The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract Offshore production in ultra deep water may present several technical challenges. New exploration frontiers, sometimes, could provide a very complex scenario for oil production, considering very far fields from the shore, very harsh environmental conditions and the necessity of well intervention, to maximize the recovering factor of the field. Scenarios like that demands functional requirements for production units which can be very hard to achieve considering conventional Production Units. The MPSO (Monocolumn Production Storage and Offloading) Unit concept has been being developed in recent years to combine small motion requirements and high stability with or without oil storage capability, with good results for oil production scenarios. However, the use of this type of Unit as a drilling Unit or for a dry tree completion scenario becomes a challenge in harsh environmental conditions, due to the very strict constrains required regarding motions and offsets of the unit. In order to obtain a Monocolumn design with dry completion capability, considering the Brazilian shore pre-salt scenario, an optimization process was set up, evaluating thousands of different configurations considering motion and stability requirements. Mooring analyses were also performed to guarantee a small offset and a small set-down of the unit. As a result, a different platform design was obtained, with very small motions and dry completion capability. Introduction For some scenarios considering pre-salt reservoirs, which are located in Santos Basin, in deep water productions fields, the use of dry completion Units could bring some economical advantages, as saving in rigging costs and rigging maintenance, a greater recovering factor of the oil fields and an improved capability of workover of the production wells. In 2008, Petrobras has performed assessments of different types of dry tree unit production platforms, as SPAR and TLP platforms. Besides that, a Monocolumn dry tree production platform concept was already designed for operations in those scenarios. The Monocolun concept has been being developed for different companies, including Petrobras, which has your own concept, called MonoBR (Masetti et al, 2006), developed in partnership with the São Paulo University, as shown in Figure 1. Besides the Petrobras concept, there are alternative concepts developed by other companies as well (Syvertsen et al, 2004), (Orr et al, 2009). The main idea of the MonoBR was originally designed to be riser friendly considering Steel Catenary Riser subsea systems an the concept combines low motion levels with storage capability and great stability, which are concurrent requirements, in general. To achieve this objective, in general, it is necessary a huge Unit, deep draft and the Use of huge appendages to create vortex damping and added mass effects.
A methodology for fatigue reliability based design optimization is proposed for the design of bending stiffener. Bending stiffener is employed to protect the upper connection of umbilical/flexible riser against damage from overbending. It is prone to cause fatigue failure due to the wave induced vessel motions. Therefore, its fatigue character has a great impact on the safety of oil and gas production and we should pay more attention to it. In addition, the fatigue analysis involves material, geometric, and loading uncertainties, hence the reliability analysis is performed for considering the influence of uncertain factors. In this work, the fatigue reliability based optimization involves the fatigue analysis and complex optimization algorithms the metamodel is used to reduce the computational cost. Threemetamodels are constructed by the optimum Latin hypercube method. Then, the optimum metamodel is selected for the optimization through the accuracy evaluation. The feasibility of the methodology is verified by a test case of beam. Finally, it is applied to the fatigue reliability based design optimization of bending stiffener. The results demonstrate that this methodology is rational and improves the fatigue reliability of bending stiffener. First, compared with deterministic optimization.
Abstract The well optimization technique with backward elimination aims to determine the optimum number of wells and their locations that can maximise project value and its recoverable resources, through repeated ranking of candidate wells and eliminating the poorest performer. For a greenfield development, subsurface uncertainties are typically still very large due to limited data from exploration and appraisal wells. This study outlines our approach to perform well optimization with these governing uncertainties in order to support the decision-making process. First, multiple realizations of reservoir models are constructed to represent range of possible outcomes by sampling different values from uncertainty parameters. Backward elimination for well optimization is then performed on those realizations. Wells can be ranked based on means and standard deviations of their performance, and the lowest rank candidate is eliminated from the process one at a time. At this point, project economic and resources are evaluated to find optimum set of wells for field development. Furthermore, well performance data from multiple realization models are carefully analyzed to define key subsurface uncertainties that need to be managed. Solution from this backward elimination with subsurface uncertainty workflow can maximize project valuation because it balances the risk of overspending to drill sub-optimum wells in some realizations with the risk of losing sell opportunity due to insufficient field deliverability in the other realizations. Development decision will be more robust because it is based on the optimum configuration that is applicable irrespective of the unknown uncertain quantities. Moreover, detailed analysis on well performance data allows us to better understand the risk associated with our planned wells so that appropriate de-risking plan can be developed and combined into development strategy. The backward elimination process is straightforward to implement and normally does not require very high computational expense. Thus, it is suitable to be used with uncertainty workflow with multiple realizations of reservoir models, which will increase computational requirement by multiple times. Other commonly used techniques for well optimization such as a Genetic Algorithm (GA) or an Evolutionary Algorithm (EA) are computational expensive by themselves already; and they will require even more runtime when using them with this uncertainty workflow. This paper extends backward elimination approach for well optimization to be used with uncertainty workflow. The overall uncertainty analysis workflow is discussed and provided, with key steps detailing the approach taken. Project valuation and recoverable resources can be further optimized with this new approach, and ultimately can guide the decision making in field development.
Bello, Opeyemi (Institute of Petroleum Engineering, Clausthal University of Technology) | Teodoriu, Catalin (Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma) | Yaqoob, Tanveer (Institute of Petroleum Engineering, Clausthal University of Technology) | Oppelt, Joachim (Institute of Petroleum Engineering, Clausthal University of Technology) | Holzmann, Javier (Institute of Petroleum Engineering, Clausthal University of Technology) | Obiwanne, Alisigwe (Institute of Petroleum Engineering, Clausthal University of Technology)
Abstract Artificial Intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and oil and gas upstream industry is no exception to it. AI involves the use of sophisticated networking tools and algorithms in solving multifaceted problems in a way that imitates human intellect, with the aim of enabling computers and machines to execute tasks that could earlier be carried out only through demanding human brainstorming. Unlike other simpler computational automations, AI enables the designed tools to "learn" through repeated operation, thereby continuously refining the computing capabilities as more data is fed into the system. Over the years, AI has led to significant designing and computation optimizations in the global Petroleum Exploration and Production (E&P) industry, and its applications have only continued to grow with the advent of modern drilling and production technologies. Tools such as Artificial Neural Networks (ANN), Generic Algorithms, Support Vector Machines and Fuzzy Logic have a historic connection with the E & P industry for more than 16 years now, with the first application dated in 1989 for development of an intelligent reservoir simulator interface, and for well-log interpretation and drill bit diagnosis through neural networks. Devices and softwares with basis from the above mentioned AI tools have been proposed to abridge the technology gaps hindering automated execution and monitoring of key reservoir simulation, drilling and completion procedures including seismic pattern recognition, reservoir characterisation and history matching, permeability and porosity prediction, PVT analysis, drill bits diagnosis, overtime well pressure-drop estimation, well production optimization, well performance projection, well / field portfolio management and quick, logical decision making in critical and expensive drilling operations. The paper reviews and analyzes this successful integration of AI techniques as the missing piece of the puzzle in many reservoir, drilling and production aspects. It provides an update on the level of AI involvement in service operations and the application trends in the industry. A summary of various research papers and reports associated with AI usage in the upstream industry as well as its limitations has been presented.
Abstract Many optimization tools exist for well placement into reservoirs for maximum oil recovery. Conventional tools such as simulated annealing, response surface technology, gradient-based optimization, mixed integer programming etc. abound. However, artificial intelligence optimization tools have emerged over the years and are gaining ground. Artificial bee colony (ABC) has become one of the most common optimization methods in the domain of Artificial Intelligence since it was first conceived in the early nineties. As a result, avalanches of researches to its credit in well placement optimization exist. This paper therefore, highlighted conventional well placement optimization tools and also reviewed the artificial intelligence based optimization tools especially ABC and hybrids of ABC Algorithms formulated for well placement and compared them with each other using four basic criteria. The review has shown that ABC algorithms are very efficient in handling the placement of wells in reservoirs during well planning. This work therefore opens up a new vista in the area of well placement optimization and is therefore recommended to anyone looking for a pivot on the well placement optimization discussion.