bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Grid Resolution on Underground Hydrogen Storage Compositional Reservoir Simulation
Authors: Alhotan Muhammad , Delshad Mojdeh , Sepehrnoori Kamy 
Published: March 2023

Abstract
Hydrogen is poised to play a critical role in the global transition to sustainable and securable energy. Exploration of Hydrogen energy technologies is gaining unprecedented momentum due to its promising potential as a low carbon energy storage technology. Hydrogen is also a versatile fuel with economy-scale applications in transportation and industrial processes. Among the challenges that arise from considering economy-scale hydrogen utilization is the large volume seasonal storage. While hydrogen has an energy mass density higher than gasoline, it requires an ample and secure storage medium due to its gaseous utilization form and low volumetric density. As such, the development of storage technologies such as Underground Hydrogen Storage (UHS) is necessary. As a sub-domain of gas reservoir engineering, UHS applications face similar challenges to those faced by other subsurface technologies in terms of understanding the geological substrata. Reservoir simulation offers an approach to improve our understanding of hydrogen behavior in geological reservoirs. However, as an emerging technology, best practices in simulating hydrogen storage in porous reservoirs is not fully understood in the literature. One key factor to consider is numerical grid size and its effect on simulation results.
In this paper, the effect of numerical grid resolution on UHS simulations is investigated using a commercial compositional reservoir simulator. Additionally, a real field geological model is used to evaluate the grid resolution effect on hydrogen storage volume, reservoir pressure, cushion gas, working gas capacity, water production, hydrogen solubility in water, and residual trapping of hydrogen. The published measured hydrogen properties, relative permeability, and capillary pressure are incorporated in these simulations. Results indicate that grid size is highly influential to accurately model hydrogen migration during injection and production cycles. In addition, solubility quantification of hydrogen was found to be affected by grid resolution.
Enhancing Fracture Network Complexity Using Carbonated Slickwater Fracturing
Authors: Karadkar Prasad , AlTammar Murtadha J. , Alabdrabalnabi Mohammed , Busaleh Ahmad 
Published: March 2023

Abstract
Fracturing with slickwater has been widely adopted over the past couple of decades in the development of shale and tight formations. This paper proposes the use of CO2-foamed slickwater, termed carbonated slickwater, as a potential fracturing fluid that maintains suitable proppant carrying capacity while achieving less freshwater consumption, faster and efficient flowback recovery, improved hydrocarbon recovery due to CO2 miscibility with reservoir fluids, deeper CO2 penetration, and potentially higher fracture network complexity and more extensive stimulated reservoir volume.
A circulating-loop foam rheometer was utilized in this study to compare the rheological behavior of slickwater and CO2-foamed slickwater at 50% foam quality. The slickwater, with and without foaming, was tested under a wide range of conditions, including under pressures up to 2500 psi, temperatures up to 300°F, and shear rates up to 1500 1/s. The stability of CO2 foamed slickwater was tested for 30 min at 250°F, 275°F, and 300°F. The viscosity increased to 6.3 cp from 2.7 cp after 50% foaming with CO2 measured at a constant shear rate of 1000 1/s and 300°F. The foaming characteristic can be measured on site using a simple blender test described in this paper. Static foam stability describes the change in foam height or liquid drainage with time known as foam half-life also measured at atmospheric conditions. Using the blender test, around 70% foam quality was achieved, which gave more than a one-hour foam half-life under atmospheric and static conditions.
In this paper, we have explored foamed slickwater as a potential alternative fluid to slickwater for fracturing unconventional formations. We theorize that because of the similar viscosity, carbonated slickwater would have similar fracture propagation/complexity and proppant-carrying capability. During shut-in after fracturing, the CO2 could stimulate additional smaller fractures, ultimately leading to more fracture complexity. Additionally, during flowback, CO2 can maximize flowback recovery pumped slickwater.
An Innovative Machine Learning Method for Predicting Well Performance in Unconventional Reservoirs with a Relatively Small Data Set
Authors: Liu Hui-Hai , Zhang Jilin , Temizel Cenk , Abdelrahman Moemen 
Published: March 2023

Abstract
The machine learning method, now widely used for predicting well performance from unconventional reservoirs in the industry, generally needs large data sets for model development and training. The large data sets, however, are not always available, especially for newly developed unconventional plays. The objective of this work is to develop an innovative machine learning method for predicting well performance in unconventional reservoirs with a relatively small data set.
For a small training data set, the corresponding machine learning model can significantly suffer from so-called overfitting meaning that the model can match the training data but has poor predictivity. To overcome this, our new method averages predictions from multiple models that are developed with the same model input, but different initial guesses of model parameters that are unknowns in a machine learning algorithm and determined in the model training. The averaged results are used for the final model prediction. Unlike traditional ensemble learning methods, each prediction in the new method uses all the input data rather than its subset. We mathematically prove that the averaged prediction provides less model uncertainty and under certain conditions the optimum prediction. It is also demonstrated that the method practically minimizes the overfitting and gives relatively unique prediction. The usefulness of the method is further confirmed by its successful application to the data set collected from less than 100 wells in an unconventional reservoir. Sensitivity results with the trained machine learning model show that the model results are consistent with the domain knowledge regarding the production from the reservoir.
A Dynamic Residual Learning Approach to Improve Physics-Constrained Neural Network Predictions in Unconventional Reservoirs
Authors: Mohd Razak Syamil , Cornelio Jodel , Cho Young , Liu Hui-Hai , Vaidya Ravimadhav , Jafarpour Behnam 
Published: March 2023

Abstract
Predictive models that incorporate physical information or constraints are used for production prediction in subsurface systems. They come in many flavors; some include additional terms in the objective function, some directly embed physical functions and some use neural network layers to explicitly perform physical computations. In unconventional reservoirs that are characterized by tight fractured formations, a detailed and reliable description of the flow and transport processes is not yet available. Existing physics-based models use overly simplifying assumptions that may result in gross approximations. In physics-constrained neural network models, the network predictive performance can be degraded when the embedded physics does not represent the relationship within the observed data.
We propose dynamic residual learning to improve the predictions from a physics-constrained neural network, whereby an auxiliary neural network component is introduced to compensate for the imperfect description of the constraining physics. When a dataset cannot be fully represented by a trained physics-constrained model, the predictions come with a large error or residual when compared to the ground truth. A deep neural network utilizing a masked loss function to enable learning from wells with varying production lengths is employed to learn the complex spatial and temporal correspondence between the well properties such as formation and completion parameters to the expected residuals. The new formulation allows for dynamic residual correction, avoids unintended bias due to less-than-ideal input data, and provides robust long-term predictions when partially-observed timesteps are present. The proposed method results in a final prediction that combines the prediction from the physics-constrained neural network with the predicted residual from the auxiliary neural network component. Several synthetic datasets with increasing complexity as well as a field dataset from Bakken are used for demonstration.
Digital Twin of Degradable Fluid Loss Additive Supported by Advanced Slurry Flow Model
Authors: Isaev Vadim , Khan Abdul Muqtadir 
Published: March 2023

Abstract
This study is a part of larger scheme of reservoir strategy for use of degradable fluid loss additives. Components of this framework have been presented in two previous papers and this paper specifically describes the novel modeling approach. The hydraulic fracturing model accounts for the effect of fluid loss additive on fluid leakoff into the rock during the treatment. It is implemented with slurry flow model based on the lubrication theory. An empirical closure relation for Carter leakoff coefficient represents the effect of leaked fluid volume and the volume of fluid loss additive deposited in the filter cake. The numerical algorithm is based on the combined Eulerian-Lagrangian approach. A simulation example showing the effect of fluid loss additives on pad volume reduction and potential production performance enhancement is presented.
The presented approach is the first slurry flow model which allows to accurately simulate the benefits of fluid loss additive and aims to create a digital twin of these additives eliminating the need to conduct laboratory experiments and design scenarios, revamping the fracturing design strategy.
Stability Analysis for Sidetracked Wells in Poro-Elastoplastic Formations
Authors: Sabitov Denis , AlBahrani Hussain , Hamid Osman 
Published: March 2023

Abstract
In this study, we formulate the Finite Element model (FEM) of the stress-strain state for a three-dimensional (3D) borehole section with a sidetracked hole in fluid saturated elastoplastic formation. We then investigate the dependence of the safe mud weight range on the build angle while considering the plastic rock deformation behavior. The objective here is to showcase the unique drilling recommendations that can be generated for the sidetrack initiation or junction point, especially when compared to those of the mother-bore.
3D meshes are developed to analyze sidetracking scenarios in terms of orientation and initiation points with respect to the mother-bore. We utilize a 3D poro-elastoplastic model to take into account the pore pressure, using the concept of Biot-Terzaghi effective stresses. The Galerkin weak formulation is used to obtain the equations in discrete form. A robust solution of the static problem for elastoplastic body with stress boundary conditions is ensured by adding the Lagrange multipliers to set the constrains on rotation and translation of the solid as a rigid body. We utilize the Newton-Rapson method to get the solution of the equilibrium equation by providing the non-linear iterations.
We demonstrate the ability to determine a safe mud weight range by computing several scenarios for in-situ stresses in a sandstone formation. The scenarios consider changes in the borehole orientation angles and the junction or initiation point. The scenarios also analyze the dependence of the stress-strain state near the sidetracked borehole section on the existing mother-bore. The shape and thickness of the plastic zone strongly depends on the orientation of the borehole and applied pore pressure distribution. Utilizing the elastoplastic model, we implement the procedure to find the optimal build angle for the design of sidetracking sections considering the hole orientation and geometry.
The developed code for poro-elastoplastic formations is realistic alternative to conventional geomechanics models that fail to consider the influence of the mother-bore on the drilling conditions of the sidetrack.
Drilling with 3d Geomechanical Modeling - Efficient Simulation Method
Authors: Al-Hamad Hamad , Sarah AlSamhan , Al-Naqi Meqdad , Sajer Abdulaziz , Hussein Assef , Ni Qinglai , Kumar Surej 
Published: March 2023

Abstract
As a field development strategy, KOC is developing highly depleted reservoir. The field has been experiencing wellbore instability issues. Some recent wells have encountered stuck pipe and mud losses in clastic and carbonate sections. To reduce geomechanical related Non-Productive Time and rig days, it is important that combined effect of in-situ stress state and well trajectory on wellbore stability should be thoroughly investigated. Rock mechanical behaviours also need to be evaluated to optimize drilling practice.
The growing appreciation of the effects of regional tectonics is making it crucial to move away from simplified characterisation of rock behaviour and to turn into advanced geomechanical modelling techniques to engineer better wells and fields. The advanced 3D coupled Geomechanical-Fluid-Flow modelling method combines input data of different origin, such as seismic data, petrophysical data, fluid-flow data and well logs. With such an integrated model, spatial variations of the in-situ stresses in the field are obtained due to reservoir structure, presence of discontinuities as we as because of reservoir depletion. The whole production history spanning seventy years was simulated.
The 3D coupled geomechanical model was able to reproduce the observed wellbore instability events for fifteen wells drilled at different times and various reservoir depletion stages. Drilling instability events included tight spots, cavings and stuck pipe in major clastic sections; and mud losses in carbonate sections. Two blind tests for wells not used for model calibration were carried out to examine the mechanical properties, stress profiles and caliper logs within various formations. The match between the model prediction and the data was in good agreement. In addition, a 3D description of the mud weight was computed, which allowed to obtain drilling maps across the field highlighting zones of high, medium and low drilling risks. Such drilling maps enabled optimizing placement of future planned wells and provide guidance in mud weight design. Nevertheless, drilling through faults requires careful attention due to the localized stresses concentration developing along their geometries. High resolution near wellbore stability analysis helped to optimize the drilling mud weight for wells crossing faults.
The powerful combination of multidisciplinary domains into one integrated 3D geomechanical model improved the understanding of subsurface behaviour. With such an integrated model, complex technical challenges as drilling complexities in the study field can be achieved and hence decreases the Non-Production Time by avoiding problems prior to their occurrence. The calibrated model showed satisfactory predictability for the whole production period and thus is used as a mitigate problem measure to placement of new planned wells.
An Intelligent Safe Well Bottom-Hole Pressure Monitoring of CO2 Injection Well into Deep Saline: A coupled Hydro-Mechanical Approach
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: March 2023

Abstract
Geological Carbon Sequestration (GCS) in deep geological formations, like saline aquifers and depleted oil and gas reservoirs, brings enormous potential for large-scale storage of carbon dioxide (CO2). The successful implementation of GCS requires a comprehensive risk assessment of the confinement of plumes and storage potential at each storage site. To better understand the integrity of the caprock after injecting CO2, it is necessary to develop robust and fast tools to evaluate the safe CO2 injection duration.
This study applied deep learning (DL) techniques, such as fully connected neural networks, to predict the safe injection duration. A physics-based numerical reservoir simulator was used to simulate the movement of CO2 for 170 years following a 30-year CO2 injection period into a deep saline aquifer. The uncertainty variables were utilized, including petrophysical properties such as porosity and permeability, reservoir physical parameters such as temperature, salinity, thickness, and operational decision parameters such as injection rate and perforation depth. As mentioned earlier, the reservoir model was sampled using the Latin-Hypercube sampling approach to account for a wide range of parameters. Seven hundred twenty-two reservoir simulations were performed to create training, testing, and validation datasets. The DNN model was trained, and several executions were performed to arrive at the best model. After multiple realizations and function evaluations, the predicted results revealed that the three-layer FCNN model with thirty neurons in each layer could predict the safe injection duration of CO2 into deep saline formations. The DNN model showed an excellent prediction efficiency with the highest coefficient of determination factor of above 0.98 and AAPE of less than 1%. Also, the trained predictive models showed excellent agreement between the simulated ground truth and predicted trapping index, yet 300 times more computationally efficient than the latter. These findings indicate that the DNN-based model can support the numerical simulation as an alternative to a robust predictive tool for estimating the performance of CO2 in the subsurface and help monitor the storage potential at each part of the GCS project.
Efficient Through-Casing Surveillance of Steam Chamber and Reservoir Oil Using a New Pulsed Neutron Technology and an Advanced Interpretation Algorithm
Authors: Kim Yonghwee , Kotov Alexandr , Chace David , Yuan Peng , Anniyev Toyli , Inanc Feyzi , McGlynn Ian 
Published: March 2023

Abstract
Steam flooding is an essential recovery process in developing heavy oil reservoirs. Operators typically drill and case observation wells to monitor the movement of the injected steam and changes in heavy oil and water saturations. This in-well surveillance is performed using pulsed neutron well logging techniques.
Pulsed neutron well logging technology has been used for more than 60 years to determine formation fluid saturation behind casing. We introduce a next-generation slim multi-detector pulsed neutron well logging tool. The new pulsed neutron tool integrates an upgraded pulsed neutron generator, lanthanum bromide scintillation detectors, and an improved electronics system.
A robust data analysis technique is another vital component of through-casing multiphase formation fluid quantification. A conventional method for analyzing three-phase saturation uses two pulsed neutron logs in sequence. We have adopted a simultaneous analysis approach that combines two pulsed neutron measurements simultaneously to evaluate the volumes of multiphase fluid components.
We present a case study of oil sands produced by the steam-assisted gravity drainage (SAGD) method. We also show comparisons of data acquisition with the previous-generation and new pulsed neutron tools, operating time, and data quality. We acquired time- and energy-based gamma-ray spectra from multiple detectors to extract key pulsed neutron measurements such as ratios of inelastic and capture gamma rays and carbon/oxygen ratios. Time- and energy-spectra-based salinity-independent nuclear measurements were combined to compute three-phase formation fluid saturation. The new tool acquired data of the same quality at least three times faster than the legacy tool. The new tool that offers three improved features (higher pulsed neutron outputs, denser scintillation detectors, and high-speed digital electronics) combined with a new acquisition technology that records time- and energy-spectra-based pulsed neutron data sets simultaneously enables faster reservoir surveillance.
Operators using thermal methods for heavy oil recovery must understand the current underground steam distribution. This affects steam injection optimization and determines subsequent reservoir management activities. A technique for delineating steam, heavy oil, and water through cased monitoring wells was improved by incorporating a new well logging tool, an innovative acquisition mode, and an advanced nuclear data analysis workflow.
A Comparison of Machine Learning Algorithms for Rate of Penetration Prediction for Directional Wells
Authors: Li Chengxi , Cheng Peng , Cheng Chris 
Published: March 2023

Abstract
One of the biggest challenges facing oil and gas companies is to lower the cost of drilling operations. The most critical parameter affecting drilling cost is the rate of penetration (ROP). Improving the ROP and reducing the drilling cycle can be significant for companies to reduce drilling costs and risks, thus enhancing market competitiveness. In the present study, we evaluate the accuracy and effectiveness of different machine learning algorithms for directional wells. We collect many field drilling datasets such as bit type, bit drilling time, revolutions per minute (RPM), weight on bit, torque, formation type, rock properties, and hydraulic and drilling mud properties. We input these data to the machine learning model to be trained, validated, and tested for predicting ROP. The machine learning models we used include linear regression, Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) algorithms. In this study, we implement the Genetic Algorithm (GA) to optimize the hyper-parameters of the five machine learning algorithms. We also apply Savitzky-Golay (SG) smoothing filter to reduce the noise in the original dataset. We use accuracy metrics such as root mean square error, mean absolute error, and regression coefficient (R2) to compare the accuracy of several machine learning models. At last, we select the best-performing algorithm as the prediction tool for ROP. We conduct 50 cases for each machine learning model, where we evaluate the performance of the models and measure the time required for the models to be trained for the prediction tasks. The comparative study shows that implementing the GA optimization algorithm increased the accuracy of individual ROP models. We find that optimizing only a few hyper-parameters can significantly improve the machine models’ accuracy. We also compare the results from the model trained by the data processed with the SG smoothing filter with those trained by the original data. The study demonstrates that the SG algorithm effectively improves the accuracy of the machine learning models. Among the four machine learning models, ANN has the highest accuracy after GA optimization, reaching 97% on average. The overall training time for all four algorithms is between two and four minutes, considered a reasonable time frame for a real-time training and prediction task. We compare several machine learning methods’ accuracies in predicting ROP in real-time. We find that ML-based prediction models, especially ANN with hyper-parameters optimized by Genetic Algorithm, can accurately predict ROP in real-time and provide the operator with suggestions for appropriate measures.