bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bonding Strength at the Cement-Casing Interface in Hydrogen Storage Wells
Authors: Zhan Zhuochen , Wang Junpeng , Taleghani Arash Dahi , Elsworth Derek 
Published: September 2024

Abstract
Given the growing interest in both short- and long-term underground hydrogen injection and reproduction, ensuring an impermeable sealing of the storage space for successful hydrogen containment is critical. Therefore, it is crucial to assess the capacity of borehole cement to maintain geomechanical integrity in the presence of changes in interfacial characteristics resulting from hydrogen exposure at the cement-casing interface. This study investigates how exposure to hydrogen affects the bonding strength and mechanical characteristics at the cement-casing interface in hydrogen storage wells typically remediated through squeeze cement jobs. New correlations link bond strength measured in double-direct shear (DDS) with x-ray micro-computed tomography (µ-CT) and ultrasonic velocity measurements. Compared to cement samples treated with nitrogen, the DDS tests showed a substantial reduction in shear bond strength for cement samples exposed to hydrogen, with a noticeable decrease in maximum shear strength and higher ductility. µ-CT imaging revealed that cement samples treated with hydrogen had increased bulk porosity with noticeable fractures, indicating weakened structural integrity of the bulk cement. These results were confirmed by ultrasonic velocity measurements, which revealed that samples treated with hydrogen had significantly lower P-wave velocities and Young's moduli. Such reductions in strength, indexed through reduced stiffness, highlight the risk of debonding and matrix cracking under repeated injection and production cycles. Importantly, both the cohesive strength at the cement-casing interface and the strength and stiffness of the cement decrease after exposure to hydrogen.
AI-Driven Environmental Data Extraction for Energy Sector Assessment
Authors: Chen Zhenlin , Zhong Roujia , Long Wennan , Tang Haoyu , Wang Anjing , Liu Zemin , Yang Xuelin , Bo Ren , Littlefield James , Koyejo Sanmi , Masnadi Mohammad S. , Brandt Adam R. 
Published: September 2024

Abstract
The oil and gas industry continually seeks to enhance environmental stewardship and reduce its operation carbon footprint, with carbon intensity reduction being a key objective. However, the lack of comprehensive and accessible global real operation data hinders accurate environmental assessment, climate modeling, and decision making. This challenge is exacerbated by the dispersion of crucial information across various sources and the high costs and legal aspects associated with accessing proprietary databases. This paper introduces an innovative framework leveraging Large Language Models (LLMs) - specifically applying GPT-4 and GPT-4o - to extract critical oil and gas asset information from diverse literature sources. Our approach aims to bridge the gap between proprietary and open-source information, creating a more accessible database for different sectors such as industry stakeholders, policy makers, and investors.
Our evaluation process rigorously assessed the LLM's performance, categorizing extractions into true positives, false positives, and false negatives. We employed key metrics including true positive rate, precision, and F1 score to balance the framework's ability to correctly identify relevant information with its capability to find all pertinent data. An iterative fine-tuning process allowed us to address specific challenges in oil and gas data extraction, such as distinguishing between active and production wells.
We developed a novel LLM framework that conducts iterative comparisons between GPT-4's output and a dataset of 108 ground truth documents which were meticulously labeled by domain experts. Through 11 training and testing iterations, we fine-tuned prompts to optimize information extraction. The framework achieved impressive results, with a true positive rate of 81.44% and an F1 score of 76.78% on the testing dataset.
The framework demonstrated significant efficiency gains, processing 32 documents in 61.41 minutes with GPT-4o, averaging 7.09 seconds per extraction. This represents a substantial improvement over traditional manual method. Moreover, the cost-effectiveness of the approach, with GPT-4o reducing extraction costs by a factor of 10 compared to GPT-4, makes it accessible to a wider range of researchers and organizations.
This research has far-reaching implications for the oil and gas industry. By creating an organized, transparent, and accessible database, we aim to democratize access to critical information, potentially revolutionizing how data is extracted and utilized in this field and beyond. This framework could support more accurate climate modeling efforts, enhance decision-making processes for operations and investments, and contribute significantly to the sector's ability to meet environmental commitments, particularly in areas such as emissions reduction and energy transition strategies.
Efficient Drillstring-Wellbore Contact Model and Implications for Real-Time Modeling of Complex Downhole Mechanics
Authors: Wilson J. K. 
Published: September 2024

Abstract
Determining the contact forces between the drillstring and wellbore wall remains a computational challenge; one that prevents real-time estimation of complex downhole mechanics. In order to achieve automation of engineering analysis for real-time dysfunction detection and response, this is a limitation that must be overcome. While current models are capable of providing accurate results for complex scenarios, they can be quite costly, especially for larger drilling assemblies (i.e. full length drillstrings). For reference, a state-of-the-art nonlinear finite element model might take 2 hours to calculate the deflections/rotations and forces/moments of ~20,000+ ft of drillstring. The goal should be the development of a model that can obtain the same result within a minute, if not seconds.
Using a validated nonlinear finite element model as an initial comparison, a new drillstring contact algorithm has been developed that can provide similar results in a fraction of the time. The new approach utilizes a direct integration scheme of forces, moments, rotations, and deflections along the drillstring. Contact forces are then determined iteratively over the length of the drillstring. This results in a performance improvement over traditional nonlinear approaches, which rely on the calculation of incremental displacements to determine the precise location and magnitude of contact forces. The new contact iteration is distinct from previous iteration approaches in that it can process multiple contact points in a single iteration, while previous algorithms are limited to adjusting only a single contact point per iteration.
The results show a 100-300% improvement in computation time, depending on the individual scenario. For smaller drilling assemblies, such as Bottom-Hole Assemblies (BHAs) with total lengths of a few hundred feet, the computation time reduction is generally on the lower end. The larger time savings occur for full drillstring assemblies that are 10,000-20,000+ ft long. Direct comparisons are shown for increasingly complex scenarios, from deflections of BHAs in 3-dimensional wellbore profiles, to full drillstring assemblies in challenging horseshoe wells. The model is further compared directly to measured downhole bending moment data, with a very good agreement between field-measured and calculated values.
This novel wellbore contact algorithm is presented to show what is possible when applying new methods to "old" problems. The new approach has enabled a drastic reduction in the amount of computation time needed to obtain a result. Promising results have set the stage for future development of modeling software and integration into real-time architectures.
Accelerated Carbon Capture and Storage via Mineral Carbonation: A Laboratory-Scale Investigation
Authors: Karabayanova Leila , Hajiyev Murad , Isabel Hernandez Madero Mariam , Sutherland Blake Edward , Harris James Bradley , Qureshi Ibrahim , Hascakir Berna 
Published: September 2024

Abstract
Carbon Capture and Storage (CCS) is globally recognized as a vital, strategic approach to reduce anthropogenic CO2 emissions and mitigate the adverse effects of climate change. Among various sequestration methods, mineral trapping is recognized for its superior safety and extensive CO2 storage capacity. This study presents a novel methodology for assessing the rapid mineral carbonation of CO2 through geochemical interactions with carbon-, magnesium-, and iron-rich minerals abundant in geological formations.
Four minerals: olivine, dolomite, magnetite, and gypsum, have been selected as candidates for the study and exposed to CO2 at various heating rates and temperatures, utilizing Thermogravimetric Analysis/Differential Scanning Calorimetry (TGA/DSC) to establish the optimal conditions for carbon mineralization at atmospheric pressure. Furthermore, the study examined the effect of the aqueous phase with low and high ionic strength on minerals’ carbon uptake efficiency. A novel combined analysis of the minerals was conducted before and after CO2 exposure using Scanning Electron Microscopy/Energy Dispersive Spectroscopy (SEM/EDS) with the normalization by TGA/DSC data to quantify the total carbon uptake by each mineral, thereby identifying the best candidate for mineral carbonation.
Results showed that olivine and dolomite in a dry state displayed the highest carbon uptake values at 200˚ and 100˚ C temperatures, respectively. Thus, it was proved that accelerated carbon mineralization is possible even in the absence of an aqueous phase. However, introducing a water phase with high ionic strength to the mineral surfaces resulted in a significant increase in carbon uptake, with an average rise of 5-15 mg per 100 mg of mineral in olivine and dolomite correspondingly. Furthermore, it was found that dolomite requires more heat to store large amounts of carbon facilitated by the high heating rate of 20˚C/min, whereas olivine demonstrated higher carbon uptake values at a low heating rate of 5˚C/min. Hence, strong correlations between carbon uptake values and the heating rates for both minerals have been established. Lastly, it was concluded that the surface void spaces of the minerals were highly affected by the addition of the aqueous phase to the mineral.
The pioneering approach and findings of this paper reveal that carbon storage can be successfully implemented in a matter of hours under laboratory conditions even at atmospheric pressure, effectively bridging a significant gap in the literature where experimental investigation of mineral carbonation has not been extensively explored. The results of this work provide the petroleum industry with a reliable and safe approach for accelerated CO2 sequestration in various rock formations, thus enabling the companies to successfully meet their sustainability and decarbonization objectives.
State-of-the-Art Modeling Eliminates Drilling Dysfunctions in a Challenging Wildcat Ultradeepwater Wildcat Well
Authors: Batruny P. , Aguiar R. , Cruz R. , Barreto D. , Carbone G. S. , Shafei M. S. , Yusof M. S. , Aziz M. F. Abd , Monte T. 
Published: September 2024

Abstract
Operators have expanded their search for new oil and gas reserves to more complex and challenging environments such as the ultradeepwater, presalt carbonate plays offshore Brazil. The major challenge these wells have faced historically is the well cost overrun from low rate of penetration (ROP) and downhole tool nonproductive time (NPT) related to drilling dysfunctions. To avoid costly trial and error, advanced software was combined with service provider and operator experience to get it right the first time: improving ROP while minimizing damage to the bottomhole assemblies (BHAs), drilling each section on time and on budget.
An advanced 4D (spatial 3D + time) finite element transient drillstring dynamics modeling package is used to design the cutting structures, predict the drilling dynamics, evaluate different BHAs and drilling configurations, and optimize drilling parameters. BHAs for riserless, post-salt, salt, and silicified carbonate sections of the well are analyzed and optimized. Simulations are run to determine proposed BHA configurations and the potential for axial, lateral/torsional, and stick-slip vibration for different rock strengths. The BHAs are then optimized by adjusting cutting structure, stabilizer placement, hole enlargement ratio, and drillstring configuration. Once an optimum BHA is selected for each section, a sensitivity analysis is conducted on RPM, weight on bit, and torque to maximize ROP for each BHA.
Three criteria are used to evaluate BHA performance: stability, robustness/reliability, and measurement quality. The performance of each optimized BHA is compared to the performance of the base case BHA from offset wells. The accuracy of the model's simulations and the optimum recommended parameters is also examined against field results. Overall, the optimized BHAs delivered up to 50% higher ROP compared to the base case from offset wells drilling similar formations. In most cases, the optimum parameters recommended by the model were also the optimum parameters in the field test. The increased confidence in the model from the field tests enabled its use to optimize the BHA further for future wells.
This work presents a digital bit and drillstring design workflow, used to virtually run and analyze different BHA designs prior to running in the field. The work, tested in the field, resulted in multiple learnings shared in this paper, specifically in the realm of drilling mechanics.
Fostering Storage Integrity in Offshore Geological Hydrogen Storage Sites: Insights from Coupled Flow and Geomechanical Studies
Authors: Okoroafor E. R. 
Published: September 2024

Abstract
The primary objective of this study is to address storage integrity concerns associated with underground hydrogen storage in offshore porous media. The focus is on preventing losses caused by the cyclical nature of hydrogen injection and withdrawal. The study aims to anticipate the emergence of storage integrity problems by determining the response of various parameters, which encompass pressure, temperature, displacement, strain, stress, and fluid composition.
This study uses a synthetic reservoir model to explore diverse parameter responses across different phases of hydrogen storage and withdrawal. The investigation commences with a straightforward hydrodynamic numerical simulation model and progressively introduces complexities such as hysteresis, faults, thermal influences, and geomechanics. The values of pressure, temperature, displacement, strain, stress, and fluid composition are tracked at grid blocks that could be potential monitoring sites.
The findings of this study show that pressures are the initial indicators of potential non-sealing faults or fractures. The arrival of fluid through these flow conduits occurs at a later time. This delay in fluid arrival can allow for timely measures to be taken to prevent fluid loss if continuous real-time monitoring is in place. Monitoring above the injection well is suitable for gauging strain, stress, and displacement, as these perturbations reach their maximum levels above the injection or production area.
This study contributes novel insights into storage integrity during offshore hydrogen storage within porous media. By investigating various parameter responses throughout different stages of storage and withdrawal, the study introduces an understanding of pressure perturbations as early indicators of potential faults or fractures. Furthermore, the distinction between the timing of pressure perturbations and fluid arrival offers a valuable opportunity for preventative actions. The study also emphasizes the significance of monitoring location in relation to the leakage site for effective response detection. Finally, the practicality of monitoring above the injection well for specific perturbations is highlighted, enhancing the knowledge base for maintaining storage integrity.
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

Abstract
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

Abstract
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

Abstract
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.