bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated Optimization of Well Placement via Adaptive Space-Filling Surrogate Modelling and Evolutionary Algorithm
Authors: Redouane Kheireddine , Zeraibi Nourddine , Nait Amar Menad 
Published: November 2018

Abstract
Well placement optimization constitutes a fundamental element in the decision-making process needed for optimizing the field development plans. The use of engineering judgment as well the usual practice of trial and error to solve the challenging problem of well placement may be not sufficient. However, the employment of optimization routines coupled to reservoir simulation models as an automatic aiding tool is a current alternative that could improve the decision-making process. Moreover, despite the successes achieved, automatic optimization strategies still suffer from various drawback such as large computational effort, substantial CPU time consumption due to the expensive simulation model, in addition to the difficulties in handling the realistic field constraints.
In this paper, A hybrid intelligent approach for optimizing well placement under field constraints is presented that use a constrained space-filling experimental design, genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS) surrogate model and one proposed adaptive sampling framework, as its basis for optimization. The hybrid intelligent strategy is initiated by uniformly distributing a limited number of design sites in the constrained search space by space-filling design, then simulations are made to generate a database to fit the ANFIS model. Thereafter, a proposed adaptive sampling routine, which combines an intelligent neighborhood search mechanism with GA is applied to augment the quality of surrogate, enhance the accuracy of the framework, and thus guide the optimization rapidly into the optimal solution.
Via a full-field reservoir case that dealing with a realistic reservoir model based on the Namorado field, Campos basin, Brazil, the proposed technique is assessed against the widely used automatic routines that are known as direct optimization with GA, offline and online surrogate-based optimization approaches. The comparison indicates that the proposed method is more accurate, reliable and efficient than all the other optimization routines presented in this work. The results of this study highlighted the effectiveness of the proposed hybrid approach for solving the real well placement problem with high accuracy in reasonable CPU-time. These auspicious features make it a reliable tool to be used on other real optimization projects.
A Data-Driven Technique for Building the Initial Velocity Model for Refraction Tomography
Authors: Re Simone 
Published: November 2018

Abstract
Near-surface characterization is an important part of seismic data processing, especially with land seismic data. The conventional approaches rely on refracted waves and estimate the compressional velocity models from the tomography of the first-break traveltimes (Glushchenko et al., 2012, Speziali et al., 2014). Despite its strong ability to image the subsurface, seismic tomography is a non-unique inverse problem (Kanlı, 2009, Mantovani et al. 2013). Because most inverse geophysical problems are non-unique, each problem must be studied to determine what type of non-uniqueness applies and, thus, determine what type of a-priori information is necessary to find a realistic solution (Ivanov et al., 2005).
There are several ways to incorporate the available a-priori information in the inverse problem; one of them is the definition of the initial model, which is the starting point of the inversion process. In this work, we present a data-driven approach that derives the initial velocity model for a refraction tomography workflow in an automated fashion, thus trying to minimize the amount of subjectivity that influences the starting model definition (Osypov, 2001). We demonstrate the technique by mean of a synthetic, but realistic, 3D example.
Large Scale Subsurface and Surface Integrated Asset Modeling - An Effective Outcome Driven Approach
Authors: Hafez Hafez , Al Mansoori Yousof , Bahamaish Jamal , Saputelli Luigi , Escorcia Alvaro , Sousa Sergio , Rodriguez Jose , Mijares Gerardo 
Published: November 2018

Abstract
Identifying opportunities in the installed capacity and proactively mitigating the limiting factors are paramount objectives for pursuing profitable production assurance. Although integrated asset modeling has been the de facto technology for supporting production planning and optimization work processes, its application is not fully adopted as it presents challenges when attempted to be used in a large-scale of multiple oil and gas assets.
This paper describes ADNOC’s innovative approach to develop a large scale subsurface to surface integrated asset modeling (LSSSIAM) solution by focusing on the desired business outcome. The paper introduces a new concept of right complexity modeling (RCM) to drive the type and level of complexity of the model/simulation based on the desired business outcome and other factors that influence the quality of the decision-making process.
The methodology has been applied on a large-scale of multiple assets for effective production assurance that integrates the subsurface to the surface physical phenomena as required by the desired business outcome—the technical assurance of production plans within the context of a country. For the presented example, the proposed methodology resulted in the design of a solution where the subsurface phenomena are represented with a data-driven model to specifically address the requirements of the decision-making process which the solution supports.
This resulted in the development of a first-of-its-kind countrywide production model that rigorously considers the properties and physics from the wells to the point of supply while also considering the subsurface phenomena as related to the production potential of the reservoirs and wells.
The solution leverages the rigor of first-principle reservoir models to obtain a data-driven proxy model suitable for integration with a first-principle model covering more than 7,000 wells, multiple network and asset facilities, and a supply point transfer countrywide network. The solution can run in a matter of seconds, allowing for the optimization of a desired objective function or the effective analysis of operational scenarios, which can include short- and mid-term production assurance, opportunities identification to increase production to capture value opportunities from a country-wide production capacity context, and compensating for possible shortfalls resulting from unplanned operational disturbances in other assets.
Waste Heat to Power Through Organic Rankine Cycle ORC Technology: Das Island Case Study
Authors: Ferrari Tommaso , Rossetti Nicola , Al-Ameiry Ahmed Abdulkareim , Anzelini Francesco , Salmeen Haitham 
Published: November 2018

Abstract
Same as other energy-intensive industries, ADNOC LNG is looking at the development of energy-efficiency solutions aimed to improve production process sustainability while maintaining effectiveness and competitiveness. The conversion of waste heat into useful power represents a viable and profitable solution to hit these targets. The possibility to implement an ORC-based heat recovery system as a replacement of an open-cycle gas turbine in Das Island facility has been investigated.
The study evolved in two main phases. The first one was aimed to identify the most suitable waste heat sources present on site. This included the quantification of the waste heat sources needed to achieve the forecasted power demand while complying with layout constrains. The second one was directed to the technical and economic analysis of different heat recovery configurations, in order to understand the best ORC solution. The outcome of the second phase constitutes the reference business case that will be used to compare the ORC with the alternative power production technology, i.e. an open-cycle gas turbine.
With a view to the commissioning of new projects in Das Island and the resulting power consumption increase, ADNOC LNG was originally planning to cover this extra power demand by installing a new open-cycle gas turbine. Given the presence on site of other several open-cycle gas turbines, a waste-heat-to-power solution appeared to be an alternative and feasible solution. The ORC technology, thanks to the use of an organic working fluid, is able to recover the exhaust gas sensible heat and produce power, resulting in a simple power plant with extremely low operation and maintenance costs, high availability, simplicity of operation and no water consumption if an air-cooled condenser solution is selected. Depending on the combination of gas turbines considered for heat recovery in Das Island, the ORC power output can reach up to 30+ MW el., covering a not-negligible portion of Das Island electrical consumption and allowing considerable fuel savings, computed in 10 MMSCFD of fuel gas that can be saved and thus re-allocated to produce useful gas products. The study showed that the ORC solution is viable and can produce the required power at the allocated plot area, which was reserved for the open-cycle gas turbine.
Das Island case study represents one of the first systematic analysis aimed at the integration of an ORC-based heat recovery system within an existing LNG facility. The results of the investigation appear very promising: with a computed LCOE &lt; 30 USD/MWh and breakeven of 3 years, the ORC embodies a possible alternative to traditional power-production technologies. This type of project would be the first in the Region, making ADNOC LNG a pioneer of the technology.
Unlocking Undeveloped Carbonates, A T1 NMR Spectrum Case Study
Authors: Serry Amr , Al-Hassani Sultan , Budebes Sultan , AbouJmeih Hassan , Kaouche Salim , Aki Ahmet , Vican Kresimir , Essam Ramy , Lee Jonathan 
Published: November 2018

Abstract
This case study demonstrates the role of nuclear magnetic resonance (NMR) T1 spectra, as used to drill complex undeveloped carbonate formations offshore Abu Dhabi. The scope of this project exceeds the traditional porosity-permeability approach to exploit the wealth of information about the rock texture, pore size distribution, flow units and a new methodology of NMR T1 data processing.
Evaluation of pore size distributions based on T1 vs. T2 spectra is addressed in two case study wells; one using a 6 ¾-in., and the other a 4 ¾-in. mandrel size for the first time in UAE. Finally, other log-derived permeabilities are presented, together with high-resolution microresistivity image interpretation and production log results in an integrated approach for improved understanding of the petrophysical character of these undeveloped units.
NMR T1 measurements are utilized for the first time in the lateral sections as part of a bottomhole assembly (BHA) consisting of a rotary steerable system (RSS), and logging-while-drilling (LWD) sensors, including high-resolution microresistivity imaging, laterolog and azimuthal electromagnetic wave resistivities, thermal neutron porosity, azimuthal density, azimuthal multipole acoustic, ultrasonic caliper and near-bit azimuthal gamma ray. During NMR T1 measurements, the spin relaxation time carries information about the liquid-solid surface relaxation and bulk-fluid relaxation, hence, it is neither affected by rock internal magnetic field gradients nor by differences in fluid diffusivity. Also, T1 logging measurements are influenced by instrument artefacts to a much lesser extent than T2 results, having several advantages over T2, especially regarding polarization and tool motion while drilling.
The real-time availability of NMR sourceless porosity measurements significantly improved drilling decisions to place the two case history wells into favourable zones and NMR T1 permeabilities were derived together with acoustic and high-resolution microresistivity image-based permeabilities which were then correlated to production logs.
The results indicate that T1 measurements are an effective, practical solution for rock quality evaluation using LWD real-time datasets free from BHA motion and fluid diffusion effects. Comparisons of T1 and T2 logs show that T1 yields equivalent formation evaluation answers, despite its sparser nature.
The T1 spectra facilitated improved pore size distribution, permeability estimation and marking of the hydraulic flow units in the heterogeneous, undeveloped Upper Jurassic reservoir units, paving the way for the consideration of T1 logging as a viable, and in some cases superior alternative to T2 logging. This paper presents the multidisciplinary approach used to benchmark and optimize the future field development program.
Overview of Reservoir Deposits in the Pike and Jackfish Oil Sands Project Areas, Southern Athabasca Oil Sands, Canada
Authors: Baniak Greg M. , Caddel E. Matt , Kingsmith Kelly G. 
Published: November 2018

Abstract
The Lower Cretaceous McMurray Formation in western Canada has over 1.8 trillion barrels of bitumen resource in place. Due to the bitumen in its natural state having a very low API (6-12°) and corresponding high viscosity, traditional primary (pump jacks) and secondary (water flood) recovery techniques cannot be used. Instead, economic extraction of the bitumen occurs via surface mining and subsurface steam-assisted gravity drainage (SAGD). Using the Pike and Jackfish oil sands project areas as a case study, it will be shown that successful SAGD operations requires a thorough understanding of the depositional fabric and stratigraphic architecture of the reservoir.
Within the study area, reservoir intervals in the form of cross-bedded sandstones and sandy inclined heterolithic strata (IHS) are present within both the middle and upper McMurray. Overlying the middle McMurray are upper McMurray parasequence cycles reflective of brackish bays and deltaic embayment deposits. In many areas, however, these parasequences are absent and instead substituted by fluvial channels with brackish water overprint. The facies within these fluvial channels are very similar in character to the those seen within the middle McMurray. To help progress our understanding of baffles and barriers to flow within these aforementioned reservoir facies, dip meter and seismic data are presented as data that can be used. From this, a better understanding of the complex interplay of facies and stratigraphic relationships can be made. More importantly, clearer insights into SAGD performance (pre- and post-steam) can also be achieved.
Using the McMurray Formation as an underpinning, the wider implications of understanding fluvial sedimentation will be addressed by using reservoirs from the Middle East as examples. For example, many siliciclastic reservoirs in locations such as Kuwait (Wara Formation) and Iraq (Zubair Formation) are also influenced to a large degree by fluvial sedimentation. Not unlike SAGD, any successful secondary recovery techniques applied within these reservoirs will also require a detailed characterization of the channel stacking patterns and channel orientations prior to implementation.
Assessment of Innovation Capabilities of Oil and Gas Industry in Sudan
Authors: Ali Fadl Ali 
Published: November 2018

Abstract
This study aims to propose, map, and assess innovation capabilities of oil and gas industry in Sudan. The author proposed a theoretical framework (the SSPP framework) composed of four main dimensions; Strategy, Structure, People, and Processes to assess innovation capabilities of oil and gas industry in Sudan. The quantitative methodology was adopted and the primary data had been obtained from 435 usable questionnaires (66.9 % of the total questionnaires) distributed to individuals working in 14 firms representing the three parts of oil and gas industry (regulatory bodies, upstream and downstream companies). Exploratory factor analysis was applied to check goodness of the measures. The multiple regression analysis has been used to test the hypotheses. The SSPP framework explained 42.1 % of the variance in innovation capabilities of oil and gas industry in Sudan; (R square = 0.421). The findings of the study revealed that value-chain governance, organizational knowledge, rewards & recognition, and work procedures have positive influence over innovation capabilities of oil and gas industry in Sudan. Whereas, the strategy formulation and organizational culture haven't such influence. Based on the results of the study, it is recommended that top management of oil and gas sector in Sudan has to make due consideration to strategy formulation and organizational culture aiming to enhance innovation capabilities. Another important recommendation is to establish a national oil and gas innovation system to integrate innovative capabilities of its constituents.
Process Safety in Design Verification in Operational Phase for Onshore Gas Production Plant
Authors: Mohammed Abdelsalam Omar 
Published: November 2018

Abstract
It is a mandatory company requirement for all offshore and onshore assets to secure the documentation and demonstration of process safety in design (PSID). The common way to conduct the PSID is to apply it within the "Design phase" and after the "Execute phase" to remove or minimize hazards and identify areas of process design that warrant specific focus. In this paper, we will show a case of implementing the PSID verification on an onshore gas production plant during operation phase which is different from the main approach and way of verification during earlier stages of project execution.
Verification is intended to confirm that the detailed documentation and calculations supporting the requirements of the Process Engineering Process Safety Design Standard have been correctly performed.
In broad terms the objective of the verification in the "Operation phase" was;To confirm that the extent of documentation available meets the minimum requirements of the company standards.To confirm that adequate layers of protection are being provided throughout the process to reduce risks to ALARP (As Low As Reasonably Practicable).To confirm that all Blowdown systems and relief valve calculations have been correctly conducted and appropriate relief cases and basis have been used.To confirm that HAZOP/HAZID has been conducted in a reasonable manner and the actions have been closed out.To confirm that layers of protection analysis "LOPA" for all site safety instrumented functions (SIFs) as part of Safety Integrated Levels (SIL) assessment have been assessed and verified.To confirm that specification breaks have been properly located and HP/LP interfaces correctly designed.To confirm that the philosophies are complete, adequate and implemented.To confirm that process safety is being assured for the project and to identify and record any failings or flaws in approach, detailed calculations or design that require rectification.
The design contractor produced a Process Safety Dossier which contained sections as following:Design PhilosophiesRelief and Blowdown DesignHP/LP InterfacesLOPAHAZOP/HAZID verification
The outcome of the verification on the existing gas production plant was a list of findings categorized – from 1 to 4 – according to their priority from safety point of view. We ensured closure of the actions starting from code 1 and code 2 in a tight time frame to avoid any risk to be released.
Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data
Authors: Giuliani Marco , Cadei Luca , Montini Marco , Bianco Amalia , Niccolai Alessandro , Mussetta Marco , Grimaccia Francesco 
Published: November 2018

Abstract
Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence (AI) technique is presented, which is able to manage big dataset to automatically match the entire production model against measured field data. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data.
The workflow starts with the modeling of the production system through physics-based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated using an artificial neural network. Finally, the AI tool fed by real-time data is used to match the field behavior: uncertain parameters are modified through a differential evolution algorithm that minimizes the error between calculated and measured variables. The matching parameters are, then, passed to the simulators achieving a field representative model.
The tool has been developed considering an operating field in offshore western Africa. The typical uncertain parameters in this kind of field are related to the fluid characteristics, in particular densities and compositions, but also to the physical characterization of the pipelines such as roughness and heat transfer characteristics. The matching process has been performed coupling the proxy model, which is a neural network able to replicate the field behavior, and a differential evolution algorithm as the optimization algorithm. The fitness function to be minimized is a Mean Absolute Percentage Error (MAPE) that represents the distance between the actual field production parameters and the modelled ones. The best configuration of both the neural network and the differential evolution algorithm required a computational time of 6 seconds with a MAPE equal to 2.6%. These results are compared to the one obtained coupling the same differential evolution algorithm with the commercial simulator to perform the matching. The required computational time is equal to about 20 hours (70400s) and a MAPE equal to 2.2%. The big gain with the novel approach is clearly the knocking down of computational time with a comparable error.
In this paper, it has been shown how substituting the physical model with a proxy one can give substantial advantages in terms of computational time. In principle, with the velocity of the tool implemented, the matching procedure could be done on a daily basis. This is a breakthrough because it allows having the simulator model always tuned and ready to be utilized.
Electrical Submersible Pumps Condition Monitoring Using Motor Current Signature Analysis
Authors: Popaleny Peter , Duyar Ahmet , Ozel Cumali , Erdogan Yusuf 
Published: November 2018

Abstract
The paper discusses how the Electrical Submersible Pumps (ESP) mechanical and electrical malfunctions are reflected in the dynamic current spectrum using Motor Current Signature Analysis (MCSA). The paper further shows the real case studies, analyses and findings on ESP. Since MCSA does not require any sensor installation on the ESP itself and the measurement is carried out remotely in the ESP motor control unit, it is especially attractive for inaccessible driven equipment and well suited for the monitoring of Electrical Submersible Pumps.