bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uncertainty Quantification of Allen-Cahn Phase Field Parameters in Multiphysics Simulation of Oil Shale Radio Frequency Heating
Authors: Ramsay Travis 
Published: September 2021

Abstract
Radio frequency (RF) heating represents a dielectric heating technique for converting kerogen-rich oil shale into liquid oil through in-situ pyrolysis. This process can be modeled using a multiphysics finite element based coupled thermal, phase field, mechanical and electromagnetic (TPME) numerical framework. This work focuses on the combination of a two-dimensional (2D) TPME multiphysics simulation with uncertainty quantification (UQ) that incorporates the Allen-Cahn phase field parameters, specifically those which describe the associated reaction-diffusion process as electromagnetic energy being converted to thermal energy in the RF heating process. The breadth of UQ performed in this study includes not only the Allen-Cahn parameters but also selected thermal, statistical rock-type distribution in the geological model, as well as electromagnetic parameters of the applied quasi-static Maxwell equation. A Non-Intrusive Polynomial Chaos (NIPC) is used for: considering the affect of Allen-Cahn phase field parameters on the evaluation of plausible conversion timelines of TPME simulation and the evaluation of summary statistics to predict the order of Polynomial Chaos Expansion (PCE) that is representative of full kerogen-rich zonal conversion response in a geologically descriptive finite element model. A sparse representation of polynomial chaos coefficients is highlighted in the process of computing summary statistics for the complex stochastically-driven TPME simulation results. Additionally, Monte Carlo (MC) simulations were performed in order to validate the results of the sparse NIPC representation. This is done considering MC is a widely recognized stochastic simulation process. Additionally, NIPC was used to illustrate the potential performance improvement that are possible, with a sparse polynomial chaos expansion enhanced by the incorporation of Least Angle Regression (LAR), as compared to MC simulation. Although the parametic uncertainty of the reaction-diffusion parameters of the Allen-Cahn was comprehensive, they did not accelerate the conversion timelines associated with the full zonal conversion of the kerogen-rich rock type in the statistical simulation results. By executing the stochastic simulations for a greater length of time the extent of full zonal conversion is examined in the RF modeling.
Development of Oil Based Foam for Pressurized Mud Cap Drilling PMCD Applications
Authors: Wu Qian , Penny Glenn , Rao Sai Sashankh , Mollica Juan , Samdani Ganesh Arunkumar , Ewelike Chioma , Zeilinger Sabine , Gupta Vishwas Paul 
Published: September 2021

Abstract
Pressurized Mud Cap Drilling (PMCD) technique is typically applied for drilling formations with natural fractures and large vugs that result in severe or total losses. The density of the drilling fluid used in PMCD is slightly below reservoir pore pressure. In the case of very low reservoir pressures below base oil densities (~6.7 ppg), foam can be an option. This paper presents a methodology to develop an oil-based foam system for a PMCD application. The scope includes the descriptions of a foam PMCD application, functional requirements of foam, the development workflow, testing procedures, and modeling that are necessary to qualify a foam for PMCD application. The development methodology first involves identifying the constraints and well conditions of a given PMCD application; these include wellpath, hole and casing sections, reservoir pressure and temperature, surface pumping pressure limits (typically a rotating control device limit), foam stability requirements, etc. The above constraints drive the performance requirements for the foam. Next comes the design of the foam formulation and evaluating its performance against these requirements through various lab tests and modeling efforts, which can include ambient pressure half-life tests for initial screening, static and rotational stability tests at in-situ well conditions, rheology tests, solubility tests, pressure transmission estimates, gas migration estimates, and hydraulics modeling. Example results from the lab tests and modeling are shown to provide more insights into the development process. The proposed methodology may be used as a guide to design a foam drilling fluid for a PMCD application. The iterative nature of the development method is shown and is driven by the functional requirements that are coupled to each other. For example, a more viscous foam may be more stable but it may not be pumpable. Likewise, a less viscous foam may be pumpable but may not be sufficiently stable. Similarly, a highly compressible foam may not be good at pressure transmission to monitor downhole pressure variations as compared to a less compressible foam. In summary, the methodology described in this paper explains the development of an oil-based foam for a PMCD application that satisfies a set of operational constraints and functional requirements while highlighting the major factors that could impact foam performance. The application of foam to PMCD is a new concept and to our knowledge has not been applied in the field; in significantly depleted reservoirs this may become a viable option.
Numerical RTA in Tight Unconventionals
Authors: Carlsen Mathias Lia , Bowie Braden , Dahouk Mohamad Majzoub , Mydland Stian , Whitson Curtis Hays , Yusra Ilina 
Published: September 2021

Abstract
We extend the numerically-assisted RTA workflow proposed by Bowie and Ewert (2020) to (a) all fluid systems and (b) finite conductivity fractures. The simple, fully-penetrating planar fracture model proposed is a useful numerical symmetry element model that provides the basis for the work presented in this paper. Results are given for simulated and field data.
The linear flow parameter (LFP) is modified to include porosity (LFPꞌ=LFP√φ). The original (surface) oil in place (OOIP) is generalized to represent both reservoir oil and reservoir gas condensate systems, using a consistent initial total formation volume factor definition (Bti) representing the ratio of a reservoir HCPV containing surface oil in a reservoir oil phase, a reservoir gas phase, or both phases.
With known (a) well geometry, (b) fluid initialization (PVT and water saturation), (c) relative permeability relations, and (d) bottomhole pressure (BHP) time variation (above and below saturation pressure), three fundamental relationships exist in terms of LFPꞌ and OOIP. Numerical reservoir simulation is used to define these relationships, providing the foundation for numerical RTA, namely that wells: (1) with the same value of LFPꞌ, the gas, oil and water surface rates will be identical during infinite-acting (IA) behavior; (2) with the same ratio LFPꞌ/OOIP, producing GOR and water cut behavior will be identical for all times, IA and boundary dominated (BD); and (3) with the same values of LFPꞌ and OOIP, rate performance of gas, oil, and water be identical for all times, IA and BD. These observations lead to an efficient, semi-automated process to perform rigorous RTA, assisted by a symmetry element numerical model.
The numerical RTA workflow proposed by Bowie and Ewert solves the inherent problems associated with complex superposition and multiphase flow effects involving time and spatial changes in pressure, compositions and PVT properties, saturations, and complex phase mobilities.
The numerical RTA workflow decouples multiphase flow data (PVT, initial saturations and relative permeabilities) from well geometry and petrophysical properties (L, xf, h, nf, φ, k), providing a rigorous yet efficient and semi-automated approach to define production performance for many wells.
Contributions include a technical framework to perform numerical RTA for unconventional wells, irrespective of fluid type. A suite of key diagnostic plots associated with the workflow is provided, with synthetic and field examples used to illustrate the application of numerical simulation to perform rigorous RTA. Semi-analytical models, time, and spatial superposition (convolution), pseudopressure and pseudotime transforms are not required.
An Experimental Investigation of Mass Transfer in Tight Dual-Porosity Systems
Authors: Alahmari Saeed , Jessen Kristian 
Published: September 2021

Abstract
During gas injection in ultra-tight fractured reservoirs, molecular diffusion can play a dominant role in the mass transfer process and enhance recovery by extracting oil components from matrix and delaying gas breakthrough. There has been a growing interest from scholars and operators to study the effect of diffusive mass transfer on the potential incremental recovery from CO2 and rich gas injection. However, many fundamental questions pertaining to the physics of multicomponent multiphase flow and transport are still left unanswered.
This paper aims to improve the understanding of multicomponent diffusive mass transfer between matrix and fracture blocks through experimental and modeling work. Displacement experiments were carried out using analog fluids and mesoporous medium to effectively isolate and study the relevant physical mechanisms at play. The experiments were performed in packed columns utilizing silica-gel particles that have internal porosity. The particle size is 40-70 micron with highly controlled internal pore size of 6 nm that makes up approximately 50% of the overall porosity. The quaternary analog fluids system consists of Water, Methanol, Isopropanol, and Isooctane, was used because it mimics the phase behavior of CO2, Methane, Butane and Dodecane mixtures at 2,280 psi and 100°C. Our selection of the analog fluid system and porous medium allowed us to investigate matrix-fracture fluid exchange as observed during an enhanced recovery operation in an ultra-tight fractured system. The effluents from these displacement experiments served as the basis for our analysis of diffusive mass transfer.
The role of molecular diffusion in the displacement experiments was investigated by first performing separate diffusion experiments to obtain diffusion coefficients for all relevant binary mixtures. Infinite dilution diffusion coefficients were measured for all binary mixtures and then used to model binary and multicomponent diffusion coefficients over the whole composition range. The accuracy of this approach was determined by performing additional binary diffusion experiments over a broader range of compositions.
The displacement experiments were simulated using an in-house simulator and excellent agreement was obtained: The extensive experimental/modeling work related to the diffusion coefficients of the analog fluid system was used in interpreting the diffusive mass transfer between the matrix (stagnant) and fracture (flowing) domains via a 1D linear model.
The presented work provides new insights into the role of diffusive mass transfer in ultra-tight fractured systems and builds a framework to highlight the critical data needed to effectively characterize and simulate recovery from such complex geological settings.
A Physics-Constrained Data-Driven Workflow for Predicting Coalbed Methane Well Production Using A Combined Gated Recurrent Unit and Multi-Layer Perception Neural Network Model
Authors: Yang Ruiyue , Liu Wei , Qin Xiaozhou , Huang Zhongwei , Shi Yu , Pang Zhaoyu , Zhang Yiqun , Li Jingbin , Wang Tianyu 
Published: September 2021

Abstract
Coalbed methane (CBM) has emerged as one of the clean unconventional resources to supplement the rising demand of conventional hydrocarbons. Analyzing and predicting CBM production performance is critical in choosing the optimal completion methods and parameters. However, the conventional numerical simulation has challenges of complicated gridding issues and expensive computational costs. The huge amount of available production data that has been collected in the field site opens up a new opportunity to develop data-driven approaches in predicting the production rate. Here, we proposed a novel physics-constrained data-driven workflow to effectively forecast the CBM productivity based on a Gated Recurrent Unit (GRU) and Multi-Layer Perceptron (MLP) combined neural network (GRU-MLP model). The model architecture is optimized by the multiobjective algorithm: nondominated sorting genetic algorithm Ⅱ (NSGA Ⅱ). The proposed framework was used to predict synthetic cases with various fracture-network-complexities and two multistage-fractured wells in field sites located at Qinshui basin and Ordos basin, China. The results indicated that the proposed GRU-MLP combined neural network was able to accurately and stably predict the production performance of multi-fractured horizontal CBM wells in a fast manner. Compared with Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the proposed GRU-MLP had the highest accuracy and stability especially for gas production in late-time. Consequently, a physics-constrained data-driven approach performed better than a pure data-driven method. Moreover, the optimum GRU-MLP model architecture was a group of optimized solutions, rather than a single solution. Engineers can evaluate the tradeoffs within this set according to the field-site requirements. This study provides a novel machine learning approach based on a GRU-MLP combined neural network model to estimate production performances in CBM wells. The method is simple and gridless, but is capable of predicting the productivity in a computational cost-effective way. The key findings of this work are expected to provide a theoretical guidance for the intelligent development in oil and gas industry.
The New Development of Soft Microgel Particle Flooding Technology –From Theoretical Research in Laboratory to Field Trial
Authors: Sun Zhe , Wang Xiujun , Kang Xiaodong 
Published: September 2021

Abstract
Although polymer flooding technology has been widely applied and achieved remarkable effect of increasing oil. Yet the "entry profile inversion" phenomenon occurs inevitably in its later stage, which seriously affects the development effect. In recent years, the soft microgel particle dispersion is a novel developed flooding system. Due to its excellent performance and advanced mechanism, it can slow down the process of profile inversion, and achieve the goal of deep fluid diversion and expanding swept volume.
The soft microgel particle dispersion consists of microgel particles and its carrier fluid. After coming into porous media, it shows the properties of "plugging large pore and leave the small one open" and the motion feature of "trapping, deformation, migration". In this paper, reservoir adaptability evaluation, plugging and deformation characteristics of soft microgel particle dispersion in pore throat is explored by using the microfluidic technology and 3D Printing technology. On this basis, by adopting the NMR and CT tomography technology, the research on its oil displacement mechanism is further carried out. Furthermore, the typical field application case is analyzed.
Results show that, soft microgel particles have good performance and transport ability in porous media. According to the reservoir adaptability evaluation, the size relationships between particles and core pore throat is obtained, to provide basis for field application scheme design. Through microfluidic experiments, the temporary plugging and deformation characteristics of particles in the pore throat are explored. Also, when injecting soft microgel particle into the core, the particle phase separation happens, which makes the particles enter and plug the large pore in the high permeability layer. Therefore, their carrier fluid displace oil in the small pore, which works in cooperation and causes no damage to the low permeability layer. Furthermore, by using NMR and CT techniques, its micro percolation law in porous media and remaining oil distribution during displacement process is analyzed. During the experiment, microgels presents the motion feature of "migration, trapping, and deformation" in the core pore, which can realize deep fluid diversion and expand swept volume. From 3D macro experiment, microgels can realize the goal of enhance oil recovery. Finally, the soft microgel particle dispersion flooding technology has been applied in different oilfields, such as Oman, Bohai and other oilfields, which all obtained great success.
Through interdisciplinary innovative research methods, the oil displacement mechanism and field application of soft microgel particle dispersion is researched, which proves its progressiveness and superiority. The research results provide theoretical basis and technical support for the enhancing oil recovery significantly.
Sand Mass Production in Anisotropic Stresses From Lab to Field Predictions
Authors: Papamichos Euripides , Walle Lars Erik , Berntsen Andreas Nicolas , Szewczyk Dawid 
Published: September 2021

Abstract
Sand onset and sand rate predictions are important in hydrocarbon production to optimize production, increase recovery, and reduce costs and the environmental footprint. Recent laboratory results on Castlegate sandstone from sand production tests in a True Triaxial test system have revealed that stress anisotropy plays an important role not only on sand onset but also in sand rate. The results confirmed our hypothesis that stress anisotropy means earlier sand produced but less sand. The laboratory results also revealed the effect of fluid saturation, i.e., oil, brine or irreducible water saturation on sand onset and sand rate. They allow the calibration of SandPredictor, a field sand prediction model, for stress anisotropy and production before and after water breakthrough. A field case analysis demonstrated the effects and showed the importance of in situ stress anisotropy and watercut on sand mass and rate.
High Resolution Solid State Acoustic Imaging for Advanced Well Integrity and Deformation Assessments in Conventional and Unconventional Wells
Authors: Littleford Thomas , Battistel Anthony , Simpson Greer , Wardynski Kacper 
Published: September 2021

Abstract
An advanced high-resolution acoustic imaging technology was deployed for well integrity and deformation assessments in both vertical and horizontal wells. This high frequency acoustic tool collected three-dimensional data quantifying deformation and wall thickness with resolution unobtainable by existing multi-finger caliper, magnetic flux leakage, and rotating single element ultrasonic systems. Several novel imaging methods are enabled by the high number of transducers (up to 512) on the imaging probe. These methods, including beam forming, beam steering and semi-stochastic multipulse imaging, are outlined and discussed in this paper. In addition, multiple types of standardized visualizations enabled by this high-resolution 3D data capture tool are introduced and examples of each are shown.
Lab qualification and imagery generated by the high-resolution solid-state imaging technology, when applied to various precision machined geometric anomalies, are presented. In addition to lab validation results, several field studies are showcased including assessments of ovalized casing, complex downhole corrosion, and isolated minor pitting. Leak paths, splits, and damaged regions within threaded casing collars were also identified, imaged, and quantified using the acoustic technology. Until now, these collar regions have been very difficult to image using legacy downhole tools due to fundamental limitations at the threaded connection geometry. Lastly, various downhole completion equipment case studies are presented showcasing several applications of acoustic imaging used to validate the set-position or condition of specialty downhole equipment. This paper outlines the usage of the solid-state acoustic technology to generate three dimensional geometry and wall thickness datasets with sub-millimetric resolution, providing operators with a holistic and actionable assessment of their well integrity.
An Efficient Hydraulic Fracture Geometry Calibration Workflow Using Microseismic Data, Geomechanics, DFN Models, and History Matching
Authors: Leines-Artieda Joseph Alexander , Liu Chuxi , Yang Hongzhi , Wu Jianfa , Chang Cheng , Yu Wei , Sepehrnoori Kamy 
Published: September 2021

Abstract
Reliable estimates of hydraulic fracture geometry help reduce the uncertainty associated with estimated ultimate recovery (EUR) forecasts and optimize field developing planning in unconventional reservoirs. For these reasons, operators gather information from different sources with the objective to calibrate their hydraulic fracture models. Microseismic data is commonly acquired by operators to estimate hydraulic fracture geometry and to optimize well completion designs. However, relying solely on estimates derived from microseismic information may lead to inaccurate estimates of hydraulic fracture geometry.
The objective of this study is to efficiently calibrate hydraulic fracture geometry by using microseismic data, physics-based fracture propagation models, and the embedded discrete fracture model (EDFM). We first obtain preliminary estimates of fracture geometry based on microseismic events’ spatial location and density with respect to the perforation cluster location. We then tune key completion parameters using an in-house fracture propagation model to provide hydraulic fracture geometries that are constrained by the microseismic cloud. In the history matching process, we included the effect of natural fractures, using the microseismic events location as natural fracture initiation points. Finally, we used cutoff coefficients to further reduce hydraulic fracture geometries to match production data.
The results of this work showed a fast and flexible method to estimate fracture half-lengths and fracture heights, resulting in a direct indicator of the completion design. Additionally, hydraulic-natural fracture interactions were assessed. We concluded that the inclusion of cutoff coefficients as history matching parameters allows to derive realistic hydraulic and natural fracture models calibrated with microseismic and production data in unconventional reservoirs.
Welcome to the 21st Century for Project Managers
Authors: Thompson Analise , Chaban Fady , Strathman Tony , Gönczi David 
Published: September 2021

Abstract
If the O&G industry adopted new mail technology at the same rate it adopts project management technologies, it would still be using the Pony Express. Risk aversion and resistance to change are two of the main reasons for project failure across the industry. The industry still solves problems by throwing a bunch of human resources at the issue. The more people in the room the better the solution will be, right? In the 21st century, project management needs be based on the interaction of technology with human behavior. The objective of this paper is to introduce the industry to project management in the 21st century.
In today's ever-changing global economy, the definition of success is just as fluid, and project management must be agile enough to deal with this. Finding something that works and then sticking to it for decades will no longer suffice. Modern technology companies take a unique approach to major project management which continually polls for changes and empowers individual employees to use their own best judgement while maintaining coordination with their fellows. An examination of this approach can provide helpful insight into optimizing the use of available resources, human or otherwise.
Today's top technologies make it easy for individual team members to continuously update and record the progression of the project, and helps employees work toward better solutions rather than limiting themselves to the original requirements and company protocol. Employees are empowered to look for solutions, think out of the box and outside of what is currently available in-house. In the 21st century, the solution to problems is not a complex spreadsheet shared on SharePoint, it's an elegant integration of technology that optimizes human performance as shown in this case study.