bluuebunny/crossref_metadata_2025_split huggingface dataset:
Five Keys to Shortening the Innovation Cycle - A Case Study from Cancer Genomic Sequencing to Unconventional Reservoir
Authors: Yamali Nampetch , Emery Daniel 
Published: October 2023

Abstract
Due to society’s increasing interest in the impacts of climate change, a new sense of urgency is driving the demand for shorter, successful innovation cycles with increased need for greater technology impact and global scalability. To make this happen, a comprehensive strategy to manage ambiguity, potentially competitive technical environments, and consequential communication hurdles from cross-industry backgrounds are required.
For over two decades Chevron Technology Ventures has been hard at work on accelerating our innovation cycle through a specific leadership approach. We have observed five connected challenges frequently encountered when taking an idea from inception to full-scale adoption. We will present this method through looking at one of our projects, adapting genomic sequencing from medical cancer research for drainage heights and well-to-well communication for unconventional reservoirs.
This case study will help diverse technical leaders gain direct insight into the 5 keystrategies which we’ve foundshorten the innovation cycle.
Proven strategies Include: Forming a dedicated team incorporating an energized champion, along with both technical experts, as well as resources from the business unit targeted for initial deployment.Managing technology ambiguity with key stakeholdersMitigating the potential competitiveness that can stifle innovation and progressBridging communication challenges due to diverse technical backgroundsGrowing healthy relationships and promoting a one team culture
This is the firsttime that thiscutting-edge research from the medical industry has been adopted and delivered results in an oil field application. The potential benefits of scaling this technology can aid in optimal unconventional resource development. The strategies presented can help technical leaders and practitioners champion innovation within their organizations.
Predictive Power of Theoretical Adsorption Models for Hydrogen/Coal System: Implications for Hydrogen Geostorage
Authors: Raza Arshad , Mahmoud Mohamed , Arif Muhammad , Glatz Guenther , Alafnan Saad , Hassan Amjed 
Published: October 2023

Abstract
Experimental methods are commonly used to determine the ability of a rock to adsorb gas by means of isothermal adsorption experiments. Adsorption isotherms quantitatively describe the relationship between the amount of gas adsorbed onto a rock surface and associated equilibrium conditions. It is imperative to study the adsorption of hydrogen (H2) on coal for various pressure and temperature conditions to de-risk subsurface hydrogen storage. This study aims to provide a theoretical analysis of the adsorption behavior of hydrogen onto coal, based on representative models in order to assess the suitability of theoretical isotherms. Specifically, we employ a modeling strategy to quantify the applicability of the Langmuir, Tóth, and BET framework for a hydrogen/coal system based on literature data. The novelty of the work derives from that fact that the predictive power of well-known gas adsorption isotherms for experimental H2-coal adsorption data has not been studied yet. To assess storage security and economics via modeling, in situ hydrogen storage requires suitable isotherms. Here we consider data for pressures up to 102 bar and temperature in between 303-333 K. Findings suggest that in a RMSE-sense, adsorption isotherms may be ranked as Langmuir&gt;Tóth&gt;BET for the coals studied. The results of this study contribute to an improved understanding of the predictive powers of adsorption isotherms and provide valuable numerical input variables for modeling coal seam formations at the reservoir scale, facilitating history matching and enabling predictions of formation behavior.
Leveraging Autonomous Moon-Based Rovers for Multiple, Near-Term Applications in Field Operations
Authors: Cyrus Justin , Wagner Van , Kenrick Joseph 
Published: October 2023

Abstract
Lunar Outpost is an advanced technology startup specializing in the development of sophisticated robotic platforms for both space and terrestrial applications. Recognizing the need for automated site inspection and observation in industries such as upstream oil and gas, Lunar Outpost has developed the ‘Hound’ rover—a terrestrial robot designed for monitoring and gathering operational measurements of various field assets, including wellheads, flowlines, separators, tanks, pump stations, and pipelines.
This paper describes the research and development efforts behind the Hound rover, focusing on the discovery of end-user requirements to effectively address industry pain points. The development process involved extensive stakeholder engagement, allowing for the identification of critical needs and challenges faced by the industry. The resulting platform was designed to meet these requirements and underwent rigorous verification and validation processes to ensure its capability and effectiveness.
The research findings informed key decisions regarding prototyping, design, and the implementation of various features and capabilities. Each of these elements underwent thorough verification or validation, confirming their alignment with the original needs expressed by industry stakeholders. By addressing these needs, the Hound rover offers a comprehensive solution for autonomous monitoring and observation in the oil and gas industry.
Furthermore, a comparative analysis was conducted to assess the advantages and disadvantages of implementing the Hound rover compared to existing operational procedures and other robotic platforms in use today. This analysis highlights the cost-effectiveness, improved visit frequency, enhanced observation capabilities, and strengthened regulatory compliance offered by the Hound rover. By replacing traditional site visits with autonomous systems, operators can achieve significant cost reductions, increase operational efficiency, and ensure compliance with safety and environmental regulations.
The development of the Hound rover represents a transformative opportunity for the oil and gas industry. It addresses critical pain points, improves operational efficiency, and enhances worker safety. The findings and insights from this research have broader implications for the use of autonomous robotic platforms in various industries. Continued research and development efforts can further optimize these platforms and expand their applications, driving advancements in the field of robotics and automation.
Unlocking Stranded Marginal Gas Fields in Malaysia with Low CAPEX Approach
Authors: How L. L. , Farina N. Norintan , Azmukiff K. , Osman Z. , Chang Y. S. , Shahardin S. , Azhar S. Fairudul 
Published: October 2023

Abstract
Vestigo Petroleum Sdn. Bhd. (VPSB) has been awarded the right to develop and produce gas fields via a Petroleum Sharing Contract in offshore Peninsular Malaysia. The contract area consists of undeveloped Field T, Field I and Field B integrated late life asset. The undeveloped Field T and Field I marginal gas fields lie within 60km from existing Field B production hub. The production comes from gas dominant Field B, a producing field that is in decline and of which VPSB assumed operatorship in 2017. The immediate business driver is rapid monetization of the asset via tapping into nearby potential marginal gas fields. To enable this, low CAPEX and rapid development with fast investment return are required.
Among the undeveloped gas fields, Field T was identified to anchor the development as it carries significant discovered gas volume. However, majority of the gas volume comes with H2S, and CO2 content which is beyond sales gas specification. Therefore, inter and intra fields blending were considered. Owing to the complexity of Field T that is dependent on other fields to provide blending medium, it is crucial to outline sequencing, timing of the other gas fields appraisal and development to optimize the development.
To start with, Field T development from a wellhead platform tied back to existing VPSB processing facilities via a 60km long pipeline was the identified concept. A standardized wellhead platform, batch drilling and fit purpose well design in conjunction with application of Zap-Lok, mechanical connection technology in pipeline and sharing of operation cost with existing facilities led to cost effective development. This is the first application of Zap-Lok technology offshore Malaysia. The deployment of VPSB standard wellhead platform enabled rapid field development, with platform installed in less than eleven (11) months after project approval. All five (5) development wells were batch drilled to penetrate multiple stacked reservoirs and completed monobore to accommodate selective bottom-up perforation. These monobore wells either commingled minor reservoirs within similar pressure regime or selectively perforated at major reservoirs only for production to meet committed gas rate. This also ensures not to jeopardize major reservoirs gas reserves and reservoir management plan.
Future developments are being planned to duplicate the success of Field T development to other identified marginal fields to drive cost reductions over time with higher efficiency.
Estimating Formation Tops While Drilling Using Rate of Penetration (ROP) and Mechanical Specific Energy (MSE)
Authors: Alshahrani Saeed , Ayadiuno Chris 
Published: October 2023

Abstract
Accurate determination of formation tops while drilling is a critical part of exploration geology workflow. Operational decisions on coring, wireline logging, casing and final well depth largely depend on it. One of the commonly used methods for picking formation tops while drilling is to correlate rate of penetration (ROP) of the new well to wireline logs from offset wells where there is no logging while drilling (LWD) data. Placing formation tops based on ROP only can result in picking the wrong formation tops. To improve the workflow and outcome, this paper proposes the combination of ROP and Mechanical Specific Energy (MSE) for estimating formation tops while drilling. MSE is a measure of energy required to crush or drill through a unit volume of rock. Because MSE is related to rock strength, it can be correlated to changes in lithofacies and formation tops.
Three key steps necessary for utilizing mechanical specific energy to estimate formation tops. The first step involves selection of input drilling data relevant to the applicable MSE equation. There are several empirical equations in the literature which can be used for estimating MSE. Input data are ROP, Weight on Bit (WOB), Bit Size (BS), Rotation Per Minute (RPM), and Torque (TORQ) from both the offset wells and the new well. Second, it is necessary to utilize predetermined empirical equation to estimate MSE. Third, correlate MSE and ROP from the new well to MSE, ROP and wireline logs from offset wells (where available) in order to determine formation tops in the new well. Application of the proposed workflow to two wells shows 1) distinct bed boundaries, which agree with formation tops picked using wireline logs; (2) that including MSE increases confidence and reliability of the data and makes it easy to identify the different formation boundaries based on the observed features of both MSE and ROP in the new well; and (3) that MSE variations are sensitive to formation strength, which may indicate rock mechanical changes and formation heterogeneity.
This paper presents an alternative method of picking formation tops using MSE and ROP while drilling. The preliminary results based on the two test wells showed over 95% match with those picked using wireline logs of the same new well. As a result, this workflow enhances the ability of geoscientists to correlate subsurface geological features, reduces the uncertainty associated with picking formation tops, casing and coring depths. Furthermore, it improves the confidence in the result of picking formation tops, enhances quality of operational decisions and reduces non-productive time (NPT) and well-cost.
A Novel Surrogate Model for Reservoir Simulations Using Fourier Neural Operators
Authors: Kazemi Mohammad , Takbiri-Borujeni Ali , Nouroizeh Hossein , Kazemi Arefeh , Takbiri Sam , Wallrich Clayton 
Published: October 2023

Abstract
This paper presents a novel approach using deep learning to address the computational challenges associated with direct numerical simulation (DNS) in reservoir management for oil and gas recovery optimization. DNS involves solving partial differential equations for millions of numerical grids, making it time-consuming and computationally expensive, especially when multiple DNS runs are required for uncertainty quantification. The proposed approach utilizes a deep learning model with a new neural operator that parameterizes the integral kernel directly in Fourier space, enabling fast and efficient predictions. By leveraging this approach, the heavy lifting of multiple DNS processes is eliminated, reducing computational time from hours to seconds. The methodology is applied to a two-dimensional reservoir use case, demonstrating its effectiveness in accurately predicting simulation results at a grid level with less than 10 percent error. This approach offers significant potential in accelerating reservoir simulation processes and optimizing oil and gas recovery strategies.
Characterization of Two-phase Slug Flow using Distributed Acoustic Sensing in Horizontal Pipes
Authors: Ali S. , Jin G. , Fan Y. 
Published: October 2023

Abstract
This article discusses the use of Distributed Acoustic Sensing (DAS) to monitor and characterize two-phase slug flow in horizontal pipes. This technology utilizes a regular telecommunication fiber connected to a DAS interrogator to detect flow signals and provide substantial insights into flow characterization and quantification. The capabilities of the DAS are highlighted through a series of experimental tests using various rates of two-phase liquid and gas fluids.
The experiments were conducted in a multiphase flow loop consisting of a transparent polyvinyl chloride (PVC) horizontal test section measuring 14 m. long and 5 cm. inner diameter. A fiber cable was wrapped helically on two 35 cm. long sections, around 61 cm apart, with 11 loops per cm on each section. Mineral oil, tap water, and compressed air were used as the testing fluids. The oil phase velocity varied from 0.2 to 0.8 m/s, while the gas phase varied from 19 to 95 LPM. The facility was also equipped with a high-speed camera and an Electrical Capacitance Volumetric Tomograph (ECVT) system for cross-validating purposes.
New low-frequency strain-rate analysis of the DAS signal demonstrates its capabilities of quantifying two-phase slug flow characteristic parameters, including the slug frequency, slug body length, slug translational velocity, the slug unit, and the liquid film region lengths. The results were compared with the data obtained from the high-speed camera, located right next to the wrapped fiber section, and ECVT, located after the second wrapped section. The three measurements show good matches for all tested flowing conditions. The results will shed light on the production prediction and profiling for slug flow in wellbores or surface pipeline systems.
Compared to other sensors that can also characterize slug flow, such as optical or conductivity/capacitance sensors, DAS technology has the advantages of real-time and continuous measurement over long distances. It can also be applied in harsh environments such as in downhole wellbore conditions where other sensors may not withstand. It opens the doors of production profiling in wellbore/pipeline over long distances which cannot be realized by other point sensors. The cost is relatively low in the field application compared to other point sensors, especially in conditions where measurements are required at multiple locations, with the fact that fiber cable is much cheaper compared to other sensors.
For the first time, this study shows in detail the capability of DAS in characterizing two-phase slug flow in horizontal pipes, and its potential for production profiling in horizontal wells and surface pipeline systems. New insights on the low-frequency DAS signal are discussed along with the physical interpretation of the slug flow dynamics. The new analysis will be beneficial to the petroleum industry in advancing production profiling technologies, production process optimization, and operation safety management.
A Comparative Study of Deep Learning Models for Fracture and Pore Space Segmentation in Synthetic Fractured Digital Rocks
Authors: Wang Hongsheng , Guo Ruichang , Leng Jianqiao , Hosseini Seyyed A. , Fan Ming 
Published: October 2023

Abstract
This study focuses on the comparative study of deep learning (DL) models for pore space and discrete fracture networks (DFNs) segmentation in synthetic fractured digital rocks, specifically targeting low-permeability rock formations, such as shale and tight sandstones. Accurate characterization of pore space and DFNs is critical for subsequent property analysis and fluid flow modeling. Four DL models, SegNet, U-Net, U-Net-wide, and nested U-Net (i.e., U-Net++), were trained, validated, and tested using synthetic datasets, including input and label image pairs with varying properties. The model performance was assessed regarding pixel-wise metrics, including the F1 score and pixel-wise difference maps. In addition, the physics-based metrics were considered for further analysis, including sample porosity and absolute permeability. Particularly, We first simulated the permeability of porous media containing only pore space and then simulated the permeability of porous media with DFNs added. The difference between these two values is used to quantify the connectivity of segmented DFNs, which is an important parameter for low-permeability rocks. The pixel-wise metrics showed that the nested U-Net model outperformed the rest of the DL models in pore space and DFNs segmentation, with the SegNet model exhibiting the second-best performance. Particularly, nested U-Net enhanced segmentation accuracy for challenging boundary pixels affected by partial volume effects. The U-Net-wide model achieved improved accuracy compared to the U-Net model, which indicated the influence of parameter numbers. Similarly, nested U-Net has the closest match to the ground truth of physics-based metrics, including the porosity of pore space and DFNs, and the permeability difference quantifying the connectivity of DFNs. The findings highlight the effectiveness of DL models, especially the U-Net++ model with nested architecture and redesigned skip connections, in accurately segmenting pore spaces and DFNs, which are crucial for pore-scale fluid flow and transport simulation in low-permeability rocks.
Implementation and Evaluation of Digital Sliding Automation for Remote Directional Drilling Operations
Authors: Forrest D. , Neufeldt A. , Fernandes A. 
Published: October 2023

Abstract
Slide drilling with a bent housing downhole motor is the most widely utilized method of directional control for unconventional reservoirs in North America. There are many challenges involved with consistent directional control using this method, given the increased directional work for more complex well trajectories. Challenges include managing reactive torque generated at the bit, sliding through lithology transitions, and adapting to the ever-changing relationship between differential pressure and tool orientation. Additionally, since sliding is primarily a human managed process, performance can vary substantially between directional drillers for the same rig and well. Digitally automated sliding provides consistency, accuracy, improved rate of penetration, and better management of toolface through challenging transitions. Additionally, digitally automated sliding easily enables fully remote directional drilling operations.
This paper outlines the implementation of fully remote directional operations using a digitally automated sliding application running on the rig's process control system. The software application uses auto-populated fields for target toolface and slide length from an off-site directional driller, which are accepted by the driller on location. The software application uses machine learning to achieve the correct reactive torque and differential pressure for orienting toolface both while tagging bottom and while sliding. Once active, the software takes control of the top drive and draw works to orient toolface and maintain minimal toolface error while increasing ROP.
Thorough analysis of human versus digital automation will be presented across multiple well pads in the same field on a Super Triple 1500HP AC rig. Overall performance will be evaluated by comparing toolface error while sliding as well as sliding ROP. This paper demonstrates that sliding automation has achieved a 50% improvement in sliding ROP while reducing toolface error by 30% over manual sliding in the same section. Human factors influencing the implementation of sliding automation will also be discussed. Final analysis will focus on the broader performance gains such as improvements in effective motor yields and sliding intervals needed to achieve directional requirements.
The intention of this paper is to provide a robust analysis of the benefits that digital sliding automation can provide. By analyzing a large dataset to attenuate the effects of well-to-well variables, results are more representative of automation's true value. There is a significant focus on analyzing aspects of the operation that are difficult to quantify, such as automation's effect on motor yields. This paper describes a sliding technology that not only utilizes differential pressure for toolface control, but also incorporates machine learning to automatically learn from previous slides for better optimization of both the tagging bottom and sliding processes going forward.
DNA Tracer Application in a Waterflood Study in Edmisson Field: A Case History
Authors: Asadi M. , Blair T. , Ryan J. 
Published: October 2023

Abstract
This paper presents and discusses data on the application of DNA tracers to a waterflood project in Edmisson Clearfork field in Permian Basin, Tx. The field is divided into two sections, West and East. The West section includes five injection wells, fifteen producers and the East section includes two injection wells and nine producers. To fully understand the flow path within both sections, seven unique DNA tracers were selected and injected. In addition, it was desirable to accurately determine water breakthrough through each production well and to evaluate heterogeneity of the formation between the injection and production wells. Furthermore, identification of high and low permeability zones within the reservoir was identified. Reservoir properties such as drainage area, porosity, water saturation, connate water saturation and water injection rate per well along with the minimum detection limit of DNA tracers were used to calculate the amount of DNA tracers needed to inject in each injection well. A robust sampling schedule was used to make sure an early detection of tracer(s) at each production well. A selected number of samples were used for analyses of tracers. Other samples were kept for future analyses as needed depending on tracer response curve between each injection well and producer.