bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning and Quantitative Seismic Attribute Analysis Using Reprocessed 3D Pre Stack Depth Migrated Seismic and Well Data in a Southern California Gas Field, Offshore Federal Waters
Authors: Ojukwu Chima , Smith Kevin , Baldwin Kimberly , Leung Mark , Mondegari Nadia , Cao Jenny 
Published: April 2022

Abstract
The Bureau of Ocean Energy Management (BOEM) has a mandate to evaluate hydrocarbon resources for the national interest. In addition, the Bureau has begun subsurface characterization for carbon capture and sequestration (CCS). For both endeavors, an accurate assessment depends on a robust, well-documented workflow; however, some assessment areas lack sufficient data coverage or only contain vintage geologic and geophysical datasets. Machine Learning (ML) techniques may be useful in these situations to fill in data gaps, predict new data, and recognize outliers. In this study, ways to improve resource evaluation efforts are investigated by combining quantitative seismic attributes from a recently reprocessed 3D multichannel seismic survey, petrophysical data from 5 wells, and machine learning techniques to identify reservoir characteristics in an offshore basin in southern California. Several linear regression algorithms are evaluated to predict effective porosity, clay volume, and water saturation (Sw) from seismic attribute data. Although results generally follow established theoretical rock physics relationships, the 3000-ft Pliocene section proves challenging to model. Of the algorithms tested, RF performs best. Initial model results show a coefficient of determination score (R2) of about 0.9 for train and test data. However, after validating the model with a well unseen by the algorithm, the RF model does not perform well. These results can be explained by data leakage and overfitting of the train and test datasets. To improve the method to avoid data leakage, cross-validation is used, and groups are established on a well-to-well basis, ensuring independent and identically distributed random variables. After this improvement, prediction from this model is fair, and when plotted predicted petrophysical properties show similar pattern and range of values to true petrophysical properties. Unsupervised hierarchical clustering tests show hidden trends in the data that may help further improve future efforts to use ML techniques for reservoir characterization.
A Two-Phase Flowback Type-Curve for Multiscale Fluid Transport in Hydraulically Fractured Shale Reservoirs
Authors: Zhang Fengyuan , Zou Linjun , Rui Zhenhua , Emami-Meybodi Hamid , Cui Wei 
Published: April 2022

Abstract
The quantitative understanding of HF properties guides accurate production forecasts and reserve estimation. Type curve is a powerful technique to characterize hydraulic fracture (HF) and reservoir properties from flowback and long-term production data. However, the two-phase flow of water and hydrocarbon after an HF stimulation together with the complex transport mechanisms in shale nanopores exacerbate the nonlinearity of the transport equation, causing errors in type-curve analysis. Accordingly, we propose a new two-phase type-curve method to estimate HF properties, such as HF volume and permeability of fracture, through the analysis of flowback data of multi-fractured shale wells. The proposed type curve is based on a semianalytical solution that couples the two-phase flow from the matrix with the flow in HF by incorporating matrix influx, slippage effect, stress dependence, and the spatial variation of fluid properties in inorganic and organic pores. For the first time, multiple fluid transport mechanisms are considered into two-phase type-curve analysis for shale reservoirs. We analyze the flowback data from a multi-fractured horizontal well in a shale gas reservoir to verify the field application of the proposed method. The results show that the fracture properties calculated by the type-curve method are in good agreement with the long-time production data.
Reducing HSE Risks Through Automation & Closed Loop Systems
Authors: Smith Simon , Nilsson Olof , Skåland Aksel 
Published: April 2022

Abstract
Solids production in produced fluids is an industry-wide challenge with significant costs incurred by Operators through lost production, asset integrity and costly well intervention campaigns. In particular, these intervention activities continue to expose offshore personnel to HSE-risks. These risks are typically caused by manual handling of NORM contaminated solids (Naturally Occurring Radioactive Material), H2S, Benzene and other hazardous by-products of produced oil and gas.
Improved methods, and technologies, for handling solids have fully removed this exposure by using automated and fully enclosed systems. However, legacy equipment utilising manual solid-handling systems are still being used in the industry thereby exposing offshore personnel to unnecessary HSE risks. This paper highlights solids management technologies which are currently available and still in use topside. It also identifies the amount of exposure to hazardous by-products of produced oil and gas from these various technologies.
This paper also presents a new technology case study and compares this alongside legacy methods and practices in terms of HSE risk and cost. HSE improvement through technology is also quantified and shared.
The new technology presented demonstrates there are solutions that enable the eradication of personnel exposure to the hazardous by-products of oil and gas production. However, it is identified that legacy systems are still widely used and adopted in the market resulting in many man-hours of unnecessary exposure to unsafe operations and hazardous by-products.
The case study gives detail of a project for a major UK North Sea operator who targeted additional production by cleaning several wells that had scaled-up over time. The operator had previous bad experience cleaning up NORM contaminated scale on surface and predicted that large quantities of NORM-contaminated scale were present on the wells to be worked on. The solution utilised a compact (2.0m × 2.0m footprint) patented dual vessel desander with real-time weighing of solids and interlocked valve design. By combining fully enclosed scale debris tanks with level sensors and sight glasses alongside the dual vessel desander the need for manual handling of NORM solids was completely removed while delivering effective solids removal and accurately quantifying solids. This paper finishes by discussing how to safely handle solids using newly available technology.
Performance Baskets: An Alternative Solution to Challenging Collapse Pressures
Authors: Ybarra Maria Beatriz , Filgueiras Pedro , Kubla Guido , Thomas Timothy , Daly Daly , Oliveira Jonathas 
Published: April 2022

AbstractDeepwater and high pressure wells, especially in Gulf of Mexico, are raising unprecedented challenges to Oil & Gas operating companies due to extremely high mechanical loads. The learnings from drilling operations and the evolution of regulations, pushed the development of more complex, slimmer, non-standard well designs.This paper describes the Performance Baskets approach, a solution developed to meet those collapse needs that cannot be met using standard materials and conventional techniques for pipe performances calculation.This methodology uses production's intrinsic variability by segregating the pipes into two baskets depending on their collapse performances: one contains the pipes with standard high collapse rating, and the other includes the enhanced design collapse rating.The rating of each pipe is calculated to a target reliability level, using a proprietary model. The model accounts for mechanical properties and residual stresses, based on physical tests; and the geometry of each pipe, obtained by automated non-destructive inspection to 100% surface coverage. Pipe traceability assures the link between shop floor and data. Actual collapse tests confirm ratings and model assumptions.The Performance Baskets methodology was initially applied to 5 different products included in the string design of one of the most challenging wells in the Chevron Deepwater Asset Class. The tubular items are: 13 5/8" 88.2#, 16.08" 109.90#, 16.15" 119.23#, 16.15" 126.91#, and 16.20" 131.70#. This methodology can actually be applied to the complete range of high collapse products available in the industry.Thanks to this methodology, the enhanced design collapse rating was boosted beyond the standard published high collapse value associated to the pipe to enable the end user covering the worst case levels predicted for collapse.This approach proposes an alternative methodology that has no precedents in the industry. Based on individual pipe performance calculation, it offers a cost-effective innovative solution to reach ultimate collapse properties to balance the cost of traditional Single Basket offers.
High Precision Velocity Model Building Technology for Pore Pressure Prediction
Authors: Li Shaoxuan , Zheng Jiangfeng , Wang Wei , Han Rui , Xue Bing 
Published: April 2022

Abstract
Good knowledge of formation pore pressure is required for the safe and economic drilling of wells. At present, Eaton and Bowers methods are widely used in pore pressure prediction, and the prediction accuracy of these methods is dependent on the accuracy of the underlying formation velocity prediction. However, the accuracy of the velocity field created by conventional velocity modeling struggles to meet the requirements for accurate pore pressure prediction. To solve this problem, a new velocity modeling method is proposed that in depth domain is based on grid tomographic inversion with structural constraints. The new method allows to build a high-precision velocity model, which can help improve the accuracy of pore pressure prediction.
The research results based on physical model data show that this method can improve the modeling accuracy of the velocity field significantly, and the position and size of even small-scale geological bodies with anomalous velocities can be clearly described. This method is applied to identify a subtle velocity anomaly in the sub lacustrine fan area of the Bohai Q oilfield. In the process of velocity modeling, interpreted seismic horizons are used to constrain the velocity changes, and a seismic amplitude attribute is mapped to delineate the fan body to establish an accurate velocity field for regional pore pressure prediction. After the study, an exploratory well was drilled in this area, and its measured pressure data was found to be consistent with the predicted prespud pressure, which proved that the high-precision velocity field established by this method can improve the accuracy of pressure prediction and ensure the safety of drilling construction.
Distributed Real-Time Plume Monitoring for Deep Sea Mineral Extraction
Authors: Mons Ishan , Mansfeld Andrea , Boulais Océane , Veedu Vinod , Mrozewski Stefan , Elshahawi Hani 
Published: April 2022

Abstract
In the emerging industry of deep-sea mining for minerals and deposits (e.g., polymetallic nodules for nickel, cobalt, copper, and manganese), more data is required to understand the effects of sediment plume generation and predict the distribution of disturbed sediments. There are two main sources of plume generation, the first being at the active mining site where the "collector" directly removes the top layer of the sea floor. The other is the "midwater plume" consisting of unwanted sediment that was collected during extraction that is pumped back into the aphotic zone. The vast majority of plume generation is caused by the collector, causing detrimental and long-lasting impacts on sea floor ecosystems due to the lack of wave activity or strong currents at the sea floor. Therefore, it is crucial to invest in the infrastructure to support the study and constant monitoring over a large area of the sea floor where plume generation is present. Due to the limited number of usable channels and power requirements, current subsea wireless communications technologies are not well suited to instrumenting the large areas of the sea floor needed to monitor plume migration.
The scope of this effort is to transition experimental demonstrations of high-bandwidth, full-duplex scalable underwater laser communications to the sea floor in an open ocean environment. Specifically tackling challenges associated with the dynamic nature of the subsea world, including but not limited to, deployment logistics, sustainability, and range. The goal is to enable the internet of underwater things for deep sea industries by broadening the capabilities of subsea communications.
By using high-precision laser transmitters, many of the challenges facing current subsea optical systems can be circumvented, such as power consumption, interference, and bandwidth limitations. This approach lends itself to wireless interlinking multi-node networks, in series or parallel, facilitating the implementation of a wide array of sensor types. This interlinking allows all the data gathered from the network to be processed through a single hardline uplink to the surface, lowering the complexity required for near real-time data processing. Additionally, the laser control systems produce metadata that can be used to help characterize the water column between the nodes.
Combining data from various sensors such as turbidity, temperature, and current velocity with metadata such as beam attenuation and deflection can produce a high-resolution model of sea floor conditions around an active mining zone. The resulting near real-time model can be used to optimize location and flow rate of the mining operation to minimize and quantify the environmental impact.
High-Performance DEH-PiPfor Subsea Tiebacks: Theoretical Electrical Model and Performance Measurements
Authors: Hallot Raymond , D'Amico Amerigo , Gentil Frederic , Lozinguez William 
Published: April 2022

AbstractThe High-Performance DEH-PiP is a new technology for electrical heating of subsea tieback flowlines, able to be used for long distances thanks to an enhanced electrical efficiency. This paper describes the theoretical electrical model of this technology and its experimental validation.DEH-PiP is a well-known and field-proven technique for subsea flowline heating, but it exhibits a relatively poor electrical efficiency compared to emerging heat-traced PiPs. The approach presented in this paper to solve this drawback has been first to analyze the heat losses within the conventional DEH-PiP, then to design a new outer pipe to decrease dramatically its impedance. A strong effort was spent on the building of an accurate electrical model for the High-Performance DEH-PiP, and finally full-scale electrical tests on a representative prototype were carried out to validate the theoretical model.On the basis of the conventional DEH-PiP technology, specific design modifications were undertaken to enhance the electrical efficiency and thus the flowline step-out in the High-Performance DEH-PiP solution. A specific electrical model was developed and is detailed progressively in this paper from the basic electromagnetics considerations to the complete Finite Element Analysis including the magnetization curves of the pipe steel and the impact of magnetic hysteresis on the heating performances. Dedicated experimental works were conducted to validate the electrical model, on a specific prototype and the test bench associated. The electrical measurement curves are discussed, showing a good correlation between the theory and the experiments, and demonstrating the relevance of the electrical model. The impact of the improved electrical performance on the tieback heated flowline configuration is then discussed.This paper introduces the detailed experimental and modeling work performed to allow for the electrical engineering of a new DEH-PiP design, reaching significantly higher electrical efficiency, in the range of 90%-95%. This new solution allows reduced Greenhouse Gases Emissions by enhanced performance, lower voltage supply level enabling for longer flowlines, and lighter power chains.
Development of a Hydrate Kinetics-Based Digital Twin for Blockage Detection and Production Optimization
Authors: Dhoorjaty Pradeep , Wold Inge , Penmetsa Dileep 
Published: April 2022

Abstract
Management of hydrate risk is a challenging problem in gas-condensate production systems. A "no inhibitor, no production" operational strategy is the norm due to the insufficiency of real-time knowledge of process conditions and hydrate kinetics in the field. This implies both lost-production costs and inadequate risk awareness. Recent advances in hydrate kinetics modeling in gas-dominated systems, based on fundamental research at the University of Western Australia (UWA), however, provide an opportunity for increased operational efficiencies and safety. This paper presents progress made in a joint-industry project (JIP) towards an integrated, real-time flow assurance model incorporating hydrate kinetics.
This JIP leveraged existing data from UWA -- including flowloopmeasurements and the modelling of hydrate growth and transport mechanisms -- to achieve the integration of a hydrate kinetics model with an existing, field-proven multiphase flow simulator. This paper discusses the modeling, validation against experimental data, and the testing against historical data from a field system underhydrate-free and hydrating conditions.
The model showed good agreement for multiphase flow prediction of laboratory experiments. Trends of pressure drop dependence on various flow parameters agreed as well. For field data of a large gas-condensate pipeline, the model correctly predicted when hydrates form and when they are absent. These simulations, further, revealed the sensitivity of overall hydrate holdup and distribution to several parameters, including ambient temperature, holdup profiles, water droplet entrainment, and the strength of hydrate-wall adhesion. Since number of these parameters are subjected to underlying uncertainties in field conditions, a viable hydrate monitoring/prediction tool ought to be based on ensembles of simulations (an analogy might be drawn to hurricane track prediction). This is particularly true for predicting the location of the hydrate blockage and the time-to-remediation.
Currently, there is little modeling support to help manage hydrate risk in gas-condensate production operations without recourse to overly conservative strategies, such as overdosing. This model represents the first steps towards an online tool that can be integrated into digital twins of such fields. Such a tool will help optimize inhibitor consumption, reduce lost production costs, and provide early warning of hydrate blockage hazard. Potential end-user savings, from inhibitor consumption alone, are estimated at USD 500M in capital expenditure and USD 50M per annum in operational expenditure, for a typical moderate to large development.
Development of A CO2 Specification for Industrial CCS Transport Networks: Methodology, Limitations and Opportunities
Authors: Luna-Ortiz Eduardo , Yao Cathy , Barnes Jon , Winter Matthew , Healey Matthew 
Published: April 2022

Abstract
Impurities in anthropogenic CO2 affect several aspects of a full-chain industrial CCS hub. The concentration and type of impurities expected from various CO2 sources will differ depending on the technology that is emitting CO2. Rather than provide individual specifications to different customers, a single specification approach is being usually considered. The intent of this specification is to provide a single requirement that all sources of CO2 must abide by. If different specifications were provided to different customers, the transport network might become reliant on acombination of users supplying CO2 at specified rates to meet an overall specification. Several CO2 specifications have been developed over the years. Although these specifications serve as guidance,each project/development will develop its own specification that is appropriate for its owncharacteristics.
In this paper, we develop a multi-purpose fluid specification for a CCStransport hub. The specification is flexible as it takes into consideration a wide range of emitters,industry type and capture technology. We describe a development framework that takes intoconsideration: mechanical integrity (corrosion), operational integrity (two-phase zone, hydrateformation, water dropout, ductile fracture), HSE, storage capacity and integrity.
The proposed specification contains a wide range ofimpurities but the limits in the specification are as broad as reasonably possible. Moreover, the CO2 specification covers a large range of operating conditions: from low pressure (gas phase) and highpressure (liquid phase). As we considered more than 30 species in the fluid, the proposed specificationhas a wider scope that current known industry standards (or even known projects). A comparison with other publicly available specifications is provided. In addition, the specification is heavily relying on good understanding of thethermodynamics of CO2 fluids with impurities: we assess and provide a comparative framework of themost adequate EoS for the chemical nature of the potential impurities.
This paper offers an extended CO2 fluid specification for an industrialCCS transport system where a range of emitters (each of different nature) will tie-in. The proposedspecification offers a starting point for the design of CCS networks. It provides a common basis for thedesign of the components of the CCS system. We provide guidance on the specific issues that shouldbe particular for each project in order to optimize and customize the specification.
Artificial Intelligence Applied to Nanotechnology in the Oil and Gas Industry: Study of Asphaltene Adsorption Using Nanoparticles
Authors: Cedeno Miguel Angel , Enriquez Fernandez Andres , Cortés Correa Farid Bernardo , Franco Ariza Camilo Andrés , Moncayo-Riascos Ivan 
Published: April 2022

Abstract
Asphaltenes create severe problems in light oils (LO) and heavy crude oils (HO) production, therefore understanding the proper asphaltenes adsorption is a demanding topic to circumvent asphaltene deposition and reconfigure asphaltene viscoelastic networks. The aim of this work is to develop several artificial intelligence (AI) agents that accurate predict the asphaltene adsorption produced by different types of nanoparticles. More than 200 experimental data points were used including different types of crude oils (light oils, heavy oils, and extra-heavy oils) combined with different types of nanoparticles including silica, alumina, and cerium, among others. This work not only presents for the first time a general AI agent that predicts the adsorption isotherms of asphaltene including different types of nanoparticles, but also deploys specialized AI agents that predict adsorption isotherms of asphaltene exclusively for silica, alumina, and cerium nanoparticles.