Collaborative Working Environment CWE Strategy: An Enterprise Approach to Operational Excellence

2021 ◽  
Author(s):  
ElFadl Z. Ibrahim ◽  
Mariam A. Al Hendi ◽  
Abdulla Al-Qamzi ◽  
Nasser A. Ballaith ◽  
Maha A. Al Naqbi ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Petroleum Industry ◽  
Oil Field ◽  
Working Environment ◽  
Strategic Objective ◽  
Collaborative Working ◽  
Day To Day Operations ◽  
Set Up ◽  
Operational Issues

Abstract Collaborative Working Environments (CWE) are a business solution that improve the quality and speed of decision making by enriching the collaboration between teams and individuals, which results in tangible business benefits. The advantages of working in a collaborative environment are well understood in the organization and the concept is widely embraced throughout the petroleum industry. CWEs provide seamless communication between disciplines and between teams in different locations. Traditionally, they have been used to connect staff in remote locations to teams in the headquarters, allowing real time monitoring of the health of the field, and fast decision making on operational issues and short to medium term optimization opportunities. The main goal is to be quickly alerted to events and make smarter, faster decisions using key capabilities available to the company with access to all relevant knowledge, data and analytical tools required to reach a decision. But this drive to make smarter, faster decisions is applicable to all levels of a company. In fact, it becomes increasingly important as more complex decisions are required at higher levels, which can be influenced by interpreted data, personal opinions and perceptions. In line with strategic objective of digital transformation, a national oil company (NOC) has extensive plans to develop asset specific CWEs and enterprise level CWEs. These will be centralized collaboration facilities to provide more rigorous, effective, and consistent surveillance & optimization to help reduce deferment costs and inefficiencies and accelerate decision-making with a measurable business value to enhance HSE, Reservoir, Drilling, Well and Production system performance through emerging digital innovation. All these centers shall be equipped to receive real time and episodic data and perform exception-based surveillance through trending, analysis, and condition diagnosis. All these CWE Centers shall enable decision making with efficient multi-disciplinary collaboration to address business challenges and increase the efficiency of day-to-day operations. They will have clear roles and responsibilities serving as an integral element of the value realization across the assets. The paper will describe the enterprise CWE strategy, key technical considerations, methodology and standards that have been set up to achieve the ultimate objective of the organization to maximize oil field recovery, eliminating non-productive time, enhancing HSE aspects and increasing profitability through the deployment of these various centers.

A Success Story in Managing and Optimising Gas Lift Wells in Matured Oil Field: Automated Workflows in Digital Fields as Enablers to Accelerate Opportunities Creation and Production Optimisation

2021 ◽  
Author(s):  
Amir Badzly M. Nazri ◽  
W. M. Anas W. Khairul Anuar ◽  
Lucas Ignatius Avianto Nasution ◽  
Hayati Turiman ◽  
Shar Kawi Hazim Shafie ◽  
...  
Keyword(s):  
Model Validation ◽  
Field Performance ◽  
Oil Field ◽  
Working Environment ◽  
Key Factors ◽  
Collaborative Working ◽  
Water Cut ◽  
Integrated Operations ◽  
Collaborative Working Environment ◽  
Gas Lift

Abstract Field S located in offshore Malaysia had been producing for more than 30 years with nearly 90% of current active strings dependent on gas lift assistance. Subsurface challenges encountered in this matured field such as management of increasing water-cut, sand production, and depleting reservoir pressure are one of key factors that drive the asset team to continuously monitor the performance of gaslifted wells to ensure better control of production thereby meeting target deliverability of the field. Hence, Gas Lift Optimization (GLOP) campaign was embarked in Field S to accelerate short term production with integration of Gas Lift Management Modules in Integrated Operations (IO). A workflow was created to navigate asset team in this campaign from performing gaslift health check, diagnostic and troubleshooting to data and model validation until execution prior to identification of GLOP candidates with facilitation from digital workflows. Digital Fields and Integrated Operations (IO) developed in Field S provided an efficient collaborative working environment to monitor field performance real time and optimize production continuously. Digital Fields comprises of multiple engineering workflows developed and operationalized to act as enablers for the asset team to quickly identify the low-hanging fruit opportunities. This paper will focus on entire cycle process of digital workflows with engineer's intervention in data hygiene and model validation, the challenges to implement GLOP, and results from the campaign in Field S.

scholarly journals Digital Twin for Monitoring Ergonomics during Manufacturing Production

2020 ◽  
Vol 10 (21) ◽  
pp. 7758
Author(s):  
Alessandro Greco ◽  
Mario Caterino ◽  
Marcello Fera ◽  
Salvatore Gerbino
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Methodological Framework ◽  
Production Lines ◽  
Work Activities ◽  
Digital Twin ◽  
Set Up ◽  
Operational Issues ◽  
Smart Factories

Within the era of smart factories, concerning the ergonomics related to production processes, the Digital Twin (DT) is the key to set up novel models for monitoring the performance of manual work activities, which are able to provide results in near real time and to support the decision-making process for improving the working conditions. This paper aims to propose a methodological framework that, by implementing a human DT, and supports the monitoring and the decision making regarding the ergonomics performances of manual production lines. A case study, carried out in a laboratory, is presented for demonstrating the applicability and the effectiveness of the proposed framework. The results show how it is possible to identify the operational issues of a manual workstation and how it is possible to propose and test improving solutions.

Definition of an Offshore Petroleum Production System by Using Fuzzy Sets and Utility Functions

2005 ◽  
Vol 9 (6) ◽  
pp. 684-692 ◽  
Author(s):  
Tiago C. da Fonseca ◽  
◽  
José R. P. Mendes ◽  
Celso K. Morooka ◽  
Ivan R. Guilherme ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Production System ◽  
Petroleum Industry ◽  
Utility Functions ◽  
Oil Field ◽  
Petroleum Engineering ◽  
Decision Making Process ◽  
Field Development ◽  
Definition Of

Field development is a very important task in the petroleum industry. Decisions in this area may lead either to profitable success or to expensive failures, and usually involve several distinct areas in the scope of Petroleum Engineering and Science, such as Geology, Petreoleum Engineering, Offshore Engineering and Economics. Therefore, these subjects must be well understood by teams supporting the decision-making process. This work proposes a methodology to support managers in one stage of field development: the definition of the field production system. In order to determinate the production system to be installed in an oil field, attributes such as investment, profitability, safety, environmental preservation and technological experience must be considered. A decision-making team or agent must weight these attributes in order to achieve solutions accordingly to the company strategies and objectives. Combining a few mathematical tools to represent the process, the methodology proposed herein is an approach that considers not only the financial variables involved in a field decision process, but might include other aspects, or attributes, also important to guide a decision. To this end, the application of Multi-Attribute Analysis concepts is suggested. Also, to support the decision-making agent, the approach follows Utility Functions concepts in order to numerically represent the agent trend or inclination to each option. Considering that subjectivity and imprecision are naturally involved in the decision-making process, the approach incorporates Fuzzy Sets Theory concepts as a means of formalizing the computation of this uncertainty.

Application of Advanced Data Analytics for Gas Reservoirs and Wells Management

2021 ◽  
Author(s):  
Ashwin Srinivasan ◽  
Gaurav Modi ◽  
Rahul Agrawal ◽  
Viren Kumar
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Production Management ◽  
Petroleum Industry ◽  
Oil Field ◽  
Petroleum Engineering ◽  
Multiple Sources ◽  
Opportunity Identification ◽  
Diagnostic Plots ◽  
Time Required

Abstract Objectives/Scope The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value Methods, procedure, process The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. Results, Observations, Conclusions The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). Novel/Additive Information The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.

scholarly journals DIGITAL OIL FIELD FROM DATA TO REAL - TIME DECISION MAKING -SMART WORKING FOR OPERATIONS EFFICIENCY

2021 ◽  
Vol 2(73) (1) ◽  
pp. 5-15
Author(s):  
Mohammad Rostami Dehka ◽  
Iulian Nistor
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Value Creation ◽  
Oil Field ◽  
Data Availability ◽  
Large Area ◽  
Operations And Maintenance ◽  
Reliable Performance ◽  
Smart Working ◽  
The Right

Digital Oil Field (DOF) project in OMV Petrom aims to remotely monitor, troubleshoot and optimize operations and maintenance data and activities in a modern manner, and to foster value creation through increased integrated and reliable performance data availability to skilled professionals in order to facilitate the right decisions. DOF Project started as a pilot in an oil field, covering a large area of automated wells and facilities. The pilot field is operating around 400 wells equipped with PCP (progressive cavity pump) and more than 70% automated facilities consisting of: 14 MPSs (Meter Point Skids), 2 PMANs (Production Manifolds) and 1 OMS (Oil Metering Station), all connected to SCADA systems.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

DEVELOPMENT OF MULTIPLEX REAL-TIME PCR ASSAY FOR SIX PATHOGENS: RAPID TOOL FOR DIAGNOSIS OF BACTERIAL MENINGITIS

2019 ◽  
pp. 60-66
Author(s):  
Viet Quynh Tram Ngo ◽  
Thi Ti Na Nguyen ◽  
Hoang Bach Nguyen ◽  
Thi Tuyet Ngoc Tran ◽  
Thi Nam Lien Nguyen ◽  
...  
Keyword(s):  
Bacterial Meningitis ◽  
Real Time ◽  
Real Time Pcr ◽  
Diagnostic Methods ◽  
Type B ◽  
E Coli ◽  
Pcr Assays ◽  
Set Up ◽  
Etiological Agents ◽  
Suis Serotype

Introduction: Bacterial meningitis is an acute central nervous infection with high mortality or permanent neurological sequelae if remained undiagnosed. However, traditional diagnostic methods for bacterial meningitis pose challenge in prompt and precise identification of causative agents. Aims: The present study will therefore aim to set up in-house PCR assays for diagnosis of six pathogens causing the disease including H. influenzae type b, S. pneumoniae, N. meningitidis, S. suis serotype 2, E. coli and S. aureus. Methods: inhouse PCR assays for detecting six above-mentioned bacteria were optimized after specific pairs of primers and probes collected from the reliable literature resources and then were performed for cerebrospinal fluid (CSF) samples from patients with suspected meningitis in Hue Hospitals. Results: The set of four PCR assays was developed including a multiplex real-time PCR for S. suis serotype 2, H. influenzae type b and N. meningitides; three monoplex real-time PCRs for E. coli, S. aureus and S. pneumoniae. Application of the in-house PCRs for 116 CSF samples, the results indicated that 48 (39.7%) cases were positive with S. suis serotype 2; one case was positive with H. influenzae type b; 4 cases were positive with E. coli; pneumococcal meningitis were 19 (16.4%) cases, meningitis with S. aureus and N. meningitidis were not observed in any CSF samples in this study. Conclusion: our in-house real-time PCR assays are rapid, sensitive and specific tools for routine diagnosis to detect six mentioned above meningitis etiological agents. Key words: Bacterial meningitis, etiological agents, multiplex real-time PCR

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