scholarly journals Stochastic Simplex Approximate Gradient for Robust Life-Cycle Production Optimization: Applied to Brugge Field

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Bailian Chen ◽  
Jianchun Xu
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Operating Conditions ◽  
Optimization Under Uncertainty ◽  
Theoretical Argument ◽  
Gas Industry

In oil and gas industry, production optimization is a viable technique to maximize the recovery or the net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates settings of inflow control valves and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for the robust life-cycle production optimization. Recently, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Fonseca and co-workers (2016, A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization Under Uncertainty, Int. J. Numer. Methods Eng., 109(13), pp. 1756–1776) provided a theoretical argument on the superiority of StoSAG over EnOpt. However, it has not drawn significant attention in the reservoir optimization community. The purpose of this study is to provide a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAG generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.

Automated Creation of the Pipeline Digital Twin During Construction: Improvement to Construction Quality and Pipeline Integrity

2018 ◽  
Author(s):  
Joseph Hlady ◽  
Matt Glanzer ◽  
Lance Fugate
Keyword(s):  
Life Cycle ◽  
Net Present Value ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Life Cycle Management ◽  
Field Trials ◽  
Gas Industry ◽  
Digital Twin ◽  
Sensor Platforms ◽  
Digital Formats

The concept of the digital twin dates all the way back to the 1950’s when NASA, GE and other industrial manufacturers started creating abstract digital models of equipment to model their performance in simulations and maintain a record of the asset throughout its life span [1]. Over the years more and more industries have adopted the digital twin paradigm to improve traceability, maintenance, and analytics allowing for improved sustainment of the asset or equipment while reducing various risks identified during life cycle management. It has been found that collectively, the digital twin concept improves the overall net present value of an asset. The oil and gas industry has slowly been adopting the digital twin paradigm of asset life cycle management over the past two decades with the focus on facilities. Recently, field trials were completed to test and evaluate workflows and sensor platforms for the creation of a digital twin for pipelines. The trials resulted in highly accurate pipeline centerlines, weld locations, Depth to Cover (DoC) and ditch geometry capture in digital formats. This paper describes the methodologies used, and the results of an actual construction field trial with a comparison to traditional data collection methods for these attributes. The value of creating a pipeline digital twin during pipeline construction in near-real-time is discussed with an emphasis on the potential benefits to life cycle management and pipeline integrity.

A Novel Methodology for Optimal Design of Compressor Plants Using Probabilistic Plant Design

2013 ◽  
Vol 135 (11) ◽  
Author(s):  
Rainer Kurz ◽  
J. Michael Thorp ◽  
Erik G. Zentmyer ◽  
Klaus Brun
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Process Conditions ◽  
Plant Design ◽  
Well Performance ◽  
Worst Case ◽  
Gas Industry ◽  
Machine Performance ◽  
Equipment Sizing

Equipment sizing decisions in the oil and gas industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions, and others. Since the ultimate goal is to meet production commitments, the traditional method of addressing this is to use worst case conditions and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances, by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, however, they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will, therefore, usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs. A standardized framework using a Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance. Case studies are presented that highlight the methodology as applied to critical turbomachinery.

Wheel Box Test Aeromechanical Verification of New First Stage Bucket With Integrated Cover Plates for MS5002 GT

2019 ◽  
Author(s):  
Marco Mariottini ◽  
Nicola Pieroni ◽  
Pietro Bertini ◽  
Beniamino Pacifici ◽  
Alessandro Giorgetti
Keyword(s):  
Gas Turbine ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Response Assessment ◽  
Operating Conditions ◽  
Analytical Models ◽  
Gas Industry ◽  
Improved Performance ◽  
Cover Plates

Abstract In the oil and gas industry, manufacturers are continuously engaged in providing machines with improved performance, reliability and availability. First Stage Bucket is one of the most critical gas turbine components, bearing the brunt of very severe operating conditions in terms of high temperature and stresses; aeromechanic behavior is a key characteristic to be checked, to assure the absence of resonances that can lead to damage. Aim of this paper is to introduce a method for aeromechanical verification applied to the new First Stage Bucket for heavy duty MS5002 gas turbine with integrated cover plates. This target is achieved through a significantly cheaper and streamlined test (a rotating test bench facility, formally Wheel Box Test) in place of a full engine test. Scope of Wheel Box Test is the aeromechanical characterization for both Baseline and New bucket, in addition to the validation of the analytical models developed. Wheel Box Test is focused on the acquisition and visualization of dynamic data, simulating different forcing frequencies, and the measurement of natural frequencies, compared with the expected results. Moreover, a Finite Elements Model (FEM) tuning for frequency prediction is performed. Finally, the characterization of different types of dampers in terms of impact on frequencies and damping effect is carried out. Therefore, in line with response assessment and damping levels estimation, the most suitable damper is selected. The proposed approach could be extended for other machine models and for mechanical audits.

scholarly journals Theoretical and practical aspects of assessment of innovative activity in oil and gas enterprises

2020 ◽  
Vol 13 (4) ◽  
pp. 531-540
Author(s):  
M. V. Rybin ◽  
D. S. Lobov
Keyword(s):  
Life Cycle ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Innovative Activity ◽  
Gas Industry ◽  
The Innovative Management ◽  
And Performance ◽  
Key Indicators ◽  
Oil And Gas Companies ◽  
Oil And Gas Enterprises

Analysis of theoretical and practical aspects of assessment of innovative activity at national and foreign oil and gas enterprises revealed the necessity of improvement of the existing tools which include the lists of key indicators of efficiency and performance applied within the innovative development programs of the Russian companies. Thereby the authors analyzed national and foreign research paying the most serious attention to theoretical aspects of innovative activity assessment. Among them of greatest interest is the complex approach to application of metrics in scientific and technical development of a company. The authors have also studied the conceptual apparatus and the main terms for the problems under consideration. As a result, the research confirmed the idea that the practice of applying the lists of key indicators of innovative activity in oil and gas industry does not coincide with the results of academic works and the innovative management theory: the current methods are primarily aimed at assessment of the results of innovative activity, several stages of the life cycle of creation of innovation are not subject to monitoring. At the same time lists of key indicators of innovative activity make it possible for the company’s management to estimate economic and resource effects of innovations which corresponds to strategic interests of oil and gas companies.Lists of indicators of efficiency and performance of innovative activity can be improved by means of national and foreign research. It is important to involve more metrics which make it possible to monitor all the life cycle of creation and implementation of innovative solutions.The results of the study can be used as the basis for further research on improvement and development of the lists of key indicators of innovative activity of oil and gas companies.

Electrical Submersible Pump Prognostics and Health Monitoring Using Machine Learning and Natural Language Processing

2021 ◽  
Author(s):  
Rajeev Ranjan Sinha ◽  
Supriya Gupta ◽  
Praprut Songchitruksa ◽  
Saniya Karnik ◽  
Amey Ambade
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Operating Conditions ◽  
Learning Approaches ◽  
Submersible Pump ◽  
Gas Industry ◽  
Electrical Submersible Pump ◽  
Low Efficiency

Abstract Electrical Submersible Pump (ESP) systems efficiently pump high volumes of production fluids from the wellbore to the surface. They are extensively used in the oil and gas industry due to their adaptability, low maintenance, safety and relatively low environmental impact. They require specific operating conditions with respect to the power, fluid level and fluid content. Oilfield operation workflows often require extensive surveillance and monitoring by subject-matter experts (SMEs). Detecting issues like formation of unwanted gas and emulsions in ESPs requires constant analysis of downhole data by SMEs. The lack of adequate and accurate monitoring of the downhole pumps can lead to low efficiency, high lifting costs, and frequent repair and replacements. There are 3 workflows described in the paper which demonstrate that the maintenance costs of the ESPs can be significantly reduced, and production optimized with the augmentation of machine learning approaches typically unused in ESP surveillance and failure analysis.

Risk-based Winterization for Vessels Operations in Arctic Environments

2013 ◽  
Vol 29 (04) ◽  
pp. 199-210 ◽  
Author(s):  
Ming Yang ◽  
Faisal I. Khan ◽  
Leonard Lye ◽  
Heri Sulistiyono ◽  
John Dolny ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Temperature Data ◽  
Operating Conditions ◽  
Hydrocarbon Exploration ◽  
The Arctic ◽  
Gas Industry ◽  
Arctic Regions ◽  
Exploration And Production

Because the oil and gas industry has an increasing interest in the hydrocarbon exploration and development in the Arctic regions, it becomes important to design exploration and production facilities that suit the cold and harsh operating conditions. In addition to well-established minimum class requirements for hull strengthening, winterization should be considered as a priority measure early in the design spiral for vessels operating in the Arctic environments. The development of winterization strategies is a challenging task, which requires a robust decision support approach. This article proposes a risk-based approach for the selection of winterization technologies and determination of winterization levels or requirements on a case-by-case basis. Temperature data are collected from climatology stations located in the Arctic regions. Loading scenarios are defined by statistical analysis of the temperature data to obtain probabilistic distributions for the loadings. Risk values are calculated under different loading scenarios. Based on the risk values, appropriate winterization strategies can be determined. A case study is used to demonstrate how the proposed approach can be applied to the identification of heating requirements for gangways.

Methodology to Obtain the Life Cycle Mooring Loads on a Semisubmersible Wind Platform

2014 ◽  
Author(s):  
Raúl Guanche ◽  
Lucía Meneses ◽  
Javier Sarmiento ◽  
César Vidal ◽  
Íñigo Losada
Keyword(s):  
Life Cycle ◽  
Numerical Model ◽  
Data Base ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Offshore Wind ◽  
Mooring System ◽  
Gas Industry ◽  
Wave Basin ◽  
The Moment

Nowadays there are few methodologies related with the design of mooring systems for floating offshore wind platforms. The ones used until the moment are inherited from the oil and gas industry. Because of that, mooring loads may be incorrectly estimated. This study presents a validated methodology in order to estimate the loads of the moorings of offshore floating platforms along the life cycle of the structure. The methodology is based on an extensive laboratory test data base carried out in a wave basin of the University of Cantabria. The proposed methodology has been applied to a floating semisubmersible platform (similar to the one in Agucadoira by Principle Power). The methodology is composed by a few steps. The first step consist on the selection of the most representative sea states of a long term met-ocean data base through a selection technique named MDA (Maximum dissimilitude algorithm). Afterwards, mooring system loads and platform motion are numerically simulated. SESAM (DNV) numerical model has been used in this particular application. SESAM numerical model was previously calibrated based on the laboratory tests. Finally, based on a multidimensional interpolation technique named Radial Basis Function life cycle mooring system loads were reconstructed. A sensitivity analysis of the methodology were carried out. Based on it, it can be concluded that selecting 1000 sea states with the MaxDiss technique, life cycle mooring loads can be accurately predicted.

scholarly journals On Disaster Risk Reduction in Norwegian Oil and Gas Industry Through Life-Cycle Perspective

2019 ◽  
Author(s):  
Michaela Ibrion ◽  
Nicola Paltrinieri ◽  
Amir R. Nejad
Keyword(s):  
Life Cycle ◽  
Risk Reduction ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Learning And Development ◽  
Gas Industry ◽  
Life Cycle Perspective ◽  
Offshore Field ◽  
Perspective Analysis ◽  
Reduction Of Uncertainty

Abstract This paper presents the risk reduction in Norwegian oil & gas industry over the time (1975–2016) through a life cycle perspective analysis with the aim to identify the critical stage(s) both in terms of accident occurrence and cause of the accident. Fifteen accidents, major accidents and disasters for example Ecofisk 2/4 Alpha 1975, Alexander L. Kielland 1980, Songa Endurance 2016 were studied. Cases from outside of the Norwegian offshore field — the Piper Alpha 1988, the Bourbon Dolphin 2007, and the Deep Water Horizon 2010 — were also considered as comparison. For each accident and through the life cycle analysis, the occurrence stage of the accident and its main technical causes were identified and compared. It was found that a high risk is concentrated in the Operation (In-Service) stage and associated Marine Operations. Furthermore, it was observed that a high number of accidents in oil and gas industry are associated with mobile structures. All the investigated accidents have acted as powerful reminders to the oil and gas industry that a continuous improvement of risk management and reduction of uncertainty are of paramount importance in order to ensure safe operations and risk reduction for accidents, major accidents and disasters. However, a reactive learning from major accidents and disasters needs to be supported by a proactive learning and development of a dynamic risk culture in the oil and gas industry.

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