Digital-Twin for Production Monitoring and Optimisation: Two Case Study Application Examples

2021 ◽  
Author(s):  
Paulo J Gomes ◽  
Fei Cao ◽  
Luke Hanzon ◽  
Chinenye Excel Ogugbue ◽  
Kelda Bratley ◽  
...  
Keyword(s):  
Production System ◽  
Water Flow Rate ◽  
Production Optimization ◽  
Operating Conditions ◽  
Sensor Data ◽  
Petroleum Engineering ◽  
Production Forecast ◽  
Simulation And Optimization ◽  
Digital Twin ◽  
End To End

Abstract Well network simulation and optimization is an established technology within BP for production optimization. However, for simplicity, the processing facilities are usually only considered as fixed oil, gas and water flow rate constraints. Actual production limits vary as a function of operating conditions and/or cannot be measured directly (e.g. True Vapour Pressure (TVP) or gas velocity at the inlet separator nozzles). To improve on existing workflows, BP has expanded its existing petroleum engineering-focused toolkit and is now globally deploying an end-to-end production system digital twin that extends from the well choke to the facility export for system surveillance and optimization. The end-to-end production system digital twin is a cloud-based system that links sensor data from the asset historian with an equipment data model and third-party first principle steady state simulation tools for an accurate representation of the well network and processing facilities. It supports multi-discipline collaboration, particularly between Petroleum Engineers and Process Engineers, and is remotely accessible by a globally dispersed team. This integrated digital twin can be used in two modes: monitoring and what-if. In monitoring mode, the models are automatically updated hourly with real time data and key simulation results extracted and stored. These monitoring simulations generate virtual sensor output, providing insights that cannot be measured by real sensors. In what-if mode, engineers test scenarios risk-free to explore optimization opportunities. As well as routine optimizations to align with production forecast updates, this can also include scenarios during planned abnormal operations (e.g. facility equipment offline for maintenance or well flowback). An early pilot in a key production region delivered significant production upside and was foundational for the subsequent global roll-out program. This paper will illustrate two practical applications from early deployment activities: (1) condensate recovery optimization (2) well routing optimization / feasibility against variable processing facility limits.

Evaluation on Explicit Coupling Between Reservoir Simulators and Production System

2012 ◽  
Author(s):  
João Carlos von Hohendorff Filho ◽  
Denis José Schiozer
Keyword(s):  
Production System ◽  
Production Systems ◽  
Operating Conditions ◽  
Production Forecast ◽  
Group Management ◽  
Reservoir Simulator ◽  
Implicit And Explicit ◽  
Operating Constraints ◽  
Management Alternatives

Various methodologies to model the coupling of reservoirs and production systems have been applied in the oil industry in recent years due to the need to model properly the integrated solution of models that represent the flow of fluids through the reservoir to the surface. These methodologies are used to solve the production forecast of multiple reservoirs, sharing production platforms with limited production e injection capacities ruled by complex production systems. They can be grouped into two basic types: implicit and explicit coupling methodologies. Explicit methodology can be an efficient choice to integrate simulations because allows coupling adequate simulators to model the whole system and also to grant flexibility in study of well management alternatives. However, it is necessary to test this type of procedure to check the quality of the results. Therefore, a validation study of explicit coupling methodology is presented in this work where the production system is tested on common operating conditions during production and injection of fluids, verifying benefits and limitations of explicit methodology. Some methods for improving the explicit response are proposed and evaluated. An example of application verifies the gain of flexibility in well prioritization by the group management obtained by use of an external methodology for reservoir simulator. The explicit coupling, as implemented, has shown a satisfactory result for the integration between the simulators, honoring all operating constraints set in evaluation cases. Some correction methodologies are necessaries to obtain stabilized results.

scholarly journals Effect of reservoir and production system integration on field production strategy selection

2018 ◽  
Vol 73 ◽  
pp. 44 ◽  
Author(s):  
João C. V. Hohendorff Filho ◽  
Denis J. Schiozer
Keyword(s):  
Oil Recovery ◽  
Production System ◽  
Net Present Value ◽  
Reference Model ◽  
Computation Time ◽  
Petroleum Engineering ◽  
Real Field ◽  
Production Forecast ◽  
Integrated Production ◽  
Production Strategy

In petroleum engineering studies, the integration of reservoir and production system models can improve production forecasts. As the integration increases computation time, it is important to assess when this integration is necessary and how to choose a suitable coupling methodology. This work analyzes the influence of this integration, for a petroleum field in the development phase, evaluating the effects on the production strategy parameters. We tested a benchmark model based on an offshore field in Brazil so we could validate the solution in a reference known model. This work continues the research of Von Hohendorff Filho and Schiozer (2014, 2017) and aims to improve step 11 of the 12-step reservoir development and management methodology by Schiozer et al. (2015). The solution is tested in a reference model. Using the integrated production system and reservoir models from step 11 of the methodology, we re-optimize the production strategy of a standalone production development, while evaluating net present value as the objective function. We adapted an assisted workflow to include the optimization of new variables, such as pipe diameters of the well systems and gathering systems, platform positions, and artificial lift application, and compared these with the production strategy obtained from the same benchmark in a standalone approach. Comparing the integrated standalone and integrated production strategies, we observed important changes that indicate the need to integrate reservoir and production models. The optimized integrated systems resulted in significantly increased net present values, maintaining the same oil recovery factor while requiring lower initial investment. We implemented the best integrated production strategy and the integrated production strategy derived from the standalone case in the reference model which, in this case, represents a real field (emulating a real situation). Integration in the implementation step impacted the production forecast more than the optimization step, demonstrating the benefits of integrating reservoir and production systems to increase project robustness.

scholarly journals Field-synchronized Digital Twin framework for production scheduling with uncertainty

2020 ◽  
Author(s):  
Elisa Negri ◽  
Vibhor Pandhare ◽  
Laura Cattaneo ◽  
Jaskaran Singh ◽  
Marco Macchi ◽  
...  
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Flow Shop ◽  
Health Management ◽  
Discrete Event ◽  
Operating Conditions ◽  
Sensor Data ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Digital Twin

Abstract Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

scholarly journals Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

2021 ◽  
Vol 11 (10) ◽  
pp. 4620
Author(s):  
Niki Kousi ◽  
Christos Gkournelos ◽  
Sotiris Aivaliotis ◽  
Konstantinos Lotsaris ◽  
Angelos Christos Bavelos ◽  
...  
Keyword(s):  
Production System ◽  
Assembly Lines ◽  
Flexible Assembly ◽  
Digital Twin ◽  
Collaborative Assembly ◽  
Geometrical Information ◽  
Line Level ◽  
Dual Arm ◽  
Different Levels

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

scholarly journals Evaluation of natural gas production optimization in Kailashtila gas field in Bangladesh using decline curve analysis method

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain
Keyword(s):  
Production System ◽  
Gas Production ◽  
Curve Analysis ◽  
Production Optimization ◽  
Reservoir Pressure ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Overall Performance

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015

How Digital Transformation Improves Perceptions of Oil and Gas Industry

2020 ◽  
Vol 72 (12) ◽  
pp. 33-33
Author(s):  
Chris Carpenter
Keyword(s):  
Digital Technology ◽  
Public Perception ◽  
Business Models ◽  
Oil And Gas ◽  
Digital Technologies ◽  
Oil And Gas Industry ◽  
Production Optimization ◽  
Petroleum Engineering ◽  
Co2 Injection ◽  
The Impact

The final afternoon of the 2020 ATCE saw a wide-ranging virtual special session that covered an important but often overlooked facet of the unfolding digitalization revolution. While the rising wave of digital technology usually has been associated with production optimization and cost savings, panelists emphasized that it can also positively influence the global perception of the industry and enhance the lives of its employees. Chaired by Weatherford’s Dimitrios Pirovolou and moderated by John Clegg, J.M. Clegg Ltd., the session, “The Impact of Digital Technologies on Upstream Operations To Improve Stakeholder Perception, Business Models, and Work-Life Balance,” highlighted expertise taken from professionals across the industry. Panelists included petroleum engineering professor Linda Battalora and graduate research assistant Kirt McKenna, both from the Colorado School of Mines; former SPE President Darcy Spady of Carbon Connect International; and Dirk McDermott of Altira Group, an industry-centered venture-capital company. Battalora described the complex ways in which digital technology and the goal of sustainability might interact, highlighting recent SPE and other industry initiatives such as the GAIA Sustainability Program and reviewing the United Nations Sustainable Development Goals (SDGs). McKenna, representing the perspective of the Millennial generation, described the importance of “agile development,” in which the industry uses new techniques not only to improve production but also to manage its employees in a way that heightens engagement while reducing greenhouse-gas emissions. Addressing the fact that greater commitment will be required to remove the “tougher two-thirds” of the world’s hydrocarbons that remain unexploited, Spady explained that digital sophistication will allow heightened productivity for professionals without a sacrifice in quality of life. Finally, McDermott stressed the importance of acknowledging that the industry often has not rewarded shareholders adequately, but pointed to growing digital components of oil and gas portfolios as an encouraging sign. After the initial presentations, Clegg moderated a discussion of questions sourced from the virtual audience. While the questions spanned a range of concerns, three central themes included the pursuit of sustainability, with an emphasis on carbon capture; the shape that future work environments might take; and how digital technologies power industry innovation and thus affect public perception. In addressing the first of these, Battalora identified major projects involving society-wide stakeholder involvement in pursuit of a regenerative “circular economy” model, such as Scotland’s Zero Waste Plan, while McKenna cited the positives of CO2-injection approaches, which he said would involve “partnering with the world” to achieve both economic and sustainability goals. While recognizing the importance of the UN SDGs in providing a global template for sustainability, McDermott said that the industry must address the fact that many investors fear rigid guidelines, which to them can represent limitations for growth or worse.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

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