bluuebunny/crossref_metadata_2025 huggingface dataset:
Gas Migration in PMCD Operations: Instrumented Well Study Provides Fundamental Insights
Authors: Samdani Ganesh Arunkumar , Rao Sai Sashankh , Moganaradjou Yashwant , Almeida Mauricio A. , Kunju Mahendra K. , Upchurch Eric , Gupta Vishwas Paul
Published: March 2023

Significant discrepancy exists between the gas migration rates observed during the field applications of Pressurized Mud Cap Drilling (PMCD) and the widely used Taylor bubble velocity correlation. This impacts the fluid logistics planning and design of fluid properties for PMCD applications. Pilot-scale experiments and simulations have shown the importance of wellbore length-scale for estimating gas migration velocity (Samdani et al., 2021, 2022). Therefore, an industry-first well-scale study of gas migration in synthetic-based mud (SBM) was performed using a 5200-ft-deep vertical test-well (9-5/8″ × 2-7/8″ casing/tubing) located at Louisiana State University (LSU) well testing facilities. This test well is instrumented with 4 downhole pressure gauges and distributed temperature/acoustics sensing (DTS/DAS) fiber optic cables which were used to track the migrating gas and to determine its velocity. In a typical test, bottomhole pressure (BHP) was maintained, while gas migrated in a shut-in well. Tests were conducted by varying gas injection rate (10-250 gpm), total gas influx size (10-20 bbl), and BHP (2200-4500 psi). Gas migration rates indicated presence of Taylor bubbles at lower pressures (<2000 psi) and relatively smaller cap-bubbles at higher pressures (>2700 psi). The observation of pressure-dependent flow regime transition in a wellbore is one of the significant outcomes of this study. Changes in gas influx rate also influenced the gas migration velocity as it impacts the gas holdup and the rate at which gas can dissolve in comparison with the injection rate, under the prevailing flow regime. As a result, increase in influx rate led to higher gas migration velocity. A numerical model was also developed incorporating the experimentally observed relationship between pressure and transition of flow regime, to translate the test results into useful information and predictions for field PMCD. For example, the impact of reservoir gas solubility on gas migration rates was determined using this model while using the test-results based on nitrogen gas migration. The model results for reservoir gas migration rates in SBM showed a reasonable match with field-PMCD data under similar conditions.
Correlation of Borehole Images in Conductive and Non-Conductive Muds to Understand Carbonate Heterogeneity: Case Study from Sichuan Basin, China
Authors: Hou Kejun , Zhao Zuoan , Ni Gensheng , Sultan Gamal , Liu Bo , Zhang Tianhua
Published: March 2023

The heterogeneous carbonate reservoir is the most important exploration target in Sichuan basin. It is challenging to identify the potential flow units in the tight carbonate reservoir. High-resolution borehole resistivity image can reveal the dissolution features such as vugs or fractures in the pay zone. Operators in Sichuan Basin have acquired lots of borehole resistivity image using water-based mud imager in key exploration wells to identify potential reservoir. The oil-based mud has become increasingly popular in exploration and development because it reduces complex drilling practices reducing drilling costs. The new generation oil-based imager is used in these new wells. The borehole resistivity image can be obtained from both OBM (oil base mud) imager and WBM (water base mud) imagers. It is important to understand the response differences through the comparisons between different borehole imaging technologies in the same wellbore. The objective of this study is to understand the responses and refine the rule of oil-based mud borehole image interpretation in tight carbonate reservoir.

The operator has logged both WBM and OBM imager in the same wellbore at different times. The WBM resistivity image is acquired in water-base mud conditions in the beginning. Then drilling fluid system was changed to OBM, and 200 meters were drilled further in the borehole. Finally, the OBM imager was logged in the hole. A comparison between the WBM resistivity images and OBM resistivity images are carried out. Increased understandings of the image log responses in this vuggy or fractured carbonated reservoir were obtained.

The borehole resistivity image from WBM and OBM imager is highly correlatable over most of the logging interval as they both show similar resistivity response (bright / dark) on both images for the same geologic feature. The new generation OBM resistivity imager is proven to have similar applications as the WBM resistivity could offer. In fact, the OBM borehole images has higher resolution than the WBM borehole image in most of the intervals, especially in high resistivity formations. It was found that the responses of vugs or fractures can be interpreted slightly different in some intervals. Only the unfilled vug will have resistive response in OBM image while it will be very conductive in the WBM image because it was invaded by the resistive or conductive mud. The responses of induced fractures or natural fractures will be slightly different, which is influenced by formation conditions such as Rt/Rm. The induced fracture could be conductive or resistive under different logging conditions.

This case study improved the understandings of the OBM imager response in tight carbonate reservoirs. These observations can be used as a reference for borehole image interpretation in similar reservoirs.
Evaluation of Slickwater Refrac Treatments Utilizing Mechanical and Formation Diversion Techniques ao Extend Wells Productivity
Authors: Alnakhli Wafaa , Habiballah Zeyad , Baki Sohrat , Alzamil Abdullah , Rueda Jose , Alowaid Abdulrahman , Mechkak Karim
Published: March 2023

The objective of the paper is to evaluate the successful implementation of refrac treatment in unconventional wells with initial completion and stimulation practices. The implementation of refracturing treatment utilizing various diverter systems proved to be operationally feasible, increase productivity, and extend well life cycle.

Slick-water refrac practice relies on the combination of adding additional clusters through e-coil within previously stimulated clusters and utilizing mechanical or chemical diverters to plug dominant clusters, allowing the treatment to stimulate newly added or poorly contributing clusters. A fit-for-purpose criteria was developed for proper diverter selection, number and spacing of additional clusters, diverter drop systems, enhanced pumping schedules, and contingency plans.

The approach had been utilized in several wells, previously stimulated with various fluid systems and job volumes, completed with different completion types and sizes, and through numerous diverter types and drop systems, with notable success from operational and production point of view. Clear indications of effective diversion and stimulation were observed based on pressure response during diverter drop and treatment cycles, as well as an increased wells productivity. In conclusion, the application of refrac approach proved to be operationally feasible while allowing for an enhanced well performance post the refrac treatment.

The implemented combined approach of additional clusters, optimized diverter volumes, adjusted pumping schedules, and multiple diverter drop options offer several benefits in multiple categories: Operational (allowing for on-the-fly addition of diverters and continues cyclic pumping, and preventing the occurrence of screenouts during the refrac treatment), Technical (increasing the stimulated reservoir volume and contact with the formation), and Productivity (enhancing well performance and increasing well life cycle)
Impact of Permeability Heterogeneity Coupled with Well Placement Strategy on Underground Hydrogen Storage Reservoir Simulation
Authors: Alhotan Muhammad , Jia Cunqi , Alsousy Abdulhamid , Delshad Mojdeh , Sepehrnoori Kamy
Published: March 2023

The world is experiencing a transition from traditional to renewable and sustainable clean energy sources. One of the biggest hurdles for this transition is the storage of the excess electricity generated by wind or solar power. Hydrogen has been recognized as a clean-burning fuel that could overcome the barriers to meet this transition. However, there are many challenges associated with hydrogen energy since it requires vast storage volumes. Several new technologies have been proposed to store and produce hydrogen efficiently. One of the most promising storage technologies is Underground Hydrogen Storage (UHS). Reservoir simulation is a critical tool for understanding hydrogen behavior in geological porous media. This study examines the effect of permeability heterogeneity coupled with well placement strategy on several fundamental properties. The simulations are based on a 3D heterogeneous aquifer model with one well as injector/producer. Fifteen-permeability data sets were generated using the Gaussian sequential method using Dykstra-Parson's coefficients (VDP) of 0.3, 0.5, and 0.7, with several realizations. Additionally, the impacts of the well placement strategies in terms of orientations, such as vertical and horizontal configurations, and their locations are investigated. The properties examined are reservoir pressure, hydrogen volume in place and cumulative hydrogen volume recovered. The study uses a real field geological model and a compositional reservoir simulator to conduct these simulations. The published measured hydrogen properties and H2/water relative permeability and capillary pressure curves are incorporated in these simulations. Results indicate that reservoir pressure, the amount of hydrogen in place, and cumulatively produced hydrogen are highly dependent on the heterogeneity of the reservoir and the injection/production well placement. Models with less heterogeneity (i.e., relatively low VDP) show more consistent results on multiple realizations when the well location and orientation are kept constant. On the other hand, high VDP models showed significantly higher variations with different realizations. This paper provides new insights on how to model UHS projects to get the maximum return possible in terms of injected and recovered hydrogen.
An Engineer's Systematic Approach to Achieving Superior Safety Performances on Drilling Rigs
Authors: El-Shamouty Basma , Osisanya Samuel , Rahman Md Motiur , Ghosh Bisweswar , De Athe Mark , Al Jasmi Bekheet
Published: March 2023

Safety on the rig floor must be addressed habitually to avoid tragic fatalities and environmental crises. The objectives of this study were: (1) creating an engineer's system approach named "" to achieve a superior drilling rig safety performance by changing the Health Safety and Environment (HSE) culture and worksite behavior, (2) ensuring proper engagement and commitment of the workforce from the top to the bottom of the hierarchy and (3) to provide a tool that can be utilized on any rig floor. This tool assists in evaluating the risk from three different perspectives: People, Equipment, and Process (PEP). The research covered surveys of offshore oil companies in the Middle East. A total of 16 sites, which included four jack-ups, eight island rigs, and four service areas (mud and cementing units). The feedbacks from the surveys were used to create a risk model and a risk assessment tool, which evaluates the risk factor of hazards resulting from the interaction between people and equipment. Based on the risk factor value and each survey feedback, an episode is created to deliver a very attractive and methodical safety message for the rig-floor workforce. The crew's safety performance has been monitored on a monthly basis to test the impact on the company's’ HSE performance. The results show that had a significant positive impact. A total of 335 days of an injury-free environment was achieved in 2018 while delivering several wells with a lateral length of 25,000 feet in a total of 66 days.

Additionally, the number of incidents was reduced by 85% by the end of 2018. The survey data also show insightful analysis: 50% of the incidents are due to lack of situational awareness; the rig floor equipment that exhibits the highest risk are top drive, iron roughneck, manual tongs, air wench, and lifting gears. The humane factors that indicate the highest risk on the rig floor are people's attitude, awareness, communication, driller's influence, and green hands. The identified opportunity areas of major concern are the Red zone, functional Iron-Roughneck, and regular equipment maintenance. The six by four weeks’ work-leave rotation cycle results in more incidents compared to the four-by-four weeks’ rotation cycle.
Maximizing Productivity Index by Minimizing Fluid, Proppant and Additive Volumes
Authors: Rueda Jose , Alzamil Abdullah , Alowaid Abdulrahman , Valbuena Jose , Alnaimi Rashed
Published: March 2023

As part of the stimulation plan to obtain the optimum slickwater frac design in the main source rock play in Saudi Arabia, a series of systematic trials were carried out testing different frac design parameters, such as the proppant volumes and types, and fluid & additives volumes (friction reducers, spearhead acid etc.). These trials were carefully designed in several 4-well pads across the field, comparing different designs in wells next to each other in the same pad, to allow consistency in reservoir and geology conditions allowing the trials to be conclusive. Wells were flowed back for a relatively short period of time and different production analysis techniques were used to compare the different designs against the standard.

In this paper, the Normalized Initial Productivity Index of the wells was compared using the Gas, Gas Equivalent, Total Fluid Downhole Flow Rates Productivity Index (PI), and the Effective Fracture Lengths from rate transient analysis were used to help on selecting the optimum frac designs. In these trials, other important completion parameters were kept constant: the number of clusters per stage, cluster spacing, type of perforation guns, frac fluid and proppant type. The flowrates are measured using the three phase separators and the corresponding bottom-hole flowing pressures are estimated using multi-phase flow parameters, and the matched wellbore hydraulic models. The reservoir pressure is estimated from the pressure decline analysis of the frac stages along the lateral.

The results show that there was almost no change in the productivity results by lowering the proppant volumes 25%. The results also show that better wells are obtained by lowering fluid volumes by 33% and decreasing the additive volumes by more than 75%. Overall, the improved PI obtained with lower proppant and fluid volumes and less additive concentrations, provided an optimized frac design that is being carried into the development phase wells.

This paper helps in understanding and maximizing the productivity of the wells and optimizing the design including chemicals, to capitalize the value generation from the individual wells on the multi-well pads.
Effect of Grid Resolution on Underground Hydrogen Storage Compositional Reservoir Simulation
Authors: Alhotan Muhammad , Delshad Mojdeh , Sepehrnoori Kamy
Published: March 2023

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

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

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

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

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

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

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

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

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

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.
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

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

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

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

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.