Wells in the Gullfaks field, situated in the Norwegian sector of the North Sea, are characterized by long, highly deviated producing intervals. Cased hole completions with 177.8 mm production liners are dominant, but since 1994 several wells have been completed as open hole in one or two intervals. The Gullfaks field is producing from a reservoir that consists of a multitude of high permeability loosely consolidated sandstone formations. Formation permeability varies from 50 md to as much as 10 darcies, while reservoir pressure ranges from 220 bar to 320 bar. Sand production from the loosely consolidated formations is prevented with traditional gravel-packs. Internal gravel-packs in the Gullfaks field are performed with under-balanced snubbing, thereby eliminating the need to kill the well at any stage of the operation. Leakoff in the different formation sands is well defined by log permeability and reservoir pressure; high choke pressure during the under-balanced operation aids in controlling the pre-determined return rate throughout the job. This paper describes an approach where gravel-pack treatments pumped in the Gullfaks field have been designed and evaluated using a pseudo three-dimensional gravel placement simulator. With reservoir pressure given as a range rather than a specific value, a sensitivity study yielded the best estimate for reservoir pressure. Following verification of reservoir parameters and simulator capabilities, new jobs were optimally designed using the simulator so that potential pitfalls in traditional designs could be avoided. Designs were modified to achieve a successful annular pack, also permitting a priori knowledge of safe operational limits. This further allows better control over fluid selection, rate determination, tool position and other parameters that are critical to achieving a successful outcome of the treatment. Post-job evaluation of gravel-pack treatments using this approach confirms the validity of the designs. Three wells in the Gullfaks field operated by Statoil, serve as the basis for the case studies presented in this paper. Recommendations are made on how to extend the technique to similar fields. P. 149
fax 01-972-952-9435. AbstractIn the mid-
As Rate of Reserve Replacement continues to be challenged, International Oil Companies (IOCs) face competition from other IOCs as well as from entrepreneurial national oil companies (NOCs) aggressively looking to develop assets domestically and overseas. A disproportionately large fraction of reserves are controlled by a few NOCs, and downstream resources are similarly pursued by NOCs and IOCs alike. Evaluation of potential countries or regions targeted for reserves replacement objectives must be made based on assessment of key risk factors for mid to long timeframe. Country Risk Assessments for new country or region entry decisions serve as a crucial decision point in determining viability of project or project portfolio from a location perspective, but such assessments typically tend to be time consuming, complex, somewhat subjective, and often do not provide a basis for any objective comparison between candidate countries or regions. Using a matrix of weighted risk factors this model presents a simplified approach for objectively assessing a potential country or region under consideration, and provides a basis for apples-to-apples comparison between shortlisted countries or regions. This approach is applicable to IOCs, NOCs, or Service Companies alike. Based on a relevance-weighted approach this model can assist in quick screening before further due diligence is embarked on. This not only accelerates decision-making for such investment considerations, but also provides input for later detailed project risk management. Introduction The hunger for oil continues to rage, undaunted by ever-climbing crude prices. Today's tight supplies are primarily because of surging world economy and growing demand, particularly in Asia and Middle East. Technological advances have made it possible to explore and exploit resources that were previously beyond reach, or simply uneconomic - but higher cost of development coupled with resource nationalism and regulatory uncertainty by NOCs and host governments have made it harder to bring supplies to market quickly. Amidst the feeding frenzy the reserve replacement challenge gets tougher with each passing day. As easier plays are tapped, the hurdle simply gets raised in terms of geographic, political, energy security, economic, or technical challenges that must be surmounted in order to reach the next barrel of crude. Today's realities appear to be more on management of above-ground risk in developing, transitional and politically unstable countries than managing below-ground risk. Figure 1 shows average annual change in consumption of crude oil and natural gas liquids in major world regions and countries in million of barrels per day. For example; in OECD Europe, oil consumption increased from 13.7 MMbbl/d in 1990 to 15.5 MMbbl/d in 2005 (a net increase of 1.8 MMbbl/d). Oil consumption is expected to increase by only 0.1 MMbbl/d in next 15 years in OECD Europe. On the other hand, in Russia and FSU countries, oil consumption is expected to dramatically increase in next 15 years after a long and continuous decline in consumption levels in these countries. In addition, developing Asia and the Middle East is expected to reshape the future oil market.
The workforce transition from the graying, experienced generation to the incumbent crop of workers underscores the absence of many advances that were promised as part of the Smart Wells and collective Intelligent/Digital Oil Field of the Future (DOFF). As we prepare to hand over controls to the thumbs seasoned on Nintendo controls, we don't commonly find touchscreen controls, 3-D visualizations, automatic diagnostics and predictive analytics widely prevelant in the industry. Environmental factors such as ubiquitous computing, networking, and communication have contributed to nurture a worker profile that is likely to exhibit a need for much more speed and openness. Incredibly adept at technology applications as compared to the outgoing workers, however, the same incumbent worker is going to be years behind on the experience scale. The average worker experience profile is going to see a dramatic drop in the next five to ten years. Workforce effectiveness and sustenance gains renewed focus in the oil field's "Big Crew Change". In that context, the delayed promises of the DOFF become painfully evident. This study challenges the notion that current pace of DOFF implementations is aggressive enough to materially contribute to the industry's future. Introduction Our quest for the erstwhile DOFF begins first and foremost with a general definition of the term. We propose that one or more of the following characteristics must be attributable to any initiative being considered a potential candidate of DOFF:Commonplace use of real-time, integrated data for wells and equipmentClosed-loop feedback cycles between Operations and Planning/AlanyticsEnhanced degrees of human collaborationUse of the latest communications technologies The precise definition can be elusive, though many have been attempted. Many oil companies have similar-sounding DOFF programs, though their details tend to vary wildly. To a certain degree this is natural, since DOFF programs need to address the most pressing problems for the company, much like any other improvement initiative. Though common pain points exist from company to company, these can be unique at the asset or business unit level - the level at which most DOFF programs originate and begin to take real shape. Status Check: Implementation Metrics and Qualifications As we attempted informal surveys among various majors, independents, and service companies to determine the approximate state of the industry, we came to realize that there is no authoritative source of data in most organizations that can clearly identify measurable progress towards DOFF. By and large, DOFF is a byproduct of general operations and optimization rather than an easily defined, distinguishable initiative by itself. Organizations generally tend to qualify metrics such as network bandwidth capability and utilization, data storage capability and utilization, and similar measures. Unfortunately, DOFF-specific progress metrics are virtually impossible to extract from those bundled numbers. Ambiguity surrounding the precise definition of DOFF further exacerbate this confusion. To be fair, it must be acknowledged that enabling, or component technologies are much more widespread than a decade ago. Examples are advanced SCADA systems for production operations, real-time monitoring centers for drilling operations, or collaboration technologies for support centers. It has been argued, however, that these represent evolutionary expansion and wider adoption rather than revolutionary progressions of capability.
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