Enhanced-Oil Recovery (EOR) evaluations focused on asset acquisition or rejuvenation involve a combination of complex decisions, using different data sources. EOR projects have been traditionally associated with high CAPEX and OPEX, as well as high financial risk, which tend to limit the number of EOR projects launched. In this book, the authors propose workflows for EOR evaluations that account for different volumes and quality of information. This flexible workflow has been successfully applied to oil property evaluations and EOR feasibility studies in many oil reservoirs. The methodology associated with the workflow relies on traditional (look-up tables, XY correlations, etc.) and more advanced (data mining for analog reservoir search and geology indicators) screening methods, emphasizing identification of analogues to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance by using reservoir .
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Neural Computing and Applications
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Earth Science Informatics
We consider the challenging task of evaluating the commercial viability of hydrocarbon prospects based on limited information, and in limited time. We investigate purely data-driven approaches to predicting key reservoir parameters and obtain a negative result: the information that is typically available for prospect evaluation and is suitable for data-based methods, cannot be used for the required predictions. We can show however that the same information is sufficient to produce a limited list of potentially similar well-explored reservoirs (known as analogues) that can support the prospect evaluation work of human geoscientists. We base the proposal of analogues on similarity measures on the data available about prospects. Technically, the challenge is to define suitable similarity measures on categorical data like depositional environment or rock types. Existing data-based similarity measures for categorical data do not perform well, since they do not take geological domain know.
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IOR 2015 - 18th European Symposium on Improved Oil Recovery
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SPE Reservoir Evaluation & Engineering - SPE RESERV EVAL ENG
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The world’s energy consumption has risen geometrically over the last three decades due to advancement in technology. In response to this ever increasing rising demand, other sources of energy have been explored. Reports show that fossil fuels (crude oil and natural gas) continue to take the lead despite these efforts. Hence, the Oil and Gas industry has put in a lot of technical measures to meet up with this high energy demand. Many works have been done on how to increase reserves and ultimately increase recovery but little has been achieved in this area. Therefore, a more reliable, efficient and effective way of enhancing recovery is necessary to make headway in countering this challenge. This research seeks to provide the feasibility of enhanced oil recovery (EOR) projects using high level data mining technology in the African Oil Producing Regions. Data mining is a process which finds useful patterns from large amount of data. Data analysis process involves data exploration, pattern identification and pattern deployment to make accurate judgement necessary for EOR investment decisions. It provides a significant reduction in the level of uncertainty when compared with other existing techniques. In our concept, artificial intelligence and genetic algorithm was employed and recommendation made. Results show that data mining technique is a robust tool for making EOR investment decisions, right from the early life of a field. Thus, this concept makes it more economical to execute EOR projects with lower level of uncertainties. Marginal fields can also be invested upon using data mining techniques.
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SPE Reservoir Evaluation & Engineering
Summary We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the paramete.
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SPE Europec/EAGE Annual Conference
While North American shale gas recovery efforts are booming, Europe and other parts of the world are just entering the game. The first main hurdle for operators today is to decide if and where to plant a flag in these vast and essentially uncharted territories. This paper presents a process developed to support exploration teams in the screening and ranking of shale play candidates. It is based on the Analytical Hierarchy Process methodology. The decision problem is first broken down into a treelike structure, or hierarchy, of controlling criteria. These criteria can relate to any aspect of the decision—tangible or intangible, accurately or roughly estimated, quantitative or qualitative. They are weighted as a function of their relative importance in meeting the set goal. Secondly, the candidates are scored according to how well they fulfil the criteria. Finally, these evaluations are converted into overall ranks, enabling straightforward comparison of the various courses of action.
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SPE Annual Technical Conference and Exhibition
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Waterflooding is among the oldest and perhaps the most economical of oil recovery processes to extend field life and increase ultimate oil recovery from naturally depleting reservoirs. During waterflood operations, water is injected into the reservoir to maintain a certain reservoir pressure as well as to push the oil in the reservoir towards the producing wells. Nowadays, any organization always has to strive for lean and efficient technologies and processes to maximize profit also when looking deeper into their reservoir portfolios in order to identify additional waterflooding opportunities. Time and information constraints can limit the depth and rigor of such a screening evaluation. Time is reflected by the effort of screening a vast number of reservoirs for the applicability of implementing a waterflood, whereas information is reflected by the availability and quality of data (consistency of measured and modeled data with the inherent rules of a petroleum system) with which to extract significant knowledge necessary to make good development decisions. A new approach to screening a large number of reservoirs uses a wide variety of input information and satisfies a number of constraints such as physical, financial, geopolitical, and human constraints. In a fully stochastic workflow that includes stochastic back-population of incomplete datasets, stochastic proxy models over time series, and stochastic ranking methods using Bayesian belief networks, more than 1,500 reservoirs were screened for additional recovery potential with waterflooding operations. The objective of the screening process is to reduce the number of reservoirs by one order of magnitude to about 100 potential candidates that are suitable for a more detailed evaluation. Numerical models were used to create response surfaces as surrogate reservoir models that capture the sensitivity and uncertainty of the influencing input parameters on the output. Reservoir uncertainties were combined with expert knowledge and environmental variables and were used as proxy model states in the formulation of objective functions. The input parameters were initiated and processed in a stochastic manner throughout the presented work. The output is represented by a ranking of potential waterflood candidates. The benefit of this approach is the inclusion of a wide range of influencing parameters while at the same time speeding up the screening process without jeopardizing the quality of the results.
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