental protocol to measure the MCC model parameters for well-cement and to quantify the plastic damage accumulation under realistic cyclical conditions relevant to the Dutch geothermal wells. The protocol was tested on class G cement.
The experiments were carried out in a triaxial apparatus and consisted of (1) unconfined compressive strength (UCS) tests; (2) hydrostatic compression tests to find the initial size of the yield surface (p0) and the slope of the swelling and compression lines (κ and λ); (3) triaxial tests on both the dry and wet side of the yield surface; and finally (4) cyclic loading tests under conditions relevant to a mid-enthalpy doublet. The effect of cyclic loading on cement integrity is quantified by measuring the inelastic deformation and elastic moduli after each cycle, followed by a triaxial test.
The UCS tests on class G cement show peak stresses of 34-41 MPa, and critical state stresses (CSS) of approximately 10 MPa. The triaxial tests performed on the dry and wet sides display a strain softening and hardening behavior, respectively, as predicted by the MCC model. CSS values from all the tests align along the newly defined critical state line (CSL) for class-G cement. The CSL has a slope M = 1.5 and an intercept (p) of −3.7 MPa, close to the anticipated tensile strength of class-G cement. Hydrostatic tests give p0 of 23-29 MPa and κ and λ of 0.0046 and 0.02. The cyclic test simulating the producer well shows negligible plastic deformation after 10 cycles. Conversely, the deep injector well shows cement damage accumulation.
Geothermal Reservoir Optimization Empowered by a Generalized Thermal Decline Model and Deep Learning
Authors: Yan Bicheng , Gudala Manojkumar , Sun Shuyu 
Published: June 2023

Abstract
Geothermal energy is naturally renewable harnessed from subsurface reservoirs and is feasible to help enrich the energy spectrum and decarbonize the economy. Cold geo-fluid such as water is injected to extract the heat from hot rocks, and then hot fluid can be produced, which can be used for the purpose of heating or power generation. The reservoir management of geothermal recovery process is an integration of geology, drilling, reservoir and production, and particularly it requires expensive simulations that couple the thermo-hydro-mechanical (THM) effect. In this study, we developed a reservoir simulation model to simulate the enhanced geothermal systems (EGS). After evaluating the produced fluid temperature curves, we proposed a generalized thermal decline model that considers the thermal breakthrough and the following decline behavior. This model is parsimonious with only 3 variables. Moreover, a forward surrogate model by deep neural network is developed to predict the decline model variables and the ultimate total net power based on the reservoir parameters. The forward surrogate is integrated with a differential evolution optimizer, which considers reservoir uncertainties and nonlinear constraints for the optimization of the total net power. Accelerated by the thermal decline model and forward surrogate model, we were able to efficiently perform reservoir optimization in high-performance computing environment, and this makes the workflow quite scalable for real-time reservoir management.
Numerical Investigation of Subsurface Hydrogen Storage: Impact of Cyclic Injection
Authors: Zhang Haiyang , Al Kobaisi Mohammed , Arif Muhammad 
Published: June 2023

Abstract
The use of hydrogen (H2) as a clean fuel has gained enormous interest in recent years. For this purpose, excess H2 can be stored in subsurface geological formations. The underground hydrogen storage (UHS) can help to mitigate the challenges associated with seasonal variability in renewable energy production and provide a reliable source of hydrogen for future utilization. While recent studies showed that repeated hydrogen injection and production in aquifer can result in hydrogen and water cyclic hysteresis, the existing classical trapping models fail to model such phenomena in the context of hydrogen and brine. Moreover, the impact of cyclic hysteretic behavior effect received little or no attention on the reservoir scale and thus still remains poorly understood. This study conducts numerical simulations to analyze the impact of cyclic hysteresis on the efficiency of underground hydrogen storage. The results showed that the cyclic hysteresis effect will result in a shorter lateral migration of the injected H2 and more H2 accumulating in the vicinity of the wellbore due to the poorer hydrogen flow ability and higher critical hydrogen saturation. The accumulated hydrogen will in turn contribute to a higher hydrogen recovery factor and thus improve the efficiency of underground hydrogen storage.
Chasing Gas Asset Value Maximization: An Integrated Workflow Led by Reservoir Monitoring
Authors: Mariotti Pamela , Toscano Claudio , Vecera Carmela , Da Marinis Annunziata , Frau Simone , Poggio Franco , Pangestu Imam , Praja Kurna 
Published: June 2023

Abstract
Currently the oil and gas industry is becoming more digitalized. The abundance of data varieties that are recorded has driven the industry to move forward from the conventional data management to more fashioned data acquisition. The field under study (Field A) is a deep-water gas asset, characterized by a complex internal architecture of many separate and discrete gas charged stacked sand bodies. Objective of this paper is to show the key role of the reservoir monitoring strategy, fully integrated in a multidisciplinary workflow that allowed to detail the reservoir conceptual model leading to the identification of valuable production optimization opportunities.
Field A produces through smart wells with selective completions, equipped with permanent down hole gauge (one for each open layer) allowing Real Time Monitoring of the key dynamic parameters (e.g., rate, flowing bottom hole pressure) and implementation of surveillance actions such as selective Pressure Transient Analysis. A workflow is implemented to be able to describe each open layer performance integrating all available data starting from well back allocation verification through virtual metering implementation. Then, Inflow Performance Relationship per layer is used to back-allocate well production to each unit. Robust continuous update of material balance analysis for each layer allowed to verify alignment between the geological gas volume in place and the dynamic connected volume, leading to update coherently also the dynamic model.
Comparison between geological gas volume in place and dynamic connected one triggered a revision of geological modelling, reviewing seismic uncertainty and facies modelling, trying to embed dynamic evidence. Among parameters taken in account, layers internal connectivity resulted as the most impacting one. The revised model allowed to identify and rank residual opportunities on developed layers and possible additional explorative targets. The result of this screening led to the strategic business decision to plan an infilling well, with primary target the best unexploited sub-portion identified inside one of the analyzed layers together with other stacked minor targets. The expectation of primary target resulted confirmed by the data acquired in the new well drilled.
Moreover, the real time monitoring workflow has been implemented in a digital environment for continuous automated update resulting in continuous reservoir monitoring and management.
The successful experience on Field A proved the key role of a structured Reservoir Monitoring strategy as "drive mechanism" for a decision-making process extremely impacting on the core business. The automation of data extraction, will lead the way to an increasingly efficient use of "big amount" of data coming from real time monitoring, thus further improving the overall process of asset maximization opportunities identification.