bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cloud-Enabled Ubiquitous Collaboration on Large Models Using Augmented Reality
Authors: Boonyasaknanon Phathompat , Pols Raymond , Schulze Katja , Rundle Robert 
Published: November 2020

Abstract
A cloud-based augmented reality system is presented which is designed to enhance the real-time collaboration of domain experts involved in modeling large reservoirs. An evaluation of traditional techniques is compared with this new approach. Work-from-home (WFH) scenarios are becoming more important and, in some cases, critical. There is a need to untether the geologist and other domain experts from high end office-bound workstations.
Reservoir models in recent years have become increasingly large. The volume of seismic, well data and modeling data structures presents a challenge in both management of the data and making it accessible to domain experts and others. The traditional practice of siloing data in various disconnected data repositories in various corporate data centers is becoming increasingly untenable. What is needed is a comprehensive approach that scales to the largest models and is accessible anywhere for time critical analysis and collaboration. The authors have created a prototype of a potential solution to this problem, where the model resides in the cloud and can be visualized in augmented reality either as a hologram through a head mounted display or through the AR capabilities of tablets and smartphones. This technology is ideal for WFH scenarios: it is light weight, inexpensive and does not require broadband speeds in excess of what most home users can access.
What we have found is that AR based approaches to modeling large reservoirs can rival traditional workstation approaches. Furthermore, AR based approaches are superior for close collaboration among domain experts. Collaboration on 3D models has not changed significantly in a generation. For co-located personnel the approach is to gather around a 2D screen. For remote personnel the approach is sharing a model through a 2D screen along with video chat. Over the years various attempts have been tried to enhance the collaboration experience and have all fallen short. Virtual reality (VR) has been proposed as a solution. However, we have found that augmented reality (AR) is a much better solution for many reasons which are explored in the paper.
The cloud has been transformative for nearly every industry. The oil and gas industry has been slow to adopt cloud technologies for many reasons that have largely been overcome. AR has already acquired an impressive track record in various industries. AR will have applications in nearly all industries. For various historical reasons, the uptake for AR is much faster in some industries than others. It is too early to tell whether the use of cloud-based augmented reality for modeling large reservoirs will be transformative, however the results of this initial work are promising.
Prediction of Fluid Flow in Porous Media using Physics Informed Neural Networks
Authors: Almajid Muhammad Majid , Abu-Alsaud Moataz Omar 
Published: November 2020

AbstractIn the realm of reservoir engineering, we are categorized to be in the small data regime because of the complex physical systems we are dealing with as well as the inherent uncertainty in our problems. The objective of this paper is to present a physics informed neural network (PINN) technique that is able to use information from the fluid flow physics as well as observed data to model the Buckley-Leverett problem.We use the classical problem of drainage of gas into a water-filled porous medium to test our implementation. The analytical solutions obtained using the method of characteristics are compared with solutions obtained using the DeepXDE library that is built on top of TensorFlow. The automatic-differentiation capability of TensorFlow allows us to interweave the underlying physics of the problem with any observed data to come up with a data-efficient universal function approximator. The flexibility of TensorFlow also allows us to set multiphase parameters to be trainable or not, which would ultimately assist in any inverse modeling efforts using the problem.Several cases are presented that highlight the importance of the coupling between observed data and physics-informed neural networks for different parameter space. The cases demonstrate the capability of PINNs to augment data-driven solutions. Our results indicate that PINNs are capable of capturing the overall trend of the solution even without observed data but the resolution and accuracy of the solution are improved tremendously once the augmentation of data and physics is implemented. Even with a large mobility ratio, the predicted solution of the PINNs seems promising. The results in our paper indicate that such methods can be utilized to train models that could be used estimate a well-informed initial guess in a reservoir simulator because they capture the overall behavior but miss the intricate details that could be circumvented with conventional reservoir simulation.The work presented in the paper demonstrates the importance and the capability of applying machine learning approaches in reservoir engineering problems. Additionally, it gives a forward-looking approach for the future of reservoir simulation techniques that could augment data with the physics.
An Efficient and Robust Method for Optimizing the Number of Non-Linear Iterations for Simulating Highly Heterogeneous Naturally Fractured Reservoirs
Authors: Mohajeri Sina , Eslahi Reza , Bakhtiari Maryam , Alizadeh Ali , Zeinali Mostafa , Madani Mohammad , Rajabi Hamed , Sharifi Ebrahim , Mortezazadeh Emad , Mahdavifar Yasser 
Published: November 2020

Abstract
For speeding up the complex fractured reservoir simulating, we have given more attention to reducing runtime and improving efficiency of the solver. In this work, we describe an improved and computationally efficient version of Newton's method, which reduces the non-linear iteration count, increases time steps, and furthermore reduces time spent in nonlinear loops of reservoir simulating. Safeguarded variants of Newton's method which have used in current reservoir simulators cannot guarantee convergence of the solution, especially in highly heterogeneous, detailed and fractured reservoirs. In such simulators time step chopping is often observed. From other hand, with growing complexity, convergence difficulties can lead to considerable losses in computational effort and prohibitively small time steps. For overcoming this problem, an improved and computationally efficient version of Newton's method, which uses higher order terms in the Jacobian matrix in addition to the Newton's basic linear terms to account for cross variable dependencies including a high-tech stable linear solver, is proposed. This scheme leads to a smaller number of non-linear iterations compared to other known commercial simulators with larger time steps. In addition to SPE10 model, an under saturated fractured black-oil reservoir model, including single and dual grid blocks together, with dimension of 124*105*60 grid blocks is used to evaluate the method efficiency, by comparing the results using the known commercial simulator. This heterogeneous model includes the features of hysteresis, gravity drainage, oil-gas surface tension, and moderate aquifer with 8 active production wells which have produced for 24 years. The numbers of time steps are 525 and 2693 for proposed method and the commercial simulator, respectively. The numbers of Newton iterations are 1667 and 9399, and the average time step size is 26.2 and 5.62 days, for proposed method and the commercial simulator, respectively. These results obviously indicate the efficiency of the proposed method in reducing Newton's iterations and accordingly increasing simulation speed. The novelty of the proposed creative method is in reducing non-linear Newton's iterations and increasing time steps which leads to reduced simulating elapsed time, which has never been observed in current simulators.
Best Practice for Inflow Control Technologies
Authors: Rinde Trygve , Jondahl Morten Hansen , Moen Terje 
Published: November 2020

Abstract
The use of Inflow Control Technologies (ICTs), both passive and active, has been motivated by the technology's ability to significantly improve the overall production from a reservoir. Increased oil production combined with reductions in volumes of both water and gas should be expected if these technologies are deployed correctly. These technologies may also affect the total reservoir drainage in a positively manner with less energy used for long term pressure support as result. ICTs are available in passive, active and autonomous versions, all with different physical working principles. Even though some of these are used world-wide, the lack of best-practice advisory documents for planning, implementation, and operation effectively hinder further acceleration of its deployment. Since the first inflow control device (ICD) was patented in 1992, it is response to varying production rates, phase fractions and reservoir pressures that has been addressed, tested, and modelled by many instances. Some of these models are available in today's commercial reservoir simulators, although with poor accuracy.
Today, 28 years after the first ICD patent, an important challenge to widespread deployment of the technology is believed to be the lack of established best-practice documents and adequate simulation models. This may hinder efficient comparison of the different technologies against each other and alternative barefoot completions.
This paper presents the establishment of such best-practice documents and the development and implementation of generic, effective, and high accuracy simulation models with the ambition to enable qualified selection processes within the different operating companies.
The project presented in this paper presents best practice workflow for selecting best ICD completion and the modelling challenges for these completions and their impact on the total drainage from the reservoir. The project has unveiled new understanding that led to drastic changes in the way ICD completions should be evaluated and compared by the operator to ensure maximum recovery.
Low Cost Solution for Marginal Subsea Field Development Based on Innovative Insitu Subsea Power Cells and Subsea Hydraulic Power Units
Authors: Elsabbagh Sameh , Elwakil Mohamed 
Published: November 2020

Abstract
This work studies how to build a simple yet efficient subsea field architecture utilizing emerging offshore wind, subsea power cells and subsea hydraulic power units. Such architecture eliminates expensive topsides equipment, subsea umbilical and related distribution equipment which reduce CAPEX for marginal field and lead to economical feasible development.
The emerging innovations in offshore wind, subsea power cells and subsea hydraulic power units will be surveyed and benchmarked against to actual needed subsea power and hydraulic requirements based on gained experience from Mediterranean subsea and deep water Egyptian gas fields. Advantage and limitation for subsea power cells and the subsea hydraulic units will be compiled through nonbiased reference along with their technology readiness. Also subsea storage units will be allowing for any chemical injection requirements. Furthermore, a buoy solution will proposed to locate control & monitoring equipment along with a downline connecting the subsea field. The downline will assure communication with subsea equipment and also will allow for filling the subsea storage units with chemicals needed as per flow assurance requirements.
The work is supported by insightful evaluations performed during participation in subsea gas projects. In addition, latest technologies/ solutions assuring tie-back with existing infrastructure and related potential de-rating requirements will be integrated to the overall system. The paper concludes with nuclei scalable architecture solving common marginal field challenges while assuring cost effective development. An illustrative example will be worked out.
The intended work is unique as it combines innovative technologies in a novel efficient architecture allowing for low cost marginal field development.
An Effectiveness Descaling Production Tubing Utilizing a Torque Action Debris Breaker TADB to Restore Wellbore Accessibility in Offshore Oil Wells
Authors: Al-Dhafeeri Abdullah M. , Al-Dhaferi Yousef R. , Al-Enezi Bader M. , Al-Fawwaz Abdullah A. , Atwa Elessawy A. 
Published: November 2020

Abstract
Several offshore oil wells suffer from the most common production problems which are related to scale blockages. The subject wells can not reach the target recommended production rate as well as issues with their wellbore accessibility. The scale can develop at different depths from production tubing. Based on the well intervention and integrity surveys in offshore fields, the subject wells can not be accessible due to an obstacle from scale deposition at a wellbore level.
An attempt to perform a downhole scale clean-up job using slickline operation was recommend as a field trial for first time in offshore oil field. Torque Action Debris Breaker (TADB) tool was utilized effectively in order to restore wellbore Accessibility in terms of scale removal and debris cleaning. The TADB tool is jarred down mechanically in oil wells as a unique helically split torque sub applies a short duration torque to its cutter below with each downward jar. The main torque body can be 1 ½ in, 1 7/8 in, and 2 ½ in with a number of different shoe sizes which it can be easily changed on the main body of the tool as a scale to be broken inside the wellbore.
Based on the evaluation results, the scale removal tool (TADB) can be mechanically efficient depending on the location of the scale and its physical properties. This tool showed overall reduction in time required for descaling, thus reducing intervention time and costs. In addition, an exist of left hand threads within the tool can prevent tool back-off-down-hole. It is also indicated that the tool provides cost-effective results compared to other descaling alternative such as mechanical, chemical or sonic break-down. The approach made a tremendous cost avoidance of utilizing rig operation in order to remove hard scale in the production tubing in oil wells.
The paper covered a first field trial in offshore oil wells utilizing a Torque Action Debris Breaker (TADB) tool to remove different type of scales deposition. The approach applied deep investigation by slickline operation technique to have better results based on a well selection and a problem identification. This paper illustrated the best practices on tool utilization in terms of designing, operating, and monitoring with a long-term strategy execution for advanced solutions related to scale issues at a wellbore level.
3D Static Model Robustness Indicator: A Sequential Blind Test Implementation in Abu Dhabi
Authors: Rodriguez Gonzalez Jose Gregorio , Al Blooshi Farah 
Published: November 2020

AbstractBlind testing is an intuitive and well-known process for assessing how predictive an estimator is. The technique proposed below applies blind testing sequentially in a 3D geostatistical model. It is an adaptation of "Jacknife" technique to investigate and select the best geomodeling parameters and scenarios. As any method, it has its applications and limitations that are discussed in the paper. First, the 3D model is populated using all the available observations (ni), then the 3D estimation is recalculated with one observation less (n−1). For example, a data point is taken out at location Oth; then a comparison is made at this location (Oth) between the results from the extrapolation (i*ith) and the true value (ith). This is known as a single blind test. However, if the process is repeated for all samples, one at a time, bringing the previous sample back, it becomes sequential. This process continues through all the samples (n) or through a selected group of samples (a selective sequential blind test).The error (ei) will be contrasted with a pass/fail threshold (PASS), such that if ei&gt; PASS, then the test fails, and if ei ≤ PASS, it will pass. The number of well locations, Oth, that pass the test indicate the predictive nature of the estimation.The Sequential Blind Test (SBT) results can be used as a ranking element for multiple realizations and/or scenarios and measure the 3D geomodeling quality before the model is sent to simulation. The application of SBT is extended to selecting model parameters, algorithms, and geological assumptions to maximize predictive ability (P).SBT has been implemented in an onshore Abu Dhabi Gas where more than 4 geological scenarios and close to 12 parameters and modeling approaches were made. The SBT became a statistical tool to rank and select the most likely case (more robust) among them that was brought to simulation.
Evaluating Distribution of Circulating Temperature in Wellbores using Drilling Microchips
Authors: Li Bodong , Dokhani Vahid , Zhan Guodong David , Sehsah Ossama 
Published: November 2020

AbstractCritical decision making requires accurate downhole data to determine proper action for drilling operation. Using cost-effective drilling microchips to collect near real-time temperature distribution in the wellbore is valuable to accurately predict control parameters such as drilling fluid rheology and density under in-situ conditions. This study presents an iterative method to calibrate microchip measurements with depth. The accuracy of a transient thermal model to simulate the measured temperature profile is also investigated.Multiple sets of microchips were deployed into two wells, a vertical and an inclined well, to describe the dynamic temperature distribution along the wellbores. The retrieved data from drilling microchips are compiled and processed. An iterative algorithm is developed to calibrate the microchips time-scale to depth considering slippage of microchips in drillpipe and annulus. An in-house transient thermal model is modified to predict the variation of wellbore temperature during microchips circulation.It is shown that the true velocity of tracers in each interval depends on the flow regime and the rheological properties of drilling fluid. Comparing the predicted arrival times of tracers with the actual arrival times conclude that the maximum fluid velocity in each interval shall be used for calibration of tracers' data. The study concludes that the prediction of the transient model differs from the measured temperature profile for downhole intervals in early times. The discrepancy can be attributed to unknown prior temperature distribution in the wellbore, inaccurate thermal properties of the drilling fluid, and inaccurate empirical correlations.This new integrated measurement and analysis method can be used as a primary input for well planning, cementing design and a diagnostic tool for potential problems such as lost circulation. This study provides new insights about the temperature distribution along the wellbore and elaborates on the necessity to address the transient processes in wellbores particularly during startups in deepwater drilling.
A Cloud–Based Smart Engineering and Predictive Computation System for Pipeline Design and Operation Cost Reduction
Authors: Chowdhury Khairul , Lamacchia Diego , Frenk Feldman Vily , Mallik Akash , Rahman Israjur , Alam Zahedul 
Published: November 2020

AbstractConsidering the low and volatile oil and gas prices, cost reductions are an impending challenge in the oil and gas industry. For example, offshore pipelines constitute approximately 12% of total CAPEX and potentially 30% of the total OPEX of the entire offshore field development cost. Workforce shortages and manual engineering processes have a high cost impact due to the excessive time to optimize pipeline design, construction, and operation for a robust and efficient system. If the design phase takes excessive time, it is difficult to optimize cost for CAPEX and OPEX while managing risks, with the added complexity of price uncertainties in the market due to unforeseen geopolitical instability. The objective of this paper is to present an artificial intelligence and automation driven pipeline engineering computation system for performing pipeline design and integrity engineering that can save substantial engineering time with a better and optimized design that can accommodate to crude price uncertainty.A comprehensive computational framework is utilized for pipeline conceptual/preliminary, FEED and operational integrity analysis. The digital computational framework is equipped with a distributed data intake, a robotics process automation, and a predictive analytics computation system. A self-service 3D digital twin creator is utilized to operate from the front end and receiving intelligence from the analysis. The automated design engineering engine is utilized to perform and create ready reports for conceptual/preliminary and FEED/Detail design of a subsea pipeline system. A predictive analytics computation system is utilized to perform pipeline operational integrity using in-line-inspection data and life prediction.The computational system was tested for a subsea flowline conceptual, FEED level design (i.e. pipeline thermo-mechanical and on-bottom-roughness FEA analysis) and thermomechanical structural integrity during the operational phase. The design time was substantially lower than the traditional manual pipeline design suggested a saving as much as 80% of the engineering time. The computation engine was also tested to conduct pipeline integrity using machine learning from direct in-line-inspection data. The flexibility and scalability of the computational framework allow incorporation of any organization's existing pipeline data and engineering process.The significant saving in the engineering time should be able to reduce contingency in hedging due to the price uncertainty. The novel computational framework is made available online offers real-time on-demand analytics for instant intelligence for making decisions for both in design and in operational phases. Reusing of all engineering efforts over the life of the pipeline or for other projects is another possibility offered by the cloud-based computational framework.
Generating Green Value from Data: Applying AI–Based Analytics to Monitor and Manage Energy Usage Across Oil and Gas Operations
Authors: Blumenthal Ralf , El Naser Ammar , Blug Christian 
Published: November 2020

Abstract
As upstream E&P operators increase their investments in digitalization and artificial intelligence to improve their cost structures and competitive advantage, they have a tremendous opportunity to generate green value from their energy usage data. In doing so, they can tap hidden sources of energy savings using cloud-based managed services, such as Energy Efficiency Analytics (EEA) and Power Quality Analytics (PQA). Both require high-frequency sampling rates of the energy usage by such common capital assets in E&P facilities as rotating equipment, compressors, and pumps. This paper explains how EEA and PQA work as well as the advantages of these managed services being cloud-based. Use cases are discussed to illustrate their operation and capabilities.