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Saudi Aramco studied such algorithms to produce images simulating the flow inside a pipe’s cross section, possibly reducing the need for separator-based multiphase flowmeters. A former technical manager with Petrobras discusses the development of the company’s flow assurance philosophies and strategies. Looped lines are used to reduce pressure drop and increase flow capacity, but information on the flow behavior or predictive methods are not available for these systems. Bypass pigging has advantages over conventional approaches. Its application to multi-phase flow with high wax content crude is discussed.
Measuring the flow of water, mud, and cuttings from a well is critical, and difficult. A new flowmeter design that promises to be both accurate and durable is one of three technologies featured in a JPT series on drilling measurement innovation. The complete paper discusses the advancements in mud-displacement simulation that overcome the limitations of the previous-generation simulator and provide a more-realistic simulation in highly deviated and horizontal wells.
The AI-driven tool will detect anomalies in subsea oil and gas infrastructure. Wood is Shell’s exclusive partner for Shell’s Smart Choke technology, which suppresses riser-induced slugging. Wood will provide the FEED and design for the offshore project. First oil is anticipated in 2024. As part of the contract, Wood will provide the topside modifications needed for the Snorre A and Gullfaks A platforms to integrate the Hywind floating wind park with existing systems powering the facilities.
Measuring the flow of water, mud, and cuttings from a well is critical, and difficult. A new flowmeter design that promises to be both accurate and durable is one of three technologies featured in a JPT series on drilling measurement innovation. This paper describes a virtual metering tool that can monitor well performance and estimate production rates using real-time data and analytical models, integrating commercial software with an optimization algorithm that combines production and reservoir information. In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells. Australian technology developer MezurX is touting its newly introduced flow, density, and mud monitoring system as a significantly better alternative to the widely used Coriolis meter.
Multiphase Measurements In Practice Course Description This course covers a brief introduction to multiphase measurements. This will include their value proposition, hardware selection with placement considerations and integration of the hardware and data. It will further focus on 3-phase in-line measurements and not necessarily on measurement after partial separation. Learning Level Introductory Course Length 1/2 Day Why Attend Measurement of produced assets (gas, oil and water) is essential for reservoir maintenance, production optimization, marketing, and other disciplines. When assets change hands, a robust understanding of the measurement, its uncertainty, benefits and challenges with these measurements enable the parties to make the right decisions.
The global oil industry has a large installed base of mature offshore platforms and facilities, often with declining production. A trend is to use this infrastructure to produce and process new discoveries, reducing time to production and recovery costs while extending infrastructure operating life.
However, production of new oil fields using existing infrastructure presents unique technical challenges. Accurate, repeatable and reliable measurement of produced oil and water is vital for fiscal, allocation or royalty purposes, as well as automation and control. This challenging measurement requires high-energy mixing with low power consumption and negligible pressure loss in a very small footprint.
The measurement uncertainty of net oil (oil minus water) for fiscal and allocation purposes is defined by international standards and contracts. With the increase in the use of declining facilities, a new technical challenge has emerged. To be able to accurately allocate the new fluids to the field, partner or different tax regimes they must be measured to fiscal accuracy prior to being mingled with other fluids for processing. Oil and water measurements are often performed at the output of first stage separation. This creates a challenge, as the fluids are close to the critical pressure, meaning that any pressure drop potentially results in cavitation. In addition, production pipelines are generally less than 8 inches, so any obstruction causes an undesired pressure loss or restriction.
These requirements highlighted the need for an effective, nonintrusive mixing device to enable operators to accurately measure and control the new fields being developed with today's greater capital constraints. Research was conducted, as part of a Joint Industry Project, at Imperial College in London followed by extensive computation fluid dynamics modeling to develop a conceptual design. The design was independently tested at the National Engineering Laboratory at East Kilbride, UK. The test independently verified that the design met the uncertainty performance criteria of the international API and ISO sampling standards across the wide range of water cut seen in both mature and new production. Once verified, the design was scaled for operating line sizes seen in the targeted application, and early deployment sites were identified.
The paper discusses the application envelope identified by offshore operators and the technical challenges they were seeking to solve. It follows the design process, highlighting the choices made and the results and methodology used in the independent testing to verify performance.
Multiphase flow meters are often built based on one or many single-phase flow metering technologies. Following the trend, Coriolis meters are being increasingly used in upstream applications in conjunction with an independent water cut meter to measure multiphase flow. Coriolis meters are well-known for fiscal metering applications as they offer unparalleled accuracy without having to input detailed information on the fluid being metered. They offer two distinct measurements: density, and mass flow rate, which is often not possible with other metering technologies. Subsequently, under multiphase flow, the biggest problem with liquid Coriolis meters is their tendency to stall when large amounts of gas flows through them. Many manufacturers over the last 10 years have developed techniques to adjust the drive gain to enhance the ability of these meters to handle increasing amounts of gas. There have also been several developments in using advanced signal processing and machine learning methods to help the meters to self-calibrate and correct for the presence of gas. These methods range from a simple error analysis on certain raw measurements to more sophisticated "Digital Twin" based concepts to simulate the behavior of the Coriolis meter internally. The paper describes the concept of "Digital Twin" in detail and outlines the reasons for the superiority of such an approach.
ElSayed, Mohamed Salah (PETROBEL) | Zayed, Samir (PETROBEL) | Tolba, Mohamed Ahmed (PETROBEL) | Omar, Muhammad Karam (PETROBEL) | Hussein, Ola Amr (Schlumberger) | Darwish, Hatem Mohamed (Schlumberger) | Negm, Mohamed Nagy (Schlumberger)
Offshore oil and gas fields have gained tremendous importance for the world's energy supply. Our ability to tap into these reserves is one of the main reasons that the predictions of the "Club of Rome" in 1972 about diminishing hydrocarbon reserves and "limits to growth" turned out to be pessimistic. As the transport of oil and gas products in a multiphase manner is increasingly stretched over greater distances with development in more hostile environments, especially when designing large-scale ultradeepwater production network, it is mandatory to ensure sustaining reliable and robustness operations as the access to subsea infrastructure becomes increasingly limited and to reduce the influence on the downstream facilities. An integrated multiphase dynamic model was used to optimize operating procedures for initial well cleanup and ramp-up to production from a sizeable deepwater production system before first gas. This approach was essential to create and test startup scenarios given several well and reservoir uncertainties.
Driven by lower-for-longer cost of operations, companies continuously look for effective ways to increase efficiency and support smarter decision-making. This paper will show how large operations such as Appomattox can use an integrated dynamic simulation-based solution throughout the project lifecycle to aid in design verification, operator training, startup support, and real-time surveillance. The recommendations and findings in this paper can be applied to similar project implementation efforts elsewhere in the oil and gas industry.
Appomattox is a four-column semi-submersible production platform located 80 miles off the coast of Louisiana in approximately 7,200 ft of water and features a subsea system with six drill centers, 15 production wells, and five injection wells. Appomattox produces from two deep-water fields – Appomattox and Vicksburg, with potential for asphaltene precipitation and scale formation due to high reservoir temperature and sour service.
The Appomattox project team decided in the design phase that dynamic simulation would be a useful tool to assist with operator training due to the complexity of the distributed control system (DCS) and the need for operators to gain familiarity with the combined cycle steam system, a first in the Gulf of Mexico. A multi-purpose dynamic simulator (MPDS) was developed that integrates high-fidelity subsea and topsides models together with the Appomattox control system to provide an environment where one can operate or test without the risk of upsetting the actual process.
Prior to integration with the control system, the MPDS was used for engineering studies that resulted in design changes well before first oil, generating significant cost savings when compared to finding these issues at startup. Once integrated, it was able to be used for operating procedure validation and multiple rounds of operator training prior to first oil. Other applications have included controller loop tuning – which generated an initial set of tuning parameters for use at startup, and leak detection algorithm testing by introducing subsea leaks of various sizes. The MPDS currently resides in Shell's Remote Operations Center for production support.
The MPDS model has been cloned to be used as a Real-time Surveillance System (RTS), running in parallel with the live facility and connected via Open Platform Communications (OPC) to its historian. The Appomattox RTS sits in Shell's Azure cloud, accessible to Shell users from anywhere. Current RTS applications include virtual multiphase metering, blockage detection in wells/flowlines due to asphaltene/scale deposition, and a performance monitoring system for topsides process coolers designed to predict exchanger fouling/plugging. All key calculated values are sent to Appomattox's historian where they are visible on surveillance screens.
The industry remains focused on achieving efficiency gains through accessing and processing production, asset and original equipment manufacturer (OEM) data, and applying machine learning (ML) principles to arrive at improved outcomes. As a service provider, we are experiencing an increased activity level related to hybrid analytics which involves embedding high-fidelity physics-based models together with ML models to improve outcomes. Critically, rather than addressing one piece of equipment, or a subset of a facility, companies focus on economies of scale, and seek asset-wide understanding of implications to equipment condition when changing operating parameters.
Deployment of a Dynamic Digital Twin provides production and maintenance engineers with a ‘single source of truth’ for information (i.e. P&ID, PFD, OEM information, maintenance history, etc.) integrated with high-fidelity physics-based models for subsea and topsides processes. Field sensors measuring hydrocarbon quantity, quality and other physical properties are integrated to provide real-time and historic data. Physics-based dynamic process models are first calibrated to match the field sensor data and then used to generate synthetic data for training ML models. A high-fidelity model generates virtual measurements where field sensors are not available. Access to such high-quality virtual measurements presents a paradigm shift for upstream analytics, as ML algorithms now have access to larger datasets for training. This improves quality, allowing for proactive planning and improved uptime leading to increased facility uptime by predicting equipment failure and enabling condition-based maintenance (CBM).
In our work with major oil and gas operators, we have observed that maintenance engineers until now have struggled, because enough field sensors are not always available to support the ML algorithms, leading to less specific assumptions and lower quality results. By taking advantage of a Dynamic Digital Twin - containing the asset structure, visualization and models - hybrid analytics were applied to continuously improve predictions, thereby increasing facility uptime.
In this paper, we present a few case studies of applying hybrid analytics with some oil and gas operators to enable virtual flow metering, prediction of unplanned equipment shutdown and prediction of optimum operating parameters for increased facility uptime. Examples presented demonstrate the integration of historic and real-time measurements with the physics-based process and multiphase flow models, and ML algorithms such as Autoencoder (AE), Long Short-term Memory (LSTM) neural networks and Reinforcement Learning.