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Special Section: The Value and Future of Petroleum Engineering Global climate concerns, amplified in the public consciousness by a steady stream of violent weather events such as hurricanes and California wildfires, are generating a new set of realities for the energy industry. The oil and gas upstream sector, accounting for approximately 60% of current world energy needs, faces existential threats to its market shareโwhere inaction and/or insistence on marginal improvisations on past practices do not offer constructive and, ultimately, impactful solutions that the industry is most capable of delivering. Central to the issues at hand are questions that demand unambiguous answers: What should be ambitious yet achievable goals for the upstream industry over the short and long term (e.g., by the year 2050) and what specific programs in the spirit of an Apollo project for oil and gas should be envisioned? The often-cited argument that upstream companies are โextractors and not emitters,โ and thus its responsibility in climate matters confined only to the extraction process, is shortsighted and dilutes steps that could be taken to maintain the industryโs leading role and capacity in providing the worldโs energy supplies. Net GHG Emissions As a basic premise, it is the net emissions of all greenhouse gases (GHG), not just CO2, that drive climate change. Hence, the upstream industryโs overriding goal should be reduction and eventual elimination of net GHG emissions. Here the key operative words are โnet GHG emissions,โ a distinction worth highlighting. This opens up numerous GHG management options, including CO2 capture and storage (CCS), utilization, and removal (CDR) pathways such as afforestation, reforestation, and bio-energy with CCS. This diverse portfolio enhances the ability of both market forces and new technologies to produce evergreen solutions for reducing net GHG emissions. Equally flawed as the โupstream are only extractorsโ notion is the idea that the oil and gas industry should be accepting a carbon-free world energy model fueled 100% by renewable energy sources. While renewables are an important part of the solution in addressing climate change, they are nowhere nearly capable of replacing what oil and gas offers in support of the modern lifestyle. Substantive life-style sacrifices, however, are unlikely at a global scale and so should not constitute the underlying assumption for an ecofriendly energy future. As a further tenet for clean energy, electric vehicles, power grids (currently 85% fueled by fossil and nuclear), and battery manufacturing plants should also be judged on net emission standards. There are no silver bullets in the fight against climate change. We need every bullet in our arsenal. Eliminating certain solution pathways, such as nuclear or fossil fuels, just makes a difficult task much more difficult and expensive. By the same token, the prospect of oil and gas playing an active role will only enhance the odds of achieving the ultimate goalโto have a positive, substantive impact on climateย change.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
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Guest editorial The US administrationโs withdrawal from the Paris Agreement on climate change does not eliminate the urgency of addressing existential issues for the oil and gas industry, both in the US or globally. At the center lie two questions: 1) Should the industry take a pro or con position regarding the Paris Accord or remain neutral? 2) What strategies are critical to maintain the oil and gas industryโs preeminence in the global energy markets? There is a brave new world of energy with three defining attributes: unitization, environmentalization, and extreme efficiency. Understanding these three will help answer both questions. Unitization is the emergence of a common energy languageโkilowatts, BTUs, or caloriesโto shop among different brands/sources of energy such as oil, gas, wind, solar, or others. To consumers worldwide, it matters little whether the cars they are driving or the planes they are flying in are fueled by shale oil or hydrogen so long as the economics are right and the fuel of choice meets prevailing public norms of โenvironmental wellness.โ The latter is an imperative, not an option, in this new age. Integration of environmental wellness into corporate and government actions constitutes the essence of โenvironmentalization.โ It is not meant to be synonymous with global warming. It is the sum total of influences on all aspects of the environment. The air quality of Chengdu, China, or fracturing-induced tremors in Oklahoma must be part of this conversation. What ties everything together for consumers worldwide is the ability to choose the most efficiently delivered kilowattsโenvironmentally and economicallyโhence the imperative for โextreme efficiency.โ Extreme efficiency is the new paradigm of energy markets for fossil fuels and renewables alike. A closer look at seemingly unrelated fronts is worth noting. In the Permian Basin of Texas, the bedrock of the US oil productionโs resurgence, crude output levels reached 2.6 million B/D in October 2017 despite a 50% drop in crude prices over the past 3 years. During the same period, US solar energy power generation more than tripled while unit costs dropped 33%. Similar trends in efficiency are evident in wind and unconventional gas. More importantly, CO2 emissions in the US and European Union have come down 20% during the past decade accompanied by comparable drops in energy intensity (consumption per GDP) while China started a downturn trend in CO2 emissions in 2013. More is on the way. The industry needs to take a proactive and solution-centric position vis-a-vis the Paris Agreement. The latter provides a facilitating, albeit imperfect, platform to strive toward modest climate goalsโto keep global temperatures from rising more than 2ยฐC by the end of the century. Staying with it would have been the wiser choice for the US given its prominent position in world affairs and its enormous capacity for innovation, even more so when supported by the right policies and realistic goals. It is in these two areas where the oil and gas industry can and should contribute to the Paris Accord.
- North America > United States > Texas (0.55)
- Asia > China > Sichuan Province > Chengdu (0.25)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
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Editor's note:This is the second installment of a yearlong series designed to stimulate discussion in research and development. The Technology Tomorrow articles will be published every other month and vary in emphasis, covering topics as broad as R&D industry trends and as focused as improving reservoir recovery factors. This series is one of several actions being taken by the SPE R&D Advisory Committee to encourage R&D development and discussion. The target audience is the entire readership of JPT. We hope to create a forum that sparks discourse and ideas, including ideas that may not be in the current mainstream of thought. Comments on the articles are welcome. Please send any questions, comments, or ideas to . By any measure of efficiency, globally reported expected ultimate recoveries (EURs) have been less than impressive. The often-quoted industry average of 35% recovery (for conventional crude oil) sets a useful benchmark for the future, and raises the question, "Can we double it for the next trillion?" As we look ahead to April 2007, the date of the first SPE R&D Conference, one might ponder several related questions: How high can the industry stretch EURs? What are the major impediments to achieving them? Do R&D programs have a role to play, or does the spontaneous technology boom in the industry coupled with free-market dynamics signal the end of organized R&D? A quick look at global oil resources, their acknowledged uncertainties not withstanding, calls for some perspective on this issue (Fig. 1). In 2005, global consumption had depleted only one out of seven barrels of conventional oil initially in place (and a fraction of nonconventional), a point perhaps overlooked by peak-oil advocates (Deffeyes, 2005; Tertzakian, 2006). A 10% incremental recovery translates to about 1.4 trillion bbl of recoverable resources, roughly an additional 50-year supply of global crude consumption at current rates. The gains in gas reserves could be equally rewarding. The point is not so much to argue for the veracity or the feasibility of these figures. Instead, the intent is to highlight vast possibilities in the context of twin questions: How can R&D shape the energy future, and how should the desired future outlook govern present-day R&D? Intent matters! The industry has no choice but to articulate its own anticipations for the future regarding trillions of barrels of recoverable resources. The recovery standards of the past century certainly cannot be the basis going forward.
- North America > United States (0.49)
- Asia > Middle East > Saudi Arabia (0.33)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.33)
The growing global demand for energy and, in particular, crude for our industry: an exponential increase in availability of supplies has brought into focus the twin issues of production sustainability real-time data and the heightened expectation of commensurate and reliability. The question "What are the constituent speed and accuracy in decision making and forecasting. The question elements that determine the sustainability of a set target from a typical that begs to be raised is, "How do we transform the massive volumes field?" resonates with new urgency. A school of thought within of data into intelligent decision making with business value?" But there might be another element, often outcomes emerging from fields in terms of expected ultimate recoveries, taken for granted, that could prove to be the critical link--prudent costs, and sustainability?
Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Introduction Extrapolation of current and emerging reservoir-management trends in the industry points to a process that is increasingly multidisciplinary, integrated, technology-based, information-loaded, and real-time. None of these attributes by themselves or in total can, however, guarantee profitability or success in the coming decades. What, then, is the winning formula? If a two-hour flight from New York to London represents a straightforward goal for the next-generation airplane, what is our expectation for the reservoir-management process in the year 2010? In searching for the right path, this article proposes a "learning" reservoir as a model for a continual self-improving process. Disruptive Technologies The concept of disruptive technologies (DTs) was introduced at a plenary session, "Anticipating, Recognizing and Managing Disruptive Technologies," held by the MIT office of Corporate Relations on 9--10 May 2000. DTs are technologies that can significantly disrupt the conventional way of doing business, positively or negatively. Reservoir management, when viewed as a continual optimization process, has a vital need for DTs, because they can, when managed smartly, more than overcome challenges of increasingly complex field operations (e.g., maturing reservoirs or tight formations). DTs can be classified into three major areas.Diagnostic. Information or knowledge management. Enhanced production or so-called "Q "technologies. DTs span the spectrum from reservoir imaging and/or characterization tools, such as 3D and 4D seismic and seismic-while-drilling, to dynamic simulation models. Examples of information- or knowledge-management technologies include advanced communication systems (e.g., satellites), real-time visualization platforms, and Web-enabled data pools. Intelligent downhole completions and novel production or drilling technologies fall under the Q category. Case Examples The Shaybah field in Saudi Arabia, which went on stream in mid-1998, provides a powerful demonstration of DTs in two areas. Fig. 1 shows that in the diagnostic technology area, a comparison of the Shaybah full-field simulation-model performance statistics between 1996 and 2001 indicates a two-thirds reduction in overall evaluation turn-around time, in spite of an almost fivefold increase in model size. This trend is a result of the significant computational advances provided by massively parallel processing(MPP) simulation, using Saudi Aramco's simulator as reported by Dogru and Pavlas. The dramatic effect of horizontal well drilling on Shaybah development costs is an illustration of Q technologies (Fig. 2). The field was developed in the late 1990s, primarily by use of 1-km horizontal wells. Had it been developed with conventional wells, assuming 1980s technologies, drilling costs would have been six fold higher on a cost-per-barrel basis of initial oil production. Continuing optimization efforts toward deploying more advanced downhole configurations (including extended horizontals, multilateral wells, and intelligent completions) will further reduce unit production costs. A similar example of Q technologies is provided by a comparison of two adjacent development areas (Area 1/ 1996 vs. Area 2/2001) of the Haradh field in South Ghawar (Fig. 3). On a cost-per-barrel comparison of initial oil production rate, Haradh 2 drilling costs are approximately 20%lower, in spite of a 2:1 reservoir quality disadvantage, as inferred by comparative production indices (PIs). An example from the information-technology domain is provided by SaudiAramco's Web-based well-approval process that manages the chain of activities extending from selection to putting wells on production. The new process, oriented toward value addition across the entire chain, is run by a multidisciplinary team and uses an online information highway, which enables a seamless communication among the process stakeholders (reservoir, production, and drilling engineers; Earth scientists; and environmental and land-management specialists). Perhaps the most important aspect of the online process is with respect to its role as a data bank and integrator for all drilling-related activities and, hence, as a learning platform for continual improvement. This is achieved through a control point and feedback process, which reports and grades performance metrics (actual vs. planned) in specific categories (e.g., costs, well productivity, and completion integrity). These metrics then are used for interfield and intrafield ranking (against time) of the drilling process. The online process is expected to generate annual savings in excess of U.S. $35million. Learning Reservoirs Reservoir management can be viewed as a continuous optimization exercise within the context of systems thinking, as proposed by Senge for complex organizations. By the same token, reservoirs can be regarded as learning platforms for this continuous optimization process over the life cycle of a field, as shown in Fig. 4.
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate (0.34)
- Asia > Middle East > Saudi Arabia > Eastern Province > Rub' al Khali Governorate (0.25)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.59)
- Asia > Middle East > Saudi Arabia > Eastern Province > Rub' al Khali Governorate > Rub' al Khali Basin > Shaybah Field (0.99)
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Lower Fadhili Formation (0.99)
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Khuff D Formation (0.99)
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Summary This paper evaluates reservoir performance forecasting. Actual field examples are discussed, comparing past forecasts with observed performances. The apparently weak correlation between advances in technology and forecasting accuracy is assessed. Parallel planning is presented as an approach that can significantly accelerate reservoir forecasts. The recognition of inevitable forecasting uncertainties constitutes the philosophical basis of parallel planning. Introduction We say that reservoir performance forecasting is not an exact science would be an understatement. Even with all the significant advances occurring across a wide spectrum of related areas, questions still remain regarding the reliability of reservoir predictions. In fact, our efforts today are aimed as much at defining the limits of uncertainty envelopes as at producing forecasts. The discussion here pursues the following questions:What are realistic accuracy expectations in performance forecasts? and Are our conventional thought processes in modeling inherently ill-structured to produce rapid forecasts? By their very nature, EOR processes introduce additional levels of complexity to forecasting. This discussion relates mainly to conventional reservoir systems. Forecasting Methods and Uncertainty Methods and Limits. Current reservoir performance forecasting methods can be classified into two broad categories: empirical and mathematical. This paper focuses on finite-difference methods because they represent the predominant industry-wide vehicle in reservoir evaluations. Empirical methods, such as decline curves, are useful, yet they have a limited application domain. Continuation of past production practices and mechanisms is a precondition for forecast reliability. Likewise, hybrid methods, while suitable for a wideclass of problems (e.g., miscible, pattern floods) have not yet fully matured to offer a universal forecasting capability. Lorenz recognized the stochastic nature and hence the inherent limitations of weather forecasting. Lorenz's celebrated "butterfly effect" example points to inevitable limits of predictability. The analogies between weather and reservoirs have been noted; specifically, the sensitivity of reservoir performance and hence forecasts to certain geologic parameters (i.e., flow boundary conditions) have been highlighted. This sensitivity suggests that performance forecasts will remain uncertain indefinitely. Both internal and external reservoir factors contribute to forecast uncertainties (Fig. 1). When model forecasts diverge from actual performance, distinctions among primary causes are sometimes lost. For example, accurate models may produce apparently poor forecasts when presumed field management strategies and facility outlays are not actually implemented as a result of external factors. When model forecasts duplicate actual performance, this can also be misinterpreted as model validation. In fact, the duplication could simply reflect compensating errors among the internal and external factors. The point here is that accurate forecasts do not mean accurate models. (The hypothetical corollary also appears noteworthy: poor forecasts do not necessarily equate to poor reservoir models.) The nature of the oil industry limits predictability of external factors, such as exact field operating practices. At best, multiple forecasts need to be developed for a range of external factors. Of the four uncertainty causes in Fig. 1, data quality and mathematical solutions are becoming less pronounced, and reservoir characterization and scale-up present the primary obstacles to improving performance forecasts. The lack of determinism in both external and internal factors suggests only the obvious:all reservoir performance forecasts carry a band of uncertainty. Ballin et al. attempted to quantify this uncertainty for a special class of problems. Haldorsen and Damsleth described a general methodology for producing stochastic forecasts. Discretization Both geostatistical and finite-difference models discretize reservoirs. The two models, however, have dissimilar discretization scales (geostatistical models use inches to several feet; finite-difference models use hundreds of feet). Current and projected hardware and software limitations suggest that the discretization gap between finite-difference and geostatistical models will not disappear for giant fields. Consider the multibillion-barrel ATL/INB field in West Africa. A finite-difference model using 1-ft3 cells will require about 0.5trillion cells. The corresponding figure for the Safaniya field in the MiddleEast is about 7 trillion cells. Cells (1 in.3) will suggest models with roughly800 trillion cells for the ATL/INB field. These numbers imply our indefiniteneed for a scale-up process and hence the resulting uncertainties. Homogenization An obvious outcome of the scale-up process is homogenization. Porosity/permeability transforms, often used to describe permeability fields, also contribute to homogenized property assignments in simulation models. Fig.2 gives the porosity/permeability core data for the ATL/INB field. This field exhibits a complex lithology of predominantly silica sands intermixed with dolomite. The use of a single-variable transform, represented by the solidline, filters the observed variability in the core data. An alternative approach that would reduce the homogenization effect is the use of "cloudtransforms" developed by Kasischke and Williams. Cloud transforms produceproperty representations in models that can mimic distributions observed in real data (e.g., cores or logs). Fig. 3 shows a sample distribution generated by a cloud transform for the Elk Hills 26 R reservoir. JPT P. 652^
- North America > United States > Utah > Virgin Field (0.99)
- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 3/8 > Ninian Field > Brent Group Formation (0.99)
- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 3/3 > Ninian Field > Brent Group Formation (0.99)
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