bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishing Best Practices for Well Abandonment in CCS
Authors: Enriquez H. Costeno , Dick H. , Salter R. , Lee S. 
Published: February 2024

Abstract
In CO2 storage sites, wells located within the predicted area of review, and that penetrate the confining zone, may become a leakage pathway out of the injection zone if not properly abandoned. Prior to injection, the abandonment focus is on existing (legacy) wells. After injection has concluded, the focus will expand to include storage development wells, e.g., injector and monitoring wells. Unlike oil and gas developments, where formation fluids are produced, Carbon Capture and Sequestration (CCS) projects inject CO2 into downhole formations, creating a plume that must be contained within the target reservoirs for periods of time that are longer than oil and gas wells’ life span. These differences make it necessary to develop CCS-specific well abandonment practices.
The authors have analyzed industry-accepted well abandonment standards, abandonment recommendations and requirements from regulators for CCS projects, and several case studies and laboratory experiments on well integrity to develop an understanding of the challenges and probable solutions for CCS well abandonment. Then, a recommended practice for well abandonment in CO2 storage sites that addresses isolation of the confining and reservoir zones, the effects of a CO2-rich environment on well materials, and corrective action for legacy wells will be presented.
Most of the existing regulations and standards on CCS establish expectations and objectives for well abandonment without providing detailed guidelines that can be consistently applied across projects. Without consistent rules for CCS well abandonment, project teams will conduct design or planning exercises that will have varying outcomes as they adapt conventional well abandonment guidelines to CO2 storage site requirements. This may result in under- or over-designing the well abandonment; which could translate into compromised well integrity, impact on project value; and an ever-changing technical standpoint. The aim of a recommended practice is to establish simple high-level principles with which to approach well abandonment in CO2 storage sites to facilitate the work of project teams and to have an acceptable level of consistency on abandonment plans.
This paper will demonstrate a comprehensive abandonment strategy for CCS and legacy wells. The strategy addresses CCS-related containment concerns and local applicable abandonment regulations for all other sources of inflow not related to CCS. It discusses how the ability of a confining zone to stop the flux of CO2 through it, that depends on its permeability, thickness and capillary pressure; is relevant for establishing CCS abandonment principles.
Advancing Efficiency in Drilling: Developing a Robust AI Model for Real-Time Bit Wear Estimation
Authors: Luu Trieu Phat , Huang Xu , Zhan Guodong David , Aljohar Abdulwahab , Qahtani Yazeed , Furlong Ted , Bomidi John 
Published: February 2024

AbstractThe main objective of this study is to develop a robust AI model to estimate the level of bit wear during the real-time field deployments. We aim to enhance drilling efficiency by accurately predicting bit wear, considering limited offset training data with minimal subsurface information. The proposed model has practical implications, enabling informed decisions, optimized maintenance schedules, and reduced downtime in drilling operations.We collected drilling parameters (e.g., RPM, ROP), and subsurface data (e.g., gamma) from seven offset wells to train the AI model. We handled missing data and outliers through interpolation. The data were then standardized to ensure consistent scales, and segmented into windows. An LSTM-VAE model was designed and trained in an unsupervised manner. To predict bit wear, the trained AI model was first employed to project each window data into a latent space, similarity scores across runs were then computed, and an XGBoost model was trained to predict bit wear based on relative positions of input data in the latent space.In the field real-time model study, ten bits from five different vendors were examined to assess the performance of the AI model. To test the model's robustness, only Gamma ray logging was utilized for formation interpretation. The AI model successfully predicted the dull status for six out of eight qualified bit evaluations. Furthermore, the model accurately predicted the dull grade for bits from different vendors, indicating its agnosticism to specific bit types. These results validate the robustness and transferability of the developed AI bit wear model, particularly in challenging drilling applications.The novelty of this paper lies in the development of an AI model for real-time bit wear estimation, considering limited inputs and offset training data. The use of an LSTM-VAE model, coupled with XGBoost for estimation, presents a unique approach to address the challenges of accurate bit wear prediction. Additionally, this study introduces the implementation of similarity scores across runs to further simplify the latent space, enhancing the model's robustness.
Application of Deep Neural Network (DNN) in Reservoir Simulation for Hydraulic Fracturing and Production of Unconventional Wells
Authors: Li R. , Fu J. , Gao T. , Zhang P. 
Published: February 2024

Abstract
This study focuses on developing a Deep Neural Network (DNN)-based reservoir simulator aimed at improving the efficiency and precision of hydraulic fracturing and production forecasting. Given the complex and nonlinear nature of reservoir behavior, our goal is to implement an automated, scalable solution to effectively navigate these challenges. The proposed DNN simulator integrates diverse data sources, enhancing a comprehensive understanding of reservoir dynamics and supporting real-time decision-making and operational optimization.
Reservoir simulation, a key numerical method for predicting fluid behavior in porous media like oil and gas reservoirs, is vital for crafting and refining production strategies in the oil and gas industry. A swift and accurate simulation is essential for effective field development.
The study particularly addresses unconventional wells—horizontal wells fractured to facilitate oil and gas flow—where challenges persist in areas like fracture geometry characterization, fluid dynamics between the matrix and fractures, and pressure variation prediction during field development. The scarcity and diversity of data, encompassing both the volume and variety from sources like geology, geophysics, lab tests, and engineering, add complexity to simulating these wells.
We explore the use of Deep Neural Networks, a machine learning algorithm capable of deciphering intricate data relationships, in this context. Recent advancements in machine learning have opened avenues for employing DNNs in reservoir simulation, which is a supplement to traditional time-intensive mathematical algorithms. We constructed and simulated both three-dimensional conceptual models and unconventional field models using this approach. The results validate the deep learning method's accuracy and speed, showcasing its supplementary to conventional numerical reservoir simulators.
Production Forecasting and History Matching of Hydraulically Fractured Reservoirs Using a Pressure Depletion Volume (PDV) Method
Authors: Tian Yakai , Weijermars Ruud 
Published: February 2024

Abstract
The production rate and cumulative production of hydraulically fractured shale wells can be estimated using the pressure depletion volume (PDV) method. The Gaussian Pressure Transient (GPT) is used to compute the pressure depletion in the drainage region of single or multiple hydraulically fractured wells, and the pressure depletion is then translated to production performance. This new approach does not involve Darcy’s Law, and therefore provides an independent method to evaluate well performance. The pressure depletion in reservoir volume between hydraulic fractures is computed by integrating the normalized GPT for the fractured reservoir region, accounting for each individual fracture. Also included is the pressure drop in the nearby reservoir region from pressure changes initiated via the fracture tips. The total pressure depletion of the drained reservoir, can then be computed for each moment in time as an instantaneous analytical solution. The cumulative production is computed using from the comprehensive compressibility coefficient of the drained reservoir space. The daily production rate can then be computed from the time derivative of the cumulative production at any moment in time. To validate the PVD method, the production rate forecasts were history-matched to (1) real production data from the Eagle Ford shale formation, and separately, to (2) synthetic, noise-free CMG-IMEX production data. Both data sets could be satisfactorily matched. The PVD model can also quantify the relative contribution to production from the fracture tips and fracture box region, as well as determine how their relative importance switches over time. The PDV-method proposed in this paper is based on the GPT model, and can predict both the pressure depletion and production performance over the anticipated field life prior to drilling, which is helpful for optimizing completion designs and maximizing economic benefits.
ERD Record in Shallow Weakly Consolidated Formation with Remarkable Efficiency
Authors: Xie Tao , Hou Xinxin , Lin Hai , Zhang Lei , Li Tianyang , Li Zhiheng , Wang Wen , Wang Guiping , Chen Kejin 
Published: February 2024

Abstract
Extended reach wells are drilled to develop the remaining oil in shallow formation just around 600~1400m in China Bohai oilfield. The formation there is terrestrial sediment and heterogeneity is strong, moreover, only water-based drilling fluid can be used as Bohai Bay is an internal bay with weak self-cleaning ability. For these reasons, precise well trajectory control, wellbore cleaning, casing running and cementing are challenges in this region. Facing the problems above, a set of safe and efficient drilling technology for shallow extended reach wells has been developed. Firstly, well profile is optimized referring previous drilling experience, secondly, well trajectory is redesigned using software and directional drilling tools with high buildup rate are selected for precise well trajectory control; thirdly, drilling fluid properties are continuously improved to maintain wellbore cleaning and smooth; Lastly, innovative floating while rotating casing running method and cementing technology are employed to reduce the friction in casing running and avoid leakage during cementing job. The design build rate is about 4 degree/100ft, while the natural inclination reduction rate of formation is serious using classic directional drilling tools that fail to meet this requirement. Thus, uniform wall thickness Positive Drive Motor with 1.5 bend angle and Rotary Steering System of push-the-bit type are selected in surface and second spud drilling to ensure adequate build rate with assistance of software which can track and modify well trajectory while drilling. Water based environmental protection drilling fluid with low viscosity and high shear force are developed to remove cuttings bed and reduce ECD. Meanwhile, the inhibition and plugging of drilling fluid are enhanced to avoid hydration expansion of mudstone. The tripping and back reaming efficiency have been improved by 20%. The drag and torque of casing running in different ways are calculated, innovative floating while rotating casing running method is proposed comprehensively considering the rig capacity and the torsional strength of the casing. Low density cementing slurry system and slurry column structure are optimized to reduce ECD while cementing and cementing quality is excellent. From 2019 to 2021, these technologies have been successfully applied in 7 extended reach wells, with an average drilling efficiency of 97.1%, and the field applications show that the ROP has been improved by 40% while well construction time has been reduced by 28.4% compared with previous operation. Moreover, the horizontal displacement to vertical depth ratio of W1H well is as high as 3.23, breaking the record of Bohai oilfield.
Exploring CCUS Well Construction Using Fit-For-Purpose Cement Designs
Authors: Shine J. M. , Qasmi U. , Elhancha A. 
Published: February 2024

Abstract
As the industry moves towards well construction for underground storage projects, there could be uncertainties on the requirements for long term zonal isolation. The scope of a storage project should help with the well construction FEED though the applicability of utilizing fit-for-purpose cement design could be overlooked as well as overdesigned based upon the notion of "corrosion risks" for well integrity. The cementing design approach should look holistically at the corrosive risks and isolation requirements as opposed to a single solution approach or wort case design independent of the well utility. The manuscript discusses the approach to designing fit-for-purpose cement designs to achieve long term zonal isolation for a future CCUS project.
A Case Study Appling Multi-Well Saturation Height Modeling Combined with Pore Geometry Advanced Petrophysical Rock Types for Integrated Reservoir Characterization and Field Development Studies
Authors: Gunter G. W. , Sahar M. Y. 
Published: February 2024

Abstract and Overview
The objective of this extended abstract is to highlight how integrated studies can be enhanced and reduce uncertainty by applying these new methods including multi-well saturation height functions, advanced petrophysical rock typing methods, advanced core-log integration, machine learning & neural network algorithms. We include a generic reservoir case study showing the improved workflow.
A major challenge in most studies is how to propagate fluid saturation in a 3D model. Using the new Multi-Well Saturation Height Function method allows for improved cored-log integration of Petrophysical Rock Types (PRT) using Open Hole logs. To tackle this issue, an integrated workflow has been deployed which included the following steps (Gunter et.al. 2018, 2020-2022).
Lithofacies, pore geometry, pore types, core data and fluid contacts must be understood. Core-log integration to calibrate core and rock properties from deterministic to probalistic methods should be used and results compared. Petrophysical Rock Types definition and validation at the cored wells are extended to the non-core wells using Statistical/Probabilistic methods. A multi-well Saturation Height Modeling (SHM) is implemented based on Petrophysical Rock Types and matching fluid saturations from well logs. This multi-well approach allows a better estimation of Free Water level, improved understanding of reservoir compartmentalization and reduced uncertainty over traditional single well saturation height models.
The case study shows how this new workflow and application provides an efficient distribution of fluid saturations based on capillary theory, fluid contacts, petrophysical rock types, and pore geometry. Successful 3D models must have an excellent geological representation of the reservoir system including thin section information, mineral composition calibration, pore geometry, capillary properties, and flow capacity of reservoir units. Core log integration must be validated to identify cutoffs and then define petrophysical rock types and permeability equations.
The above enables, from statistical standpoint, successful selection of the inputs logs that would discriminates between the defined rock types through a Linear Discriminant Analysis, which is a critical point in the performance rock types propagation in the non-cored intervals/ wells.
Utilization of multi-well saturation height model improves results, the 3D distribution of the fluids and identifies fluid contacts with less uncertainty, than single well based methods.
Maximization of the Productivity Index Through Geometrical Optimization of Multi-Lateral Wells in Heterogeneous Reservoir System
Authors: Amin Rana S. , Abdulwahab Ibrahim M. , Rahman N. M. Anisur 
Published: February 2024

Abstract
In the recent decades, it is becoming the new norm to drill multi-laterals wells for better reservoir coverage, improved productivity index, and superior financial return on investment. Optimum design and placement of this type of wells can be studied through rigorous modeling. This study presents a multi-parametric optimization approach that optimizes the design of multi-lateral wells and maximizes the contact with highly productive hydrocarbon zones in the reservoir.
Advanced transient numerical models built in 3D by incorporating the dynamic data into the geological model to mimic the transient-pressure behaviors of multi-lateral wells for a given set of geometrical parameters. Such compliant models with dynamic data indeed capture the reservoir description and dynamics. The optimization process is subject to a number of input variables, such as maximum number of laterals, minimum spacing between wells, and maximum lateral length based on the reservoir characteristics. The multi-parametric optimization generates multiple realizations with different patterns. The productivity index of each pattern is calculated to look for the best multi-lateral well to be drilled.
This study presents a numerical methodology of geometrically optimizing multi-lateral wells. Several local optimizations are performed around each main wellbore to place the lateral sections, and to determine the number of lateral sections. Main objectives of all these realizations are to minimize the competition among the lateral sections, and maximize the drainage area, which do subsequently affect the well productivity index. In permeable reservoirs, interference or competition among the lateral sections comes very quickly, and however, in tight reservoirs, such an occurrence is delayed. After running each realization, well productivity index is calculated, and the productivity index graph is generated against each constraint based on the advanced transient numerical model. Graphical presentation of the productivity index helps decide on the best optimum multi-lateral design. Additional sensitivity analysis is presented to show the impact of reservoir heterogeneity, lateral-section lengths, lateral spacing, number of lateral sections, and the impact of offset producers or injectors.
This workflow will help design the most optimum multi-lateral well with a maximum productivity index under different reservoir conditions in an actual dynamic environment. The proposed workflow has been tested successfully.
Deep Learning-Aided Dull Grading Scheme for Drill Bits
Authors: Sun Jia , Guo Huijuan , Zhang Jiawei , Wang Baodong , Zhang Quanli. , Yang Xiongwen 
Published: February 2024

Abstract
Drill bit design plays a key role in drilling operations. A suitable choice of the drill bit parameters (such as the profile shape, the orientation, and the distribution of cutters, etc.) will significantly improve the durability and stability of the drilling process, increase the rate of penetration (ROP) and enhance the drilling efficiency. Traditional strategies of the drill bit design usually involve a trial-and-error process to adjust the parameters, which is usually time-consuming and contains an element of subjectivity. Alternatively, machine learning techniques provide powerful tools to make full use of the collected data, and analyze the relation between the design parameters and the performance of the drill bits on specified rock mechanics data. In the present work, we aim at building a machine learning-aided scheme to help design the parameters of the drill bits.
A machine learning-aided scheme is proposed to perform the drill bit design. First, a dull grading system is established to evaluate the wear extent of the drill bits from images. The grading system includes a cutter localization model to separate the cutters from drill bit images, a reconstruction model to regenerate the 3D geometries of the cutters from several 2D images, a classifier to evaluate the wear extent of the cutters, and an aggregative algorithm to grade the worn drill bits. The grading system is then applied to build the data set of the worn drill bits. Finally, an individual deep learning model is adopted to learn the relation between the design parameters and the wear extent, and a sensitivity analysis is performed to optimize the design parameters.
The research results showed that: (1) the proposed machine learning-aided scheme could produce instructive guides on the drill bit design, reduce the analysis cost and provide accurate prediction on the impact of the design parameters. (2) The 3D reconstruction algorithm could efficiently extract the geometry feature of the drill bits from 2D data, and provide detailed geometry parameters of each cutter. The performance of the 3D reconstruction model is mainly related to the number of corresponding 2D images of a given drill bit and the coincidence degree among them. (3) The classifier based on convolutional neural networks (CNN) could provide accurate wear extent assessments of drill bits.
In this paper a novel machine learning-aided drill bit design scheme is proposed to effectively enhance the efficiency of the evaluation and optimization of the design parameters. A 3D reconstruction method is established to extract the 3D geometry parameters of the drill bits from several 2D images. The deep learning-based dull grading model is proposed to accurately evaluate the wear extent of the drill bits.
Quality Assessment of Downhole Reservoir Fluid Sampling by Predicted Interfacial Tension
Authors: Al-Hamad Mohammed , Matter Tariq , Ma Shouxiang Mark , Abdallah Wael 
Published: February 2024

Abstract
Acquiring a representative reservoir fluid sample is critical for reservoir engineering, production, and petroleum economics. Because a well may be drilled with different drilling fluids, reservoir oil sampled could be contaminated by drilling fluid used. Although some contamination can be characterized relatively easy, others may be difficult to identify and quantify, which could affect decision making. In this paper, we present a methodology to assess reservoir sampling quality with interfacial tension (IFT) prediction while sampling.
In this study, live reservoir crude oils were sampled from different reservoirs, and their basic PVT properties were measured, which showed distinct properties in density, viscosity, and gas-oil ratio. IFT of the live crude oils was measured against that of the respective formation brines, at reservoir conditions, using a specially designed high-pressure/high-temperature cell, and the measured values range from 18.02 to 31.10 mN/m. Using the measured data of crude oil properties and IFT, an empirical model was developed to predict IFT from basic properties, including oil and water density, oil viscosity, and reservoir pressure and temperature, all can be measured downhole using a formation sampling tool. The model was validated using downhole measurements where IFT was predicted, and results indicate that the IFT prediction is very sensitive to drilling fluid contamination and this phenomenon is then used effectively to identify drilling contamination of the sampled reservoir oil.
The findings of this study provide a new methodology to assess the quality of downhole fluid sampling and therefore reduce operating cost and provide quality reservoir fluid samples. The methodology can be integrated as an answer product of a formation tester to identify contamination and confirm the accuracy of the in-situ measured properties, in addition to minimize pumping-out time, saving rig cost and reduce operational CO2 emissions. In addition, the findings of this study provide a new correlation to drive IFT downhole and at reservoir conditions, through the developed correlation.