bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reduced-Degrees-of-Freedom Gaussian-Mixture-Model Fitting for Large-Scale History-Matching Problems
Authors: Gao Guohua , Jiang Hao , Chen Chaohui , Vink Jeroen C. , El Khamra Yaakoub , Ita Joel , Saaf Fredrik 
Published: September 2019

Summary
It has been demonstrated that the Gaussian-mixture-model (GMM) fitting method can construct a GMM that more accurately approximates the posterior probability density function (PDF) by conditioning reservoir models to production data. However, the number of degrees of freedom (DOFs) for all unknown GMM parameters might become huge for large-scale history-matching problems. A new formulation of GMM fitting with a reduced number of DOFs is proposed in this paper to save memory use and reduce computational cost. The performance of the new method is benchmarked against other methods using test problems with different numbers of uncertain parameters. The new method performs more efficiently than the full-rank GMM fitting formulation, reducing the memory use and computational cost by a factor of 5 to 10. Although it is less efficient than the simple GMM approximation dependent on local linearization (L-GMM), it achieves much higher accuracy, reducing the error by a factor of 20 to 600. Finally, the new method together with the parallelized acceptance/rejection (A/R) algorithm is applied to a synthetic history-matching problem for demonstration.
How Can Drilling Engineers Help Revolutionize Space Transport and Colonize the Solar System: Focusing on Lunar Water-Ice
Authors: Joshi Deep , Eustes Alfred , Rostami Jamal , Gottschalk Colby , Dreyer Christopher , Liu Wenpeng , Zody Zachary , Bottini Claire 
Published: September 2019

Abstract
Water is considered the ‘oil of space’ with applications ranging from fuel production to colony consumption. Recent findings suggested the presence of water-ice in the Permanently shadowed craters on Lunar poles. This water present on the Moon and other planetary bodies can significantly bring down the cost of space exploration, fueling the colonization of the solar system. With low-resolution orbital data available, the next step is to drill and analyze samples from the Moon.
An extensive review of drilling systems designed by NASA was conducted focusing on the effect of different planetary environments on the drill design. Inspired by this and the drilling systems developed in the petroleum industry, an auger based rotary drilling rig was designed and fabricated with an extensive high-frequency data acquisition system, measuring all essential drilling parameters. Several analog rocks were cast with regolith simulant grout to replicate different subsurface geotechnical properties in the Lunar polar craters. The drill was tested on samples with different geotechnical properties to account for the varying properties expected in the Lunar poles.
Application of the drilling engineering concepts has resulted in the development of a robust drilling system capable of replicating drilling process for different planetary environments like the Moon and Mars. Using the data acquisition system on the rig, an advanced machine learning algorithm capable of processing and analyzing the real-time high-frequency drilling data to estimate a sample's geotechnical properties and water content was created. The evolving algorithm was developed based on initial drilling tests on homogenous and heterogeneous analogs. It was tested on samples with varying heterogeneity to estimate the geotechnical properties and the water content accurately. With some modifications, this algorithm can be applied in the Lunar and Martian missions to estimate the geotechnical properties in real-time, without the need to analyze the subsurface samples on the surface. This can result in a cost-effective exploration of water-ice resources on the Moon and Mars, kickstarting the space resources industry and the human colonization on those planetary bodies. The expertise of the drilling engineers in designing and executing wells in extreme terrestrial environments can help create significantly effective drilling systems for extraterrestrial environments.
This work details the design considerations to drill on the Moon and other planetary bodies focusing specifically on the application of drilling data to evaluate geotechnical properties and water content at Lunar polar conditions. The techniques developed here might pay a vital role in understanding the extent and composition of water-ice on the Moon, leading to efficient colonization of the solar system.
West Nile Delta Campaign Delivering Energy to Egypt
Authors: Khedr Sherine , El-dabi Fady , Nashaat Mohamed , Mohiuldin Ghulam , Galal Alaa , Slim Teddy , Hughes Andrea , Morris Lyndsay , Ramsay David , El-wakeel Wael , Mubarak Hussein , Smith Jeffrey , Munger Robert 
Published: September 2019

Abstract
Giza Fayoum Completions was the second campaign of the West Nile Delta project. The campaign consisted of eight cased-hole gravel pack subsea wells. The Giza Fayoum campaign was sanctioned in August 2017 with an execution start date five months later. In this time, the well designs were finalized, downhole completion equipment manufactured, and the execution plan approved. A high rate water pack sand control technique was designed to deliver an estimated production rate of 120 MMscf/d / well. It was planned to deliver eight wells over a period of 5 months from Q1 2018 giving an average of two and a half weeks per well. Seven of the eight wells were cleaned up through a large bore completion landing string system. Each well was flowed to high rate temporary well test equipment installed on the DP semi-submersible rig to a gas rate of 65 MMscf/d, with PLT logs conducted.
This successful, fast-paced campaign is the result of applying lessons learned from the former campaign; Taurus Libra and identifying additional efficiencies that would improve performance. The design similarities between the two campaigns permitted the team to extend the learning curve and deliver superb performance on Giza Fayoum.
As for safety performance, the campaign was delivered without any lost time incident. A rigorous approach to continuous improvement resulted in reducing the completion time to 12 days per well (not including rig move, de-suspension and suspension activities). The optimized bean up procedures supported by PLT data made it possible to reduce greenhouse emissions by 20%. The sand control technique resulted in a significant reduction of total skins. Moreover, the team succeeded in delivering the wells safely, ahead of plan and under budget while adhering to BP's overarching strategy of delivering safe, compliant and reliable wells.
The efficiencies, safety culture and technology used during this campaign are now being set as the standard for future campaigns in Egypt and beyond.
The Extended Whittle-Agarwal Equation for Investigated Volume Under Mass Recharge - Theoretical Foundations and Application to Channel-Sand and Unconventional Reservoirs
Authors: Kubota Leonardo , Keidel Conrado 
Published: September 2019

Abstract
Whittle (2008) proposes an analytical method based on pressure-rate deconvolution to determine the minimum tested volume during a transient well test. Agarwal (2010) also presents a similar equation. Let us refer to it as Whittle-Agarwal Equation (WAE).
The purpose of this paper is threefold: (1) provide a theoretical foundation for WAE based on the method of Succession of Pseudo-Steady States, Shahamat (2014), (2) propose the Extended WAE, which corrects the investigated volume from WAE to account for the presence of mass recharge, due to aquifer influx, gas or water injection in a neighboring well, and (3) provide evidence that the Extended WAE yields sound results by applying it to two field cases: (i) oil producer under the influence of a nearby gas injector, both wells located in a channel-sand reservoir, and (ii) oil producer in unconventional reservoir. Attention is drawn to the fact the original WAE will overestimate the investigated volume if mass recharge is present.
Matching of Pilot Huff-and-Puff Gas Injection Project in the Eagle Ford Shale Using a 3D 3-Phase Multiporosity Numerical Simulation Model
Authors: Fragoso Alfonso , Lopez Jimenez Bruno A. , Aguilera Roberto , Noble Graham 
Published: September 2019

Abstract
Production of oil from pilot shale wells has generally increased by implementing huff-and-puff (H&P) gas injection. The objective of this paper is using a new 3D, 3-Phase, physics-based, multiporosity model for matching and understanding primary oil production as well as recovery by H&P gas injection from a pilot well in the Eagle Ford shale.
History matching and performance forecast are carried out with a newly-developed fully-implicit 3D multi-phase modified black-oil finite difference numerical model, which uses a multiple porosity approach. "The model is capable of handling five storage mechanisms, including (1) organic porosity, (2) inorganic porosity, (3) natural fracture porosity, (4) adsorbed porosity, and (5) hydraulic fracture porosity" (Lopez Jimenez and Aguilera, 2019). Furthermore, the model has capabilities to handle dissolved gas in the solid part of the organic matter, adsorption/desorption from the organic walls, and stress-dependent properties of natural and hydraulic fractures.
These storage and fluid flow mechanisms, as well as the stress-dependency of hydraulic fractures, are widely recognized in the case of some shale petroleum reservoirs. Their inclusion in our simulation model permits evaluating the effect of these mechanisms during H&P gas injection. Results of the simulation, presented as cross-plots of production rates and cumulative production vs. time, indicate that oil recovery from shale petroleum reservoirs can be increased significantly by H&P gas injection. The possibility of desorption and gas diffusion is investigated.
The approach implemented in this H&P history match of an Eagle Ford pilot well should prove of value for simulating complex shale reservoirs.
Processing Asset Data at the Intelligent Edge: Implementation of an Industrial IoT Architecture to Drive Business Value
Authors: Joy David , Smith Doug 
Published: September 2019

Abstract
An Industrial IoT architecture is presented which enables edge computing of data collected from rotating machinery in a model conducive to multiple use cases. The architecture is compared to traditional "IoT data to the cloud" models and on-premise point solutions, illustrating the effectiveness of computing at the edge with a generic IoT solution. Customer benefits from the solution are presented and discussed. The initial solution framework of asset condition monitoring and predictive maintenance is provided by on-premise data analytics software incorporating historical models of asset behavior. Computer hardware traditionally available only in data centers is available to provide powerful computing capabilities. A wireless communications mesh is implemented to provide secure connectivity between plant assets, computers and workers. A digital twin is constructed and augmented reality methods are implemented to extract information from the raw data. Inclusion of plant personnel from planning, to installation, to implementation has been critical to the acceptance and use of the technology in operations. Implementation of a generic IoT architecture rather than a point solution increases the value obtained from the initial investment as subsequent solution frameworks are added. An IoT platform operating at the "intelligent edge" can be more efficient and less costly than cloud-based solutions, reducing time from acquired data to corrective action. An architecture which accounts for role-based and location-aware data presentation further increases the value of the data to the individual worker. Just bringing sensor data to an IoT cloud platform does not create business value from the data. Ensuring interoperability, connectivity, adherence to industry standards and integration of security and manageability are all prerequisites to architecting a scalable Industrial IoT solution that delivers value to the customer. Additional important benefits to the customer are safety, management of change and personal growth of the employees using the technology.
Effect of Compaction on Oil Recovery Under Low Salinity Flooding in Homogeneous and Heterogeneous Chalk
Authors: Hao Jiasheng , Shapiro Alexander 
Published: September 2019

Abstract
Compaction of the reservoir chalk (e.g. surface subsidence) may facilitate oil production. However, only few works have linked smart water flooding with chalk compaction and additional oil recovery. In this work, core flooding experiments with sequential injection of low salinity brines were performed to examine the effect of chalk compaction on oil recovery under smart water flooding. X-ray computer tomography scanning was applied to select outcrop and reservoir cores with different level of heterogeneity, which was demonstrated to be an important factor that determines the recovery even on core scale. A linear variable differential transformer (LVDT) device made it possible to detect changes of the core length during the experiments, which served as an indication of the compaction. Overburden pressure was increased stepwise at the final stages of the flooding to achieve higher compaction of the cores.
During secondary flooding, slight gradual compaction of the cores was observed. Subsequent low salinity flooding did not lead to further compaction for all the samples, nor additional oil recovery. Under final compaction, significantly more oil was produced from the heterogeneous cores, especially, from the reservoir core. Some fines production was observed during the core cleaning after the experiments. Fluid diversion due to closing micro-fractures under compaction and/or relocation of the fines is speculated to be a driving mechanism behind additional recovery from heterogeneous cores. Rock compaction appears to be a potential mechanism for enhanced oil recovery, however with a limited efficiency.
Formation Fluid Sampling Simulation: The Key to Successful Job Design and Post-Job Performance Evaluation
Authors: Kristensen Morten , Dumont Hadrien , Akindipe Tunde , Chugunov Nikita , Garcia German 
Published: September 2019

Abstract
Acquisition of fluid samples using wireline formation testers (WFTs) is an integral part of reservoir evaluation and fluid characterization. The increasing complexity of fluid sampling operations, especially in remote or offshore fields, requires a careful planning process involving systematic de-risking of the sampling objectives through quantitative evaluation of sampling hardware performance under uncertain downhole conditions and reservoir properties. During job execution, the cleanup of mud filtrate is monitored using downhole fluid analysis (DFA) sensor measurements. In addition to quantifying produced contamination and providing guidance for real-time decisions, these measurements hold valuable information about formation and fluid properties that can be extracted through advanced interpretation workflows.
In this paper, we demonstrate how a quantitative, model-based workflow was applied to both planning and interpretation for a series of sampling jobs in a remote and harsh environment. At its core, the workflow consists of high-resolution numerical flow models for the filtrate cleanup process that cover both conventional and focused sampling tools. To enable real-time, interactive, and probabilistic workflows, we use machine learning techniques to construct fast, high-fidelity proxy models, which, after thorough validation, replace numerical simulation in the workflow. Finally, the workflow employs methods for uncertainty quantification, global sensitivity analysis, and model inversion.
During the pre-job planning phase, the model-based workflow was used to select and mobilize the optimal sampling hardware, estimate sampling time uncertainty, and pinpoint the dominant sources of this uncertainty through global sensitivity analysis. After successful sample acquisition, the DFA measurements were reconciled with the cleanup model and the petrophysical evaluation to extract additional value from the measurements. Using measurements of water-cut and pressure, and conditioned to the petrophysical evaluation, the cleanup model was inverted for two-phase relative permeabilities. This recently developed methodology complements laboratory measurements of relative permeability on core samples.
Building on previous work in this area, this paper demonstrates the practical application of advanced planning and interpretation workflows for downhole fluid sampling. The methodology presented couples traditional, full-physics flow modeling with modern machine learning techniques to achieve highly agile workflows, enabling operators to more efficiently plan sampling jobs and extract value from the measurements.
Methods for Probabilistic Uncertainty Quantification with Reliable Subsurface Assessment and Robust Decision-Making
Authors: Tanaka Shusei , Dehghani Kaveh , Wang Zhenzhen 
Published: September 2019

Abstract
Reliability of subsurface assessment for different field development scenarios depends on how effective the uncertainty in production forecast is quantified. Currently there is a body of work in the literature on different methods to quantify the uncertainty in production forecast. The objective of this paper is to revisit and compare these probabilistic uncertainty quantification techniques through their applications to assisted history matching of a deep-water offshore waterflood field. The paper will address the benefits, limitations, and the best criteria for applicability of each technique.
Three probabilistic history matching techniques commonly practiced in the industry are discussed. These are Design-of-Experiment (DoE) with rejection sampling from proxy, Ensemble Smoother (ES) and Genetic Algorithm (GA). The model used for this study is an offshore waterflood field in Gulf-of-Mexico. Posterior distributions of global subsurface uncertainties (e.g. regional pore volume and oil-water contact) were estimated using each technique conditioned to the injection and production data.
The three probabilistic history matching techniques were applied to a deep-water field with 13 years of production history. The first 8 years of production data was used for the history matching and estimate of the posterior distribution of uncertainty in geologic parameters. While the convergence behavior and shape of the posterior distributions were different, consistent posterior means were obtained from Bayesian workflows such as DoE or ES. In contrast, the application of GA showed differences in posterior distribution of geological uncertainty parameters, especially those that had small sensitivity to the production data. We then conducted production forecast by including infill wells and evaluated the production performance using sample means of posterior geologic uncertainty parameters. The robustness of the solution was examined by performing history matching multiple times using different initial sample points (e.g. random seed). This confirmed that heuristic optimization techniques such as GA were unstable since parameter setup for the optimizer had a large impact on uncertainty characterization and production performance.
This study shows the guideline to obtain the stable solution from the history matching techniques used for different conditions such as number of simulation model realizations and uncertainty parameters, and number of datapoints (e.g. maturity of the reservoir development). These guidelines will greatly help the decision-making process in selection of best development options.
Simple, Systematic Reserves Estimation for Unconventional Wells
Authors: Cartier Cheryl , Miller Patrick , Wagner Peter 
Published: September 2019

Abstract
Booking reserves for unconventional, multi-frac wells is a critical business process, but to be done effectively, often requires significant time investment and multiple interpretation techniques. Although reserves can be estimated quickly with decline curve analysis (DCA) alone, the subjectivity in DCA makes it challenging for evaluators to estimate reserves with appropriate levels of uncertainty and maintain consistency between evaluators. The objective of this paper is to present a fast, systematic, yet rigorous methodology for estimating 1P, 2P and 3P estimated ultimate recoveries (EURs) for new wells. This methodology utilizes regression to correlate easy to obtain, early life indicators of well performance to 2P EURs, which have been estimated from more detailed interpretations. Multiple methodologies are presented for estimating 1P and 3P EURs.
Data Driven Modeling and Prediction for Reservoir Characterization Using Seismic Attribute Analyses and Big Data Analytics
Authors: Zhou Xu , Tyagi Mayank , Zhang Guoyin , Yu Hao , Chen Yangkang 
Published: September 2019

Abstract
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.
3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.
In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.