No abstract

No abstract

In 1986, oil was discovered in the Permo-Triassic Khuff carbonates of the Yibal Permo-Triassic Khuff carbonates of the Yibal Field in North Oman. This formation which covers extensive areas of the Arabian Peninsula and Arabian Gulf, had hitherto never Peninsula and Arabian Gulf, had hitherto never tested oil and the Yibal Khuff accumulation is therefore unique in the region. Dry but sour oil rates of up to 1 200 m/d [7 550 bbl/d] were tested at a depth of 3 036 m [9 960 ft]. Subsequent appraisal wells, however, revealed the reservoir to be highly complex and dramatically downgraded initial expectations of an early, large scale development. This paper describes the stepwise approach adopted to meet the dual objective of better defining the reservoir whilst at the same time developing the reserves in a cost effective way. To this end, an appraisal/development plan has been designed consisting of the installation of limited liquid and gas handling facilities, further appraisal drilling, coring and extensive production testing. The synthesis of production test, reservoir geological and petrophysical interpretations has allowed the construction of a conceptual reservoir model consisting of a prolific, fractured crest where early gas breakthrough hampers the oil productivity and marginal, low permeable flanks. Areas of non-producible oil permeable flanks. Areas of non-producible oil have also been identified. Introduction The Permo-Triassic Khuff Formation forms part of a ramp type carbonate wedge thickening from less than 300 m [1 000 ft) on the Arabian Shield towards the NE into the present Arabian Gulf where more than 1 000 m [3 300 ft) have been recorded and major gas-bearing reservoirs have been discovered. The sequence, (total average thickness of 865 m [2 840 ft) at Yibal), may be subdivided into five zones, K1-K5, of which the top three (K1-K3) form the subject of this paper. The base of each of the K1-K3 zones consists of a tight, non-reservoir, lime-mudstone deposit of shallow marine/tidal flat origin. The overlying sequence is dominated by oolitic grainstones deposited as oolitic bars in a high energy, shallow subtidal environment. The Khuff reservoir of the Yibal field (Figs. 1 and 2) was discovered in 1977 by well Y-85 which tested sour gas from the K2 unit of the Khuff formation at a depth of ca. 2 910 m [9 550 ft)- The presence of oil was not identified and, hence the gas was classified as non-associated. Y-192, drilled end 1985 as a gas exploration well, tested oil from the K2, K3 and K4 Khuff units, gas/condensate from the K1 and K5, and volatile oil from the deep Gharif sands at ca. 3 940 m [12 900 ft]. A dry oil rate of ca. 1 200 m/d [7 550 bbl/d] was obtained while testing the K2. The crude is sour with H2S and CO2 contents of ca. 4 and 6 mole% in the gas phase respectively. As a result, the K1, K2, K3 reservoir was re-classified as a saturated oil reservoir with a gas cap. P. 631

-Carbon Capture and Storage (CCS) should be a key technology in order to achieve a decline in the CO 2 emissions intensity of the power sector and other intensive industry, but this potential deployment could be restricted by cost issues as the International Energy Agency (IEA) in their last projections (World Energy Outlook 2013) has considered only around 1% of global fossil fuel-fired power plants could be equipped with CCS by 2035. The SiteChar project funded by 7 th Framework Programme of European Commission gives the opportunity to evaluate the most influential parameters of techno-economic evaluations of four feasible European projects for CO 2 geological storage located onshore and offshore and related to aquifer storage or oil and gas reservoirs, at different stages of characterization. Four potential CO 2 storage sites have been assessed in terms of storage costs per tonne of CO 2 permanently stored (equivalent cost based). They are located offshore UK, onshore Denmark, offshore Norway and offshore Italy. The four SiteChar techno-economic evaluations confirm it is not possible to derive any meaningful average cost for a CO 2 storage site. The results demonstrate that the structure of costs for a project is heterogeneous and the storage cost is consequently site dependent. The strategy of the site development is fundamental, the technical choices such as the timing, rate and duration of injection are also important. The way monitoring is managed, using observation wells and logging has a strong impact on the estimated monitoring costs. Options to lower monitoring costs, such as permanent surveys, exist and should be further investigated. Table 1 below summarizes the cost range in Euro per tonne (Discount Rate (DR) at 8%) for the different sites, which illustrates the various orders of magnitude due to the specificities of each site. These figures have how to be considered with care. In particular the Italian and Norwegian sites present very specific features that explain the high estimated costs. For the Italian site, the short duration of CO 2 injection associated with a low injection rate makes the CO 2 project comparable to a demo project. The Norwegian site is an offshore site located in a virgin area with high infrastructure costs and a combination of injection duration and injection rate that makes the derived costs very sensitive to the discount rate. The results for both UK and Danish sites confirm therefore the value range calculated by the European Technology Platform for Zero Emission Fossil Fuel Power Plants (ZEP).The main uncertainties in the costs are linked both to the choice of economic parameters (e.g. injected quantities, contingencies) and to the technical choice of operations. This has been studied by sensitivity analyses: for example, if an injection rate is halved and the injection duration is doubled, the Equivalent Storage Cost (ESC) increases by 23% (UK case at 8% DR). Introducing a water production well and water treatment facilities also increases the ESC by 23%, at ...

To support the E&P investment decision-making process we use computer models extensively. This paper discusses in a conceptual way the potential merits of moving much more than what is currently being practised into the direction of "fully probabilistic" and "fully holistic" modeling, initially at the cost of model precision. In our opinion, the currently prevailing paradigm of maximum model precision (i.e. more physics, more grid blocks, more detail) may severely limit the optimization of the E&P decision-making process. Evidence for this statement is obtained when analyzing why the average of production and cashflow forecasts generally fails to coincide with the truth, as revealed in time: high-precision models on average result in biased forecasts and, hence, often lead to sub-optimal decisions or missed opportunities. To explain our approach, we introduce two postulates, discuss three modeling dimensions and link this to the business process of decision-making. The first postulate, when modeling the E&P value chain for investment decision-making, is that all uncertainties having a potential material effect on the model outcomes must be quantified within a comprehensive, internally consistent framework, and be taken into account when making E&P investment decisions. The second postulate is that, when trading-off the degree of "model precision" vs. the degree of uncertainty modeling and/or the degree of holistic value chain modeling, the latter two are more important than the former, especially when uncertainties are large. Initially therefore, reduced-physics or "approximate" models may be used, enabling a more comprehensive decision analysis. After having thus optimized the decisions, the sensivity (or "robustness") of the optimal decision to model precision should be tested. Multi-tiered decision-making, real options and an extended definition of "value" are also discussed within the framework's context. Introduction The current debate on uncertainty modeling and decision-making, in our opinion, lacks a clear framework and, hence, lacks the conditions to make substantial progress in the area of improved forecasts and decision-making. A multitude of professional meetings have been held on this theme by a multitude of organizations. The meetings however generally result in limited guidance on how to proceed conceptually and often divert into rather detailed building blocks that fail to demonstrate their relevance within the overall framework of decision-making. While acknowledging the many excellent papers by a variety of authors, we would like to build on their work and present a framework aimed at putting the building blocks together. Since the final objective of our modeling activities is to support the decision-making process, the role of each detailed modeling activity should be understood within this context. The value loop The first step is to understand how "value" is generated. Value is not generated by an E&P company's physical assets only (i.e. hydrocarbon reservoirs). In order to capture the potential value of an asset, data on that asset must be acquired, the data must be processed and interpreted, mathematical models of the assets must be constructed in order to assess the benefits of certain actions (e.g. investments), decision options must be generated, decision criteria must be applied under various constraints, the optimal decision is then implemented, which again results in the intrinsic value being realized and new data being acquired. In this "value loop", the role of models is essential. Models allow decisions to be made and activities to be implemented, thereby generating new data and closing the loop. Let us therefore look more in detail into how we can conceptualise our modeling work.

On behalf of a group of sponsors consisting of the Norwegian Petroleum Directorate (NPD) and most E&P companies active in Norway, a workgroup was set up to author a report on the Best Practices and Methods in Hydrocarbon Resource Estimation, Production and Emissions Forecasting, Uncertainty Evaluation and Decision Making. The workgroup is part of Norway's forum for Forecasting and UNcertainty (FUN). Following a detailed data acquisition and interviewing phase to make an inventory of the current practice of all sponsors involved, the workgroup postulated a relationship between a company's practices and its economic performance. A key distinguishing factor between companies is the degree to which probabilistic methods are adopted in integrated multi-disciplinary processes, aimed at supporting the decision making process throughout the asset life cycle and portfolio of assets. Companies have been ranked in terms of this degree of integration and best practices are recommended. In many companies a gap seems to exist between available and applied technology. Data and (aggregated) information exchange between Governments and companies is also discussed. A best practice based on their respective decision making processes is recommended. Introduction Norway's forum for Forecasting and UNcertainty evaluation (FUN, ref. 1) was established in 1997, and has 18 member companies plus the Norwegian Petroleum Directorate (NPD). The forum is a Norwegian Continental Shelf arena to determine best practice and methods for hydrocarbon resource and emissions estimation, forecasting uncertainty evaluation and decision making. It focuses on matters related to forecasting and uncertainty evaluation of future oil and gas production. Its main purpose is to optimize the interplay between the private industry and the national authorities wishing to regulate their national assets. The basic question that kicked off the FUN Best Practices project was whether the accuracy of Norway's historical production forecasts has been disappointing because of erroneous contributions from the companies or because of wrong aggregation by NPD. The question was posed which "Best Practices" could improve this situation. Whereas reserves form the basis for production, capex, opex and emissions forecasting, the decision making process in the various companies and national authorities links the various components together. Using the latest WPC/SPE guidelines for reserves reporting (allowing the use of probabilistic methods), the project concentrated on assessing the potential advantages of probabilistic techniques when used in combination withfully integrated asset management workflow processes. After a discussion of the current practices of the various companies and authorities visited, "Best Practices" are formulated in the fields of estimating reserves, production, costs and emissions forecasting, decision-making, planning and communications. The paper concludes with recommendations on how to move from the "current practices" to the desired "Best Practices".

To support the E&P investment decision-making process we use computer models extensively. This paper discusses in a conceptual way the potential merits of moving much more than what is currently being practised into the direction of "fully probabilistic" and "fully holistic" modeling, initially at the cost of model precision. In our opinion, the currently prevailing paradigm of maximum model precision (i.e. more physics, more grid blocks, more detail) may severely limit the optimization of the E&P decision-making process. Evidence for this statement is obtained when analyzing why the average of production and cashflow forecasts generally fails to coincide with the truth, as revealed in time: high-precision models on average result in biased forecasts and, hence, often lead to sub-optimal decisions or missed opportunities.To explain our approach, we introduce two postulates, discuss three modeling dimensions and link this to the business process of decision-making. The first postulate, when modeling the E&P value chain for investment decisionmaking, is that all uncertainties having a potential material effect on the model outcomes must be quantified within a comprehensive, internally consistent framework, and be taken into account when making E&P investment decisions. The second postulate is that, when trading-off the degree of "model precision" vs. the degree of uncertainty modeling and/or the degree of holistic value chain modeling, the latter two are more important than the former, especially when uncertainties are large. Initially therefore, reduced-physics or "approximate" models may be used, enabling a more comprehensive decision analysis. After having thus optimized the decisions, the sensivity (or "robustness") of the optimal decision to model precision should be tested. Multi-tiered decision-making, real options and an extended definition of "value" are also discussed within the framework's context.

The Intelligent Energy vision is particularly relevant to mid-career professionals with strong management potential. As aspiring asset or service managers, this group has a strong need to improve their analytic and integrative skills, and adopt the holistic view of the industry which characterises the Intelligent Energy approach. This paper describes results of five years experience with an Executive Master program for mid-career professionals. The program was initiated at the time that Intelligent Energy concepts were being formulated, and embodies many of those principles. It is designed to achieve, within twelve months, a quantum leap in the multi-discipline integration and multi-level abstraction skills of participants, enabling them to solve efficiently complex managerial problems much earlier than traditional training programs. They learn to formulate an integrated vision of the E&P value chain and how this creates a framework for computing added value of individual projects. Mastering this skill is normally achieved after years of experience. The course could be labeled "the MBA for E&P professionals", however with both feet firmly on technical ground. The core of the program is the thesis project, which is designed to integrate all skills acquired during the program using real-world company data. It consists of two parts: an individual thesis topic relevant to the participant’s work in their own company; and a team thesis in which the group creates a virtual company and makes a corporate analysis of the portfolio generated by the individual projects. The paper gives a brief overview of the program, emphasising the shared vision with Intelligent Energy. We then discuss in detail the thesis project, and use examples of the individual theses to illustrate how the graduates and their theses act as catalysts for industry best-practices on their return to their home company.