Saasen, Arild (University of Stavanger) | Pallin, Jan Egil (JAGTECH AS) | Ånesbug, Geir Olav (JAGTECH AS) | Lindgren, Alf Magne (Schlumberger Oilfield Services) | Aaker, Gudmund (Schlumberger Oilfield Services) | Rødsjø, Mads (AkerBP)
Different logging operations can suffer from presence of metallic particles in the drilling fluids. Directional drilling in Arctic areas can be a challenge because of magnetic contamination in the drilling fluid. This is a challenge especially when drilling east-west relative to the magnetic north direction. Magnetic and paramagnetic particles in the drilling fluid will shield the down hole compasses and introduce additional errors to the surveying than those normally included in the uncertainty ellipsoid. The objective of the project is to remove the magnetic particles being the largest contributor to this error.
On many offshore drilling rigs there is mounted ditch magnets to remove metallic swarf from the drilling fluid. These magnets will normally only remove the coarser swarf. In this project we use a combination of strong magnets and flow directors to significantly improve the performance of the ditch magnets. This combination, together with proper routines for cleaning the ditch magnets significantly helps cleaning the drilling fluid.
By the combined use of flow directors and ditch magnets it was possible to extract more than five times as much magnetic contamination from the drilling fluid. This is verified by comparing the ditch magnet efficiencies from two drilling rigs drilling ERD wells. The logging tool signal strengths of several down hole instruments were unusually good and insignificant down times were observed on the logging tools. The results are anticipated to have aided to the directional drilling performance.
Detailed information on how to clean the drilling fluid properly from magnetic contamination is presented. It is also shown that this cleaning is significantly better than conventional cleaning of magnetic debris from drilling fluids.
The Internet of Things (loT) has paved the way for significant efficiency gains in the oil and gas industry. One concept that has garnered significant attention is the "digital twin". However, there remains a great deal of confusion surrounding what a digital twin actually is and how it can be harnessed to add value to oil and gas operations. Some use digital twin as a synonym for their 3D plant models, others for their predictive maintenance solutions, or their simulation models. The bottom line is that the digital twin is all of these and more and unless operators look at it holistically, they are likely to miss out on some of the benefits.
Digital twins afford companies a number of advantages that would otherwise not be possible, including the ability to run risk analyses, health assessments, and what-if scenarios in real-time; the ability to train personnel in a 3D immersive, risk-free environment; and the capability to detect faults early before control limits are reached. This paper/ presentation will elaborate on how digital twins can be used to enhance efficiency and will address their use in the wider context of the oil and gas industry – with a particular focus on its impact on reducing risk and cost during both the project and operational phases of the asset lifecycle.
The objective is to demystify the digital twin, outline the advanced capabilities it enables and illustrate how oil and gas operators can use this concept to improve their competitive advantage.
Africa (Sub-Sahara) ExxonMobil subsidiary Esso Exploration Angola has started oil production at the Kizomba Satellites Phase 2 project offshore Angola. The project involves the development of subsea infrastructure for the Kakocha, Bavuca, and Mondo South fields. Mondo South is the first field to begin production, and the other two satellite fields will follow later this year. The goal is to increase Block 15's production to 350,000 BOPD. Esso (40%) is the operator with BP Exploration Angola (26.67%), Kosmos Energy discovered gas at the Tortue West prospect in Block C-8 offshore Mauritania.
In recent years, the oil and gas industry has gained greater operational efficiencies and productivity by deploying advanced technologies, such as smart sensors, data analytics, artificial intelligence and machine learning — all linked via Internet of Things connectivity. This transformation is profound, but just starting. Leading offshore E&P operators envision using such applications to help drive their production costs to as low as $7 per barrel or less. A large North Sea operator among them successfully deployed a low-manned platform in the Ivar Aasen field in December 2016, operating it via redundant control rooms — one on the platform, the other onshore 1,000 kilometers away in Trondheim, Norway. In January 2019, the offshore control room operators handed over the platform's control to the onshore operators, and it is now managed exclusively from the onshore one. One particular application — remote condition monitoring of equipment — supports a proactive, more predictive condition-based maintenance program, which is helping to ensure equipment availability, maximize utilization, and find ways to improve performance. This paper will explain the use case in greater detail, including insights into how artificial intelligence and machine learning are incorporated into this operational model. Also described will be the application of a closed-loop lifecycle platform management model, using the concepts of digital twins from pre-FEED and FEED phases through construction, commissioning, and an expected lifecycle spanning 20 years of operations. It is derived from an update to a paper presented at the 2018 SPE Offshore Technology Conference (OTC) that introduced the use case in its 2017-18 operating model, but that was before the debut of the platform's exclusive monitoring of its operations by its onshore control room.
Among the many buzzy, digitally related words or terms bandied about the industry over the past year or two, “digital twin” serves as something of a confluence of them all. Populating many industry conference agendas are high-level presentations and discussions with descriptors such as digitization, digitalization, digital transformation, and the digital disruption, which involve big data, data analytics, advanced analytics, artificial intelligence (AI), machine learning, automation, the Internet of Things (IoT), and the ever-important, abundantly abstract cloud. Some of those terms are used rather broadly and interchangeably, leading many to wonder: What exactly are we talking about here? The definition of a digital twin is similarly less finite, but it is rather easy to conceptualize at a basic level. The technology links the physical world with the digital world, providing a digital model of a physical asset or process. It serves as a real-time data hub for its owner, allowing for reference to designed or expected performance and continuous offsite monitoring.
An application at the forefront of the accelerating digitalization of offshore exploration and production (E&P) is remote condition monitoring (CM) of platform equipment, especially with a unique data-sampling technique called time stamping. CM tracks the performance data of equipment, watching for deviations from baseline performance benchmarks. Any unexpected variances from those established baselines may indicate a developing fault in systems found typically on offshore platforms. Technicians can then be dispatched to further investigate or service the equipment, targeting root causes of the variances—an approach called condition-based maintenance (CBM). The CBM approach has shown that it can improve reliability, availability, and asset use.
Stone, Terry W (Schlumberger) | Moen, Terje (Schlumberger) | Edwards, David A. (Schlumberger) | Shadchnev, Alexander (Schlumberger) | Rashid, Kashif (Schlumberger) | Kvilaas, Geir Frode (Det norske oljeselskap ASA) | Christoffersen, Kjell (Det norske oljeselskap ASA)
Inflow control devices (ICDs) are designed to provide an even inflow across production zones by adjusting the completion pressure differential in order to balance reservoir drawdown. This helps to delay production of unwanted fluids whilst enhancing oil production. Newer designs also permit a degree of autonomous control. Although able to function without intervention, they can, in some circumstances, detect if the inflowing fluid is desirable, and act to restrict the flow if it is not. Several designs for these autonomous inflow control devices (AICDs) are available from service companies. One forces inflowing fluids to enter gates depending on inertial and viscous forces of the various fluids. Another is an autonomous valve in the shape of a free floating disc that restricts the flow rate of low viscosity fluids and is primarily used to choke gas and water inflow. Recently, a device with water swellable rubber inside the nozzle has been proposed, but is not yet commercially available.
This paper is divided into three parts. First, AICD published performance data from two commercially available devices is fitted with a generalized Bernoulli equation with adjustable parameters. Second, an optimization study is carried out where AICD device strengths and calibration parameters are specified as optimization control variables for all production wells in a detailed simulation study of a North Sea reservoir. Located in the northern part, the Ivar Aasen field (formerly Draupne Field) is a sandstone oil field where oil viscosity is low, similar to that of water. Wells in this setting can exhibit significant GOR and water cut at various times. Optimized parameters from this study, with a Net Present Value objective function, are compared to parameters from the fitted devices to determine whether commercially available devices have the performance of optimum devices, for this field. Third, to compare oil production and water control of the two commercially available devices and a third proposed device with water swellable rubber in the nozzle, a comparison of recoveries is made between a base case with open-hole completions and cases where wells are equipped with these three AICD designs. Discussions focus on (i) properties of these various devices in order to design an improved AICD for recovery operations in this field, (ii) a comparison of these devices against standard nozzle ICDs where it is shown that, for this field, the standard nozzle ICDs perform as well as the AICDs.