bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study of Cement Substitution: Demonstrating Suitable Performance Helping to Reduce the CO2 Footprint
Authors: Shine J. M. , Hassan Y. A. , Gbemiga I.
Published: September 2024

Various environmental studies conclude the process of cement production is a significant contributor to CO2 emissions. The oilwell cementing community acknowledges the current practices may not be favorable for long term sustainability and is working towards alternative techniques as well as solutions to reduce the influence on the environment. There are various efforts underway for cement substitution or cement alternatives in an equivalent sack which emphasize mined products in the domain of geo-polymers as well as other suitable cementitious materials as opposed to the traditional methods resulting from manufacturing. A cement substitution study using an alternative cementitious material shows suitable performance when used in an equivalent sack of cement in comparison to a conventional sack of API Class G cement. The manuscript demonstrates the results of the study and how it can be used to help reduce the cement industries environmental footprint one sack at a time.
Enabling Rapid Bit Design Improvements Through Automatic Extraction of Geometric Information of Used Drill Bit from Videos Captured on a Mobile Device
Authors: Safarov A. , Zhang Y. , Cortez J. , Ashok P. , van Oort E. , Stab O. , Gerbaud L. , Velmurugan N.
Published: September 2024

Detailed drill bit geometry information allows for the development of 3D bit rock interaction (BRI) models to study its dynamic drilling performance. Measurement devices that were built to extract such information from drilled bits are large and not easily deployable at scale at a rig site. The goal of this work is to demonstrate the ability to extract such information from unstructured videos captured on mobile devices. Multiple steps exist to obtain the 3D geometrical information of a drill bit from the captured videos. First, the open-source package PYCOLMAP constructs a 3D point cloud of the bit using hundreds of frames which also allows the computation of the image depth and relative coordinates of the objects on the drill bit such as cutters and blades. After obtaining a detailed geometric representation of the drill bit, the noise in the mesh needs to be reduced to achieve a more visually aesthetic 3D representation through the application of several filtering techniques. Following this, the segmentation model, based on a deep learning architecture, extracts all the cutters from the point cloud, which then allows for the calculation of the coordinate and the orientation of the cutters in three-dimensional space.

In this paper, we introduce advanced computer vision techniques for the geometric reconstruction of a dull bit using videos captured on a mobile device. After analyzing cutter geometry, they are compared with the considered ground truth to evaluate the accuracy of the analysis. The 3D model and the derived geometric features are used in the BRI models to understand the drilling dynamics. This helps conduct bit forensics to understand the progression of bit wear and bit damage, and subsequently improve drill bit design and/ or to better control the surface operational parameters to mitigate any unwanted bit-induced drilling dynamics such as stick-slip or whirling.
Estimation of Cementation Exponent m, for Complex Carbonates in Mexico, Using Data Science
Authors: Bassoult L. A. Castellanos , Paredes J. , Rojas M. , Gaytan A. , Alvarado C. , Velasquez A.
Published: September 2024

This paper presents a data-driven approach using machine learning to create a model for predicting the behavior of the cementation exponent "m" (it is a function of the shape and distribution of pores) in Mexican Naturally Fractured Reservoirs (NFRs), aiming to reduce uncertainty in reserves calculations and risks on decision-making for wells lacking core data information. The purpose is to predict "m" with limited measurements by machine learning models trained with core data and special logs from key wells. Different authors have addressed the ranges of "m" based on the type of porosity or rock texture; however, most of these studies are not from Mexican fields. Information was compiled from 1,154 data points obtained from cores in different porous systems, including matrix, fractures, unconnected vugs, and connected vugs. Probabilistic and statistical theorems were used to handle complex and nonlinear relationships between porosity and the cementation exponent "m". Data science revealed patterns that were not evident or were influenced by multiple factors.

Finally, test examples of partitioned porosity data and several measured cementation exponent core well data have been provided to verify the effectiveness and adaptability for the determination of the cementation exponent "m" through the so-called SPP (Statistical Porosity Partitioning) method. This also provides algorithm support for different porous systems (matrix, fractures and vugs), achieving a MAAPE (Mean Arctangent Absolute Percentage Error) of 0.22 between the originals and the predicted values. The first example applying the SPP method is through the development of an equation to analyze a triple-porosity NFR (intergranular, vugs and fractures), successfully predicting the porosity exponent, with an accuracy of around 70% compared to the core-measured data. The second example demonstrates the success of the equation's performance in a dual-porosity NFR (matrix-fractures). Next, the third example was conducted in a well with connected vugs and matrix, achieving a 100% prediction accuracy of the cementation exponent "m" value compared to the core-measured data. Finally, the fourth case shows an example of an exploratory well without any core analysis, and the results of applying our model achieved a better understanding of the reservoir behavior.

The input features for cementation exponent identification are effective porosity and discrete porous system log, with resulting values of 1, 2, 3, and 4, depending on the type of NFR present. The SPP method was then applied to wells lacking specific core information to obtain a cementation exponent "m" exclusively for naturally fractured reservoirs (NFR), thus reducing uncertainty in estimation of hydrocarbon reserves.
A Scalable Parallel Compositional CO2 Geological Storage Simulator
Authors: Di Chaojie , Wei Yizheng , Wang Kun , Shen Lihua , Xue Zhenqian , Chen Zhangxin
Published: September 2024

Storing CO2 in deep saline aquifers is one of the most promising methods for achieving carbon neutrality. Numerical reservoir simulation can assist researchers and engineers in comprehensively understanding and effectively managing the CO2 sequestration process, thereby ensuring its safety and effectiveness. Most of the current CO2 storage simulators use a CO2-brine fluid model which only has water and CO2 components. When impure CO2 is injected, the subsurface fluids form a multi-component gas-brine system, whose phase behavior cannot be accurately described by a CO2-brine fluid model. The current gas-water compositional models include a very limited number of gas components. In this study, we developed a fully implicit parallel CO2 storage simulator for distributed memory computers based on our in-house parallel platform. This simulator uses a fully compositional fluid model which treats water as a component and incorporates it into phase equilibrium calculations. The gas components include not only CO2 but also other common components in captured impure CO2, such as CH4, N2 and H2S. The cubic Peng-Robinson equation of state was used to predict the fugacity and PVT properties of CO2-rich phase. The fugacity in aqueous phase and brine properties are calculated by Henry's law and empirical correlations respectively. Meanwhile, the effect of dissolved gas influence on aqueous viscosity is considered which is ignored by current commercial simulator. The finite difference (volume) method is applied to discretize the compositional fluid model. Numerical experiments show that our simulator is scalable, stable and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster.
Optimizing Image Logging Acquisition with Deep Learning
Authors: Guimaraes C. , Piazza R.
Published: September 2024

With the latest advances in well logging technology, data can now be obtained dynamically during drilling (LWD) with quality comparable to that obtained under controlled conditions, using wireline equipment. When data is successfully acquired during drilling, a significant amount of time and resources are spared by the operator. However, of all the tools typically found in the bottom-hole assembly, the bore-hole image tends to be the most sensitive to the noisy environment encountered during drilling. Considering that wireline acquisitions occur after this period, it should be possible to decide whether to also run the image tool in the wireline assembly, based on the quality of the LWD data collected. Unfortunately, due to the large size of the data, image logs demand a significant time to be processed and analyzed by a professional specialist, typically exceeding the time available between the operations. Furthermore, the evaluation of the images by different professionals is subject to inconsistent interpretations for different operations. Therefore, the objective of the work is to automate and standardize the quality assessment of images acquired via LWD, to support the decision of whether to run a wireline image log.

To achieve this goal, it is necessary to efficiently analyze multiple criteria that would ordinarily be dependent on the speed and subjectivity of human interpretation. The chosen solution involves the combination of convolutional neural networks with feed forward networks, to extract and automatically classify features from the image. Using LWD image logs from eleven different wells and their respective quality annotation logs, provided by a group of professional image specialists, as well as the application of data augmentation techniques, a total of 14,000 dependent and independent variables were generated for the training and validation processes. Additionally, a blind test was conducted to confirm the robustness and reliability of the model.

The automation of image log quality assessment enables real-time decisions to be made. Consequently, unnecessary expenses on redundant acquisitions are avoided and critical resources, such as wireline image tools, are preserved. Additionally, the developed model can prevent the operator from leaving a location without having acquired proper image information, which could ultimately lead to flaws in the reservoir development and in the completion of the well. Finally, the assessment's reliance on a trained model ensures consistency in evaluations across different wells.

A plugin compatible with different petrophysical software has been developed and incorporated into operational routines. This has enabled real-time assessment to be performed in scenarios where it would not have been previously possible. This has consistently led to optimized wireline acquisition designs in terms of costs, resource allocation and well log quality.
Assessment of Geothermal and Hydrocarbon Wells Using Supercritical Fluids for Heat Production
Authors: Ratnakar R. R. , Gupta S. S. , Hackbarth J. , Livescu S. , Dindoruk B.
Published: September 2024

Subsurface geothermal resources are estimated to hold several orders of magnitude more energy than hydrocarbon resources. It is renewable, abundant, and has a small carbon footprint, however, its commercial-scale development faces several critical problems from reservoir to the final delivery point. To resolve these issues, it is essential to understand and analyze the transport and thermodynamics aspects in the geothermal well, which is the main objective of this work.

Here, we extend the previously proposed mathematical model for closed-loop wells in enhanced geothermal systems by including (i) time- and space-dependent fluid/flow properties and temperature, (ii) heat-inflow from formation undergoing transient cooling, and (iii) local-property dependent heat transfer coefficients for the two-phase flow. A detailed sensitivity studies were performed to show the effects of several transport and geometric parameters (such as flow rate, well length, inner tubing and annulus diameters, geothermal temperature, overall heat transfer coefficients) on the thermal output.

The result of this work contains two folds. In the first part, we show that the fluid properties and transfer coefficients are local-property dependent and must be included in the model. The second part was focused on sensitivity of thermal output of the well, where we observe significant difference in the power generation when local-property dependent fluid properties and transfer coefficients were included. Depending on the assumed boundary conditions and other simplifying assumptions, numerical simulations of coupled heat transport and fluid flow in well and reservoir may help predict more accurately the thermal output and the long-term economics, only when essential physics are included in the models.

The novelty of this work lies in non-isothermal fluid flow modeling of coupled well and reservoir with local property-dependent transfer coefficients and fluid (PVT) properties for enhanced geothermal systems. The work is crucial for accurately evaluating the thermal output and economics of geothermal or hydrocarbon wells for power generation, especially with supercritical fluids.
An Invention of Special Liner Completion Technology with Deeper Stimulation Capabilities for Tight Carbonate Reservoir Productivity and Injectivity Enhancement
Authors: R. V. Subba Ramarao , Alshehhi Shamma , Alhebsi Abdulaziz Ali , Mogensen Kristian , Chehabi Wissam , Punnapala Sameer , Al Jasmi Sami , Zubair Muhammad , Chang Ernesto Lenin Barragan , Reddy Ratnakar
Published: September 2024

Tight carbonate reservoir productivity and injectivity enhancement along with sustainability has been a challenge in the oil and gas industry for an extended period. Limited entry liner technology is one of the technologies that can stimulate the horizontal section up to 4 ft only. This technology is a onetime application only and allows near well bore penetration of acid to connect the sublayers and natural fractures within the target zone limiting the sustainability.

New liner completion technology, consisting of partly liner subs to introduce special needles that can penetrate deeper into the formation up to 40 ft, and remaining liner subs with special holes that can jet the acid up to 4 ft is under piloting. This innovative technology has the capability to connect sublayers and natural fractures that improves the poor vertical communication (low Kv/Kh) within the target zone, and simultaneously can stimulate the horizontal section to bypass the near well bore damage. In addition, this technology provides a flexible option for deep stimulation of the reservoir during installation, followed by near well bore stimulation using liner holes as a second step after a certain production duration. This flexibility provides a backup option for regaining sustainability after a production period.

This innovative technology pilot is under implementation at field A. The reservoir is a tight carbonate reservoir with a permeability range of 1-2 md. The present strategy is to install limited entry liner completions as part of the field development. However, the successful implementation of the new pilot with special liner completion expected to increase the production rate by three times, providing the opportunity to revise the field development plan with special liner technology in the upcoming new well drilling campaign. This paper will provide the details of the technology, well selection, completion design, implementation steps, pilot results along with lessons learned for inclusion as part of the next phase of implementations. It will further discuss the well monitoring plan, data collection and performance evaluation of the pilot. In summary, this technology will provide a backup option to conduct stimulation in case the sustainability of well productivity needs to be reestablished and overcome the current limitation of advanced completion stimulation techniques. This technology is an invention that combines deep acid stimulation technique and near well bore jetting with an option to execute them either together or separately.
Horizontal Open Hole Gravel Pack Challenges and Lessons Learned from More than 60 Offshore Wells Performed in Peregrino Field
Authors: Colbert F. C. , Pedrosa H. A. , Boldrini A. C. , Garcia F. M. , Rezende D. M. , Carrara M. , Gois S. C. , Steensland O. , Santos A.
Published: September 2024

The Campos basin is a sedimentary basin located in offshore Brazil, between the north coast of Rio de Janeiro State and the south coast of Espírito Santo State and is composed of several fields. Among, Peregrino is made mainly of a high-permeability sandstone reservoir in a water depth of 120 m, producing a 14° API grade oil at a moderate reservoir temperature (60° C). To maintain the reservoir pressure, the production drive mechanism is composed by reinjection of produced water and pressure support from the aquifer. Artificial lifting method is required to improve production. The well construction design used, to keep production economically feasible, was to drill and complete long horizontal wells passing through the reservoir.

In unconsolidated sandstone reservoirs, sand control methods are mandatory due to the risk of sand production. In the case of Peregrino field, oil producing wells required the use of screens and gravel pack while injector wells required stand-alone screens. Thorough engineering was required, and many lessons learned were acquired during the gravel packing of 66 wells since the first well was completed in 2011. Although the wells are part of the same field, different completion scenarios were observed in each well, requiring specific design in conjunction with the lessons learned and the best practices to overcome those challenges.

Considering that most of the wells have long open-hole lengths from 1,300m to almost 2,300m, a big challenge was the high friction during the alpha and beta wave propagation. The balance between increasing the pumping rate to prevent proppant settling and the increase of friction pressures without fracturing the formation was a key to the success. Beyond this challenge, many others were faced such as high losses prior to the pumping, shale areas in the interval, use of autonomous inflow control devices (AICD) screens, changes in proppant type and concentration strategy, vessel mobilization strategy change, introduction of a new rig in the field, etc. The success of this project is clearly based on gravel pack packing statistics as ∼95% of the completed wells were fully packed and on the improvement in the operational safety over the years of operations.

This paper outlines and describes the design, completion and lessons learned that were used in the past 13 years of gravel pack operations in the Peregrino field, which can be used as reference and benchmark for other offshore horizontal open hole gravel pack projects around the globe. To a large extent focus on simple and robust solutions and operations. Peregrino has contributed with many good learning points for gravel pack operations.
Fast History Matching with a Customized Physics-Based Data-Driven Flow Network Model GPSNet: Application to a Giant Deep-Water Gas Field
Authors: Guan X. , Wang Z. , Ren G. , Hui M. , Kazanov D. , Wen X. -H.
Published: September 2024

History matching with full-fidelity reservoir models could be a computationally expensive task. We have previously implemented a novel physics-based data-driven surrogate model (GPSNet) and used it for rapid history matching and optimization for waterflood and steamflood applications in mature fields. In this work, we construct a GPSNet model with customized network configurations to capture reservoir volume and connectivity distributions with associated uncertainties and demonstrate a successful application to a deep-water giant gas reservoir in Northern Carnarvon Basin offshore Australia.

This GPSNet model incorporates both current wells in-production and pseudo wells placed across the reservoir. A flow network is constructed using a network of one-dimensional (1-D) connections that link production-production wells, production-pseudo wells, and pseudo-pseudo wells. Both pseudo wells and connections in the network were designed following our best understanding of reservoir connectivity and uncertainty. Connections are grouped into different categories to account for regional variations at different scales. Reservoir properties are calibrated from historical data through a sequential history match process typical for GPSNet modeling: The average reservoir properties of each connection group were first inferred using Design of Experiment (DoE) and Generic Algorithm (GA); Then, excellent history matching results were achieved by adjusting pore volumes and transmissibilities to match build-up pressures and by adjusting completion-level producer well index and non-Darcy effects to match flowing bottom hole pressures using Ensemble Smoother with Multiple Data Assimilation (ESMDA).

With 200 connections among 39 producers and pseudo wells, the typical run time of this GPSNet model is several minutes which is almost two orders of magnitude faster than a full-physics 3-D model. The integration with a commercial simulator also makes it feasible to extend GPSNet to depict complex processes such as non-Darcy effects and well control logic. GPSNet is also flexible and can be customized to capture regional variations with different uncertainty levels which was a key factor in improving the history matching quality. The history-matched models can be used for fast and reliable decision-making as well as reservoir management.
First Worldwide Application of New Generation Single-Phase Retarded Acid in High-Temperature Carbonate, Mexico
Authors: Munoz Y. , Chavez M. Gonzalez , Cerron D. , Artola P. , Andrade E. , Macedo L. Vazquez , Valtierra J. Camarillo , Altuve A. Gomez
Published: September 2024

This work presents the first worldwide application of a new-generation, single-phase, low-viscosity retarded acid developed to effectively stimulate wells in High-Pressure, High-Temperature deep carbonate reservoirs. While single-phase retarded acid has shown success since implementation, the retardation performance is compromised at temperatures above 300°F, limiting acid concentrations to 20% HCl or lower depending on the bottomhole static temperature and well conditions. A new generation of single-phase retarded acid (Danican et al, 2024), capable of performing at temperatures up to 350°F was employed for the first time to stimulate deep and hot carbonate formations in Mexico.

The well selected for this first-time application is an openhole completed in a naturally fracture dolomitic reservoir with 905 meters openhole section and 367°F bottomhole static temperature. After dedicated laboratory test to qualify the fluid and complete placement computer modeling, we successfully executed a matrix stimulation treatment with the new single-phase retarded acid as main stimulation fluid. The initial production test, immediately after the stimulation, resulted in 3.6 production-fold increase; exceeding expectations and demonstrating that the acid system employed could create effective wormholes at this challenging conditions. This success has created opportunities to enhance stimulation outcomes in similar completions in this and other fields.
Natural Radionuclides Behavior of 238U, 232Th and 40K in the Prometeo-1 Well: A Case Study from the Middle Magdalena Valley Basin, Colombia
Authors: Echavez R. A. , Cardenas J. C. , Obando A. A. , Rueda J. P. , Perez E. R.
Published: September 2024

The development of research about preservation of the human health and environment have taken a relevant place in several industries around the world. In Colombia, recently, has been developing some studies respect to the behavior of natural radionuclides in the hydrocarbon areas in zones of interest at the Middle Magdalena Valley Basin (MMVB), these results have been considerably variables and dependents of the multi elemental composition, type of rocks, rock-fluid interactions, among others. The Prometeo-1 Well, has showed values of radioactivity concentrations from the chains 238U, 232Th, and 40K between 58 – 82, 14 – 67, and 185 – 287 Bq/kg, respectively, from formation rocks at depth between 8180 and 9260 ft; these results are related with concentrations of chemical elements such as calcium, silicium, and aluminum, and minerals such as calcite, quartz, and pyrite. The relationship between radioactivity concentrations and multi elemental composition is so important in the results for this well in the Absorbed Doses Rate (92 nGy/h) and the Annual Effective Dose Equivalent (112 µSv/y); these values are very close to the world average and suggest a specific review to know the true radiological risk. In this way, this work shows an evaluation of the natural radionuclide behavior in rocks from the Prometeo-1 Well through measurement of radioactivity concentration using gamma spectrometry in the formation members Lower Lidita and Salada, subsequently, with the results obtained using XRF and DRX a concentration relationship was made allowing to know the radioactive content, besides, the results in both formation members were compared, measuring the radiological risk using defined math equations. Finally, the results allowed to obtain a general view of point about natural radionuclides behavior in the well and create a technical criterion related to a possible radiological risk, waiting the definition of the radioactive baseline at the MMVB.
Optimizing Hydrogen Storage in the Subsurface Using a Reservoir-Simulation-Based and Deep-Learning-Accelerated Optimization Method
Authors: Eltahan Esmail , Albadan Deema J. , Delshad Mojdeh , Sepehrnoori Kamy , Alpak Faruk Omer
Published: September 2024

This paper addresses the challenge of optimizing subsurface hydrogen storage in porous media, a crucial component for advancing energy transition. The multifaceted nature of this challenge stems from the complex physics governing the process, coupled with operational limitations, and subsurface geological uncertainties. We apply a stochastic gradient-based optimization method with novel deep-learning acceleration components, tailored to maximize the efficiency of hydrogen storage by tuning well locations while honoring operational constraints.

The key objective of optimization is to maximize the amount of recoverable hydrogen while maintaining operational constraints. We adopt a robust optimization approach that maximizes the mean objective function over a set of realizations representing subsurface uncertainty. The objective function, defined as the hydrogen deliverability index, is calculated using a compositional reservoir simulator with high-resolution grids to minimize numerical dispersion. Our approach leverages a deep-learning-accelerated-gradient (DLAG) method alongside these simulations. This method is applied to the Brugge field case study, which is divided into two distinct optimization scenarios. In the first case, we evaluate the effectiveness of the optimization method with only one subsurface realization, optimizing the placement of eight storage wells and comparing outcomes with and without the application of DLAG. In the second case, we extend the analysis to include five different subsurface realizations and impose specific location constraints on each of the storage wells to optimize their placement. In the first case, the application of the DLAG method showed a clear advantage over the non-DLAG approach, resulting in faster convergence. The optimization of hydrogen storage well locations in the Brugge field model yielded notable improvements in storage efficiency, demonstrating the practicality and effectiveness of our approach.
Novel Advances in Under-Balanced Coiled Tubing Mechanical Descaling Operations Using Fiber Optic Real-Time Telemetry
Authors: Alaskar S. Y. , Waheed A. S. , Aljehairan O. H. , Azly A. A. , Alyami M. , Tapia W. , Naveed M. A. , Alsulays Ali
Published: September 2024

Downhole scale removal is known to be an uneasy task but removing scales in wells with sub-hydrostatic pressures puts it in a different league of challenges. Conventional descaling techniques fall short in sour wells due to cases of uncontrolled hydrogen sulfide (H2S) gas releases at surface, which is a major Health Safety and Environment (HSE) issue, topped with the inability to maintain fluid circulation which causes formation leak off and inevitably induced reservoir damage. This paper discusses under-balanced coiled tubing intervention of live wells for descaling operations.

The descaling technology has evolved on the backs of the recent advances made in real-time downhole monitoring capabilities using fiber optics. This system includes a bottom-hole assembly (BHA) with a full array of real-time sensors (pressure, temperature, casing collar locator, gamma ray, load measurements), and powerful wellbore cleaning tools that can penetrate and remove hard scale deposits. These BHAs’ are designed to withstand high level of shocks and vibrations with bottom-hole temperatures exceeding 300°F. Assisting the fiber-optic telemetry is a fluid hydraulics simulation software that accounts for solids (i.e. scales removed) loading in the fluid stream in equivalent circulating density (ECD) calculations and maintaining a predetermined underbalance condition throughout the descaling process. Since there is high chance for H2S leakage on surface in an underbalanced, flowing well condition, it becomes essential to have a gas-tight, error-free well testing equipment package deployed to meet this challenge.

Presented in this paper is a detailed account of the operational planning, risk assessment, and execution of the underbalanced CT descaling operation. The under-balanced approach proved instrumental in restoring wellbore accessibility and productivity, surpassing the limitations of conventional acid de-scaling methods. Comparisons between conventional descaling and the under-balanced operation highlight the advantages of the latter, including improved efficiency, cost savings exceeding 20%, a 30% production gain, and full restoration of well accessibility. The under-balanced method showcases its adaptability in addressing challenges posed by hard scale accumulations, demonstrating its superiority over conventional techniques. This paper contributes valuable insights into the evolution of descaling interventions techniques, showcasing the success of under-balanced operations as a transformative approach in the realm of live descaling methods. The comparative analysis provides a comprehensive understanding of the operational efficiencies and benefits associated with adopting innovative under-balanced techniques for well interventions.
Drilling Fluid Design for Safe, Efficient Resource Delineation in XHT Geothermal Reservoirs
Authors: Bageri Mohammed , Cascone Alessandro , Vordick Travis
Published: September 2024

The development of advanced drilling fluids capable of withstanding Enhanced Heat Transfer (XHT) conditions in geothermal reservoirs is critical for the safe and efficient exploration and production of geothermal resources. This study presents innovative fluid formulations designed to enhance drilling performance in XHT geothermal reservoirs. The research addresses challenges such as fluid stability, formation integrity, and effective lost circulation control. The primary objective is to develop and validate drilling fluids that maintain stability and performance at extreme temperatures, specifically in supercritical geothermal environments.

Laboratory tests and field applications validated the performance of these advanced drilling fluids. The methodology included the selection of base fluids, viscosifiers, and additives through a Traffic Light Analysis (TLA) approach. Key components identified were High-Temperature Stabilizer and Thermal Enhancer, due to their thermal stability and mechanical properties. The fluid's performance was further enhanced through polymer temperature extenders and advanced fluid loss control additives. High-Temperature High-Pressure (HTHP) viscosity measurements and Scanning Electron Microscope (SEM) analyses assessed the fluid's rheological properties and microstructural characteristics.

Results demonstrated that the new formulations-maintained viscosity and stability at temperatures up to 800°F, significantly outperforming traditional fluids that typically degrade at 400°F. Field trials, including the Iceland and Italy, confirmed the formulations’ effectiveness. These trials recorded improved fluid stability, with the Geothermal Specific Fluid (GSF) system maintaining a plastic viscosity (PV) of 9.9 cP and yield point (YP) of 39.8 lb/100ft2 at 120°F, which remained stable after aging at 706°F for 96 hours.

These advancements highlight the potential for these advanced drilling fluids to revolutionize geothermal drilling, making it more effective, economical, and environmentally friendly. The GSF system's ability to maintain stability and performance at extreme temperatures, improve fluid loss control and potentially reduce the risk of non-productive time related to fluid quality represents a significant leap forward in geothermal drilling technology. This approach not only meets the immediate needs of geothermal drilling but also provides a robust and efficient solution that significantly enhances operational performance and cost-effectiveness.
A Field Application of Machine Learning for Sonic Log Prediction and In-Situ Stress Estimation in Natural Buttes, Uinta Basin, Utah
Authors: Kim Changjae , Morales Adrian , Oberg Darin , Black Brooks
Published: September 2024

It is necessary to use sonic logs in gathering rock mechanical properties which are used to estimate in-situ stresses which are useful for fracture design and reservoir characterization. However, the high cost of its acquisition deters companies from getting complete well measurements and in some cases, we may have incomplete sonic log data due to tool damage, poor calibration, and other reasons. In other cases, the well of interest might only have a triple combo suite to determine target depths and left up to engineering judgment what a hydraulic fracture might look like.

This study shows a field application of machine learning (ML) algorithms using triple combo logs to predict elastic properties and finally estimate in-situ stress profile in the Natural Buttes gas field. We used a complete set of triple combo and dipole sonic logs to train K-Nearest Neighbors (KNN) regression and Bi-directional convolutional long short-term memory (Bi-ConvLSTM) models. A data preparation and transformation technique were applied to reduce heteroscedasticity by manipulating data distribution. Then, prediction-performances were compared to each other, and a selected ML model went through a validation to verify it effectively captures existing patterns in the well log data. The selected ML model was finally deployed into a GUI interface which is helpful for users to create predictions, visualize and analyze the generated results.

Comparative analysis of ML models shows that the KNN mitigates overprediction observed in the Bi-ConvLSTM. The KNN reduces training time while the Bi-ConvLSTM is computationally expensive as it considers well log data as an image. We also quantify uncertainty of KNN predictions through a heuristic approach to provide uncertainty thresholds as decision boundary for in-situ stress estimation. For calibration verification, we present one data set showing the predicted sonic velocities vs actual measured velocities as well as the predicted vs actual elastic and in-situ stress profile. The GUI can deploy trained models, take test data for prediction, visualize triple combo logs, predicted sonic logs, calculated elastic properties and in-situ stresses with variances for analysis. We conducted fracture modeling using a commercial numerical simulation tool for both the measured and synthetic cases and found the SRV was similar to each other.

This study demonstrates that the use of machine learning combined with well-established data transformation, uncertainty estimation, and visualization can reduce time and cost in extracting and analyzing a valuable information. Based on literature review, this is the first time that such a workflow has been shown up to the in-situ stresses portion of the workflow. We finally, perform hydraulic fracture simulation on the synthetic in-situ stress profile case and show equivalent results to the calibrated in-situ stress profile case.
Reassessing Free Water API Testing Procedure
Authors: Mahmoud Ahmed Abdulhamid
Published: September 2024

Accurate determination of the top of cement is critical for the success of oil well cementing operations. The American Petroleum Institute (API) free water test is a standard procedure designed to assess the free water content in cement slurries. However, this study reveals significant limitations in the API free water testing procedure, particularly for cement slurries with very low density achieved by adding more water than recommended by API guidelines. Investigation involved comparing the results of the API free water test, which is conducted over a 2 hours period, with an extended 24 hours test. The findings indicate that for the low-density slurries, the 24 hours samples exhibited a considerably higher amount of free water compared to the 2 hours API test results. This increased free water presence significantly reduces the height of the cement column, which is not accurately captured by the standard API procedure. These discrepancies highlight the inadequacy of the current API free water test for low-density cement slurries and underscore the necessity for reassessing and potentially modifying the testing standards to ensure accurate top of cement. This study provides a critical evaluation of the API free water test, emphasizing the importance of longer testing durations for the lightweight slurries to capture true free water content and suggesting improvements for more reliable and effective well cementing operations.
Incorporating Self-Degradable Fibers into High Performance High Strength Particulates to Combat Losses While Drilling Across Reservoir Section in Extended Reach Well in Middle East Field
Authors: Bamiduro Abayomi , Peres-Demarthon Guillaume , Phyoe Thein Zaw , Pallapothu Surya , Pauferro Paulo , Aghaguluyev Javid
Published: September 2024

Lost circulation on drilling wells within the fractured limestone reservoir results in a significant loss of time when treating the losses. Furthermore, lost circulation can be detrimental to well productivity due to the damage caused by the influx of lost circulation material (LCM) into reservoir sections. Drilling fluid in these sections is replaced with sea water to mitigate formation damage and to reduce the hydrostatic pressure of the fluid column at the loss zone. Despite using sea water, total losses are typically encountered in these sections. Several conventional LCMs, fibrous and cementitious LCMs have historically been used to mitigate these losses. However, these LCMs could potentially reduce future well productivity and tend to plug surface facilities / equipment during production. Therefore, the application of an effective LCM that would cure the losses while minimizing the formation damage is required.

A novel self-degradable fiber-based pill with acid soluble particulates specifically designed to cure losses in the reservoir section. Once placed, the pill forms an impermeable plug inside a fracture that is stable until the completion of the well. Based upon wellbore temperature and pressure, the fibers will self-degrade to prevent any long-term formation damage in the reservoir section that could impact future production. Laboratory tests indicated that a plug formed from the pill seals a fracture width up to 10-mm and withstand differential pressure as high as 1000 psi. The tailored pill design allows for complete self-degradation of fibers and particulate can exhibit solubility with the presence of acid following downhole placement of the pill. This pill was designed to be pumped through either a dummy bottomhole assembly (BHA), through bypass circulation ports above the BHA or an open-ended drill pipe.

The self-degradable fiber-based pill with bridging particles was successfully pumped across the 8 ½-in reservoir sections. The plug was placed through bypass circulation ports by spotting the pill above the loss zone. Following treatment, the loss rate was effectively reduced by more than 95% allowing drilling to continue with drilling operations and without any major impact on future well productivity. The self-degradable pill was used for multiple loss circulation treatments with similar results.

Deploying the pill at a different depth proved its applicability under different conditions developing into a standard engineered approach for curing losses in reservoir section. Incorporating fiber with degradable property and bridging materials proved the effectiveness of the novel self-degradable fiber to mitigate losses across reservoir zones in Middle East.
Advancing Proxy Modeling in Reservoir Simulation: A Multi-Step Embed to Control Approach
Authors: Chen Jungang , Gidlin Eduardo , Killough John
Published: September 2024

Reduced-order models, also known as proxy model or surrogate model, are approximate models that are less computational expensive as opposed to fully descriptive models. With the integration of machine learning, these models have garnered increasing research interests recently. However, many existing reduced-order modeling methods, such as embed to control (E2C) and embed to control and observe (E2CO), fall short in long-term predictions due to the accumulation of prediction errors over time. This issue arises partly from the one-step prediction framework inherent in E2C and E2CO architectures. This paper introduces a deep learning-based surrogate model, referred as multi-step embed-to-control model, for the construction of proxy models with improved long-term prediction performance. Unlike E2C and E2CO, the proposed network considers multiple forward transitions in the latent space at a time using Koopman operator, allowing the model to incorporate a sequence of state snapshots during training phrases. Additionally, the loss function of this novel approach has been redesigned to accommodate these multiple transitions and to respect the underlying physical principles. To validate the efficacy of the proposed method, the developed framework was implemented within two-phase (oil and water) reservoir model under a waterflooding scheme. Comparative analysis demonstrate that the proposed model significantly outperforms the conventional E2C model in long-term simulation scenarios. Notably, there was a substantial reduction in temporal errors in the prediction of saturation profiles and a decent improvement in pressure forecasting accuracy.
Investigating Dielectric Property from LWD Propagation Measurements a Modeling Study
Authors: Zhang J. , Ma S. , Al-Ofi S.
Published: September 2024

Formation dielectric property that is highly affected by formation fluid saturation can be an additional physical parameter for geo-steering in low resistivity pay (LRP) formations. Unlike wireline dielectric tools, LWD propagation tools operate at much lower frequencies where formation dielectric constant estimation is challenging. Thus, the sensitivity of dielectric properties for different tool's operational parameters should be investigated, as the main objective of this study.

In this paper, finite-element numerical modeling was validated and used to calculate the electromagnetic (EM) fields in a cylindrical borehole and homogenous isotropic formation. The EM propagation is governed by a set of antennas including a transmitter and two receivers geometrically modeled as in current LWD tools. During the sensitivity analysis, different operational parameters such as tool diameter, frequency, antenna spacing, borehole size, mud and formation properties were varied. Signal attenuation and phase shift across the two receivers were utilized in this study. The sensitivity of attenuation and phase shift were assessed against varying formation dielectric properties for each combination of operational parameters.

The accuracy of the numerical modeling results was validated against an established magnetic dipole solution. For a standard tool configuration and operational frequencies, i.e., 2 MHz, the sensitivity of formation dielectric constant on simulated attenuation and phase shift increases rapidly as formation resistivity is higher than 100 ohmm and dielectric constant is higher than 20. Also, the higher the frequency the better the sensitivity to the formation dielectric property. When the frequency is higher than 10 MHz, the measurement sensitivity to the formation dielectric constant is dramatically improved and thus makes it applicable for well placement in the low resistivity LRP formations. Results also indicate that the shorter Tx spacing may help the dielectric constant sensitivity but mainly improve the signal-to-noise ratio. Meanwhile, the Rx spacing does not influence the dielectric constant sensitivity significantly.

The findings of this study further confirm our recent study (Al-Ofi et al., 2024) and open a path for advanced geosteering in complex drilling conditions where some bottom-hole assembly may be limited and restricted such as in a slim hole under balanced coil tubing drilling, or when other LWD sensors cannot perform effectively.
CCUS Well Integrity Success Using Fit-for-Purpose Cement Designs
Authors: Shine J. M. , Sowailem A. W. , Bechah Z. , Qasmi U. K.
Published: September 2024

Defining long-term zonal isolation requirements for any carbon capture underground storage (CCUS) project life cycle is important to support well integrity success. The utility and scope of an underground storage project helps guide the isolation requirements for the well construction phase emphasizing the well design and cementing. Within the domain of cementing, a basis of design approach holistically can evaluate the corrosive risks, isolation requirements, cement installation, and degree of integrity for the anticipated well utility or load cases. The manuscript highlights the results of a field deployed and validated cement design being able to withstand CO2 corrosive exposure helping to achieve zonal isolation for CCUS well construction while managing the anticipated load conditions to ensure wellbore integrity. Several case histories, a 6-month corrosive study performance analysis, and cement integrity modeling support the fit-for-purpose designs helping to deliver well integrity success.