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Abstract Local Content policies are enacted by governments to provide incentives to local industry so that it can develop and attain global competitiveness. The goals of the Brazilian local content policy are to increment participation of the national industry on a competitive basis, develop technologies in Brazil, increase the competence of the technical community and generate employment and income in Brazil (ANP, 2008). The additional dimension of local content directly affects both the schedule and cost of execution of the project. This makes the job of the project manager much more difficult and much more critical. The purchasing situation is much more complex than in traditional project management. Traditional tools such as Gantt Charts and S-Curves are no longer sufficient for the work of a project manager. Global sourcing of products is a pre-requisite for economic project execution in the competitive oil and gas equipment market. The requirement to meet a certain local content places restrictions on the purchase of the best available global option. Often the price of a product with high local content is also higher than the best available price in the global market. Local content requires the project management function to be good at evaluating the premium they are willing to pay for local content. Which option should a project manager select - A supplier with 50% Local Content and a price of 100 or another supplier with 60% Local Content and a price of 105? This is not a theoretical question but a very practical one. This paper proposes a framework that allows the project management personnel to optimize the three dimensions of Lead Time, Local Content and Cost. A recent study on the competitiveness of Brazilian O&G industry concluded, as confirmed by our experience, that equipment highly dependent on scale or with relatively small levels of sophistication present higher differences in prices between global and Brazilian sources. The marginal cost of local content increases with increasing local content. The slope of a graph of cost vs. Local Content is asymptotically increasing. If all the products that make up a system had a similar price vs. local content function, the optimal system price would be achieved when all the constituent products are purchased at the system required local content. Any other system is non-optimal. In case of complex systems, it is more advantageous to purchase higher local content for more complex products and lower local content in less complex products. This insight is counter intuitive. EPIC companies purchase their most complex systems first - at the best prices and try to make up LC in the lesser complex products. This results in a non-optimal system cost. At a more abstract level, this observation has significance for Brazil local content policy. It is much better to incentivize and focus on higher technology Local Content as this has a higher probability of being cost competitive on a global scale. The hurdles for achieving global competitivity for lower technology equipment are higher. If the government policies and the market economics are aligned, this policy could bear better results - faster.
Marine dredged clay of high water content treated with low quantity of cement have been widely used as construction material for recycling, in Japan. In this study, strength mobilization of cement treated clay was carry out with different water content and cement content based on the laboratory test such as vane shear test and unconfined compressive test. Finally, Using indexes, initial water content and volume ratio normalized by liquid limit, the formulas, which predict the strength clay treated with low cement content on initial curing time, was proposed.
Abstract The Upper Cretaceous Fruitland Formation of the San Juan Basin of Colorado and New Mexico has been a very active natural gas play in recent years. Case studies of coal gas-in-place volume reassessments have revealed that gains of up to 74% are possible based upon the use of improved analysis methods recently developed by the Gas Research Institute (GRI). The greater gas-in-place estimates were consistent with production history and provide a new perspective upon the producible coal gas resources. Coal gas-in-place volume is proportional to the reservoir drainage area and three petrophysical parameters: gross reservoir rock thickness (consisting of both coal and other rock types), average reservoir rock density, and average in-situ sorbed gas content. The GRI procedure evaluates each of these parameters with a combination of core and density log data for specific reservoirs. Quantitative errors and causes of errors in these petrophysical parameters have been determined. Errors in coal gas-in-place volume estimates are caused by:geologic structural and stratigraphic variations that disrupt the lateral continuity of coalbeds using a too low maximum density cut-off limit value when determining gross reservoir rock thickness with density log data; by basing average reservoir rock density estimates upon "rules of thumb" or bounding rock densities; by performing gas desorption measurements at ambient surface temperature; and by basing in-situ sorbed gas content estimates on gas desorption data collected from drill cuttings rather than whole core samples. Introduction Coal seam gas reservoirs hold approximately 13 percent (134 Tscf) of U.S. natural gas resources1 and in recent years have been one of the most active natural gas plays in the U.S. There are currently about 6,300 coal seam gas wells throughout the U.S. which accounted for nearly 5 percent (858 Bscf) of annual domestic gas production during 1994. The success of coal seam gas production in the U.S. has sparked intense interest worldwide in this gas resource, particularly among several coal-rich nations in Eastern Europe and Asia. One of the keys to reliably determining the economic value of coal seam gas reserves is to accurately estimate the volume of gas-in-place. However, this critical analysis presents some unique data acquisition and interpretation challenges. A key difference between coal and conventional gas reservoirs is that, in the former, the vast majority of the gas-in-place volume is stored by physical sorption whereas in the latter the gas is stored by compression. The coal gas-in-place volume is proportional to three petrophysical parameters: the reservoir rock thickness (consisting of both coal and other rock types), the average reservoir rock density, and the average in-situ sorbed gas content. These three parameters are generally determined using data obtained from open-hole geophysical logs and core samples. Little has been published concerning the accuracy, comparability, and limitations of the most commonly used methods for determining these three crucial parameters. Two observations indicate that the petrophysical data used for calculating coal gas-in-place volume may frequently be inaccurate. First, accurately estimating the volume of gas stored in-situ by sorption requires making measurements on coal samples. However, widely different in-situ sorbed gas content estimates are obtained from different types of coal samples or data analysis methods. Recent Gas Research Institute (GRI) research has revealed that some commonly used coal sample type and data analysis methods have inherent shortcomings which result in in-situ sorbed gas content estimates that are low by 50%. P. 439
Abstract The proper measurement and analysis of gas desorption data from freshly cut coal samples is crucial to reliably predict the gas production performance and reserve potential of coal seam gas reservoirs. The technical literature offers may procedures for evaluating the total sorbed gas content of coal samples. This paper presents evaluations of the accuracy and comparability of gas content estimates for freshly cut San Juan Basin Fruitland Formation and Piceance Basin Cameo coal samples obtained using commonly practiced analysis procedures. Benchmark (or "ground truth") gas content values were assembled from pressure core and sorption isotherm data for comparison against gas content estimates obtained from conventional whole core, sidewall core, and drill cutting samples. Lost gas content estimate accuracy determined by the Direct (U.S. Bureau of Mines) Method, the Smith & Williams Method, and the Amoco Method was evaluated and compared. Assessments were also made of the sources and magnitudes of sampling and procedural method errors on the accuracy of the gas content estimates. The choices of sample type, gas desorption temperature conditions, and lost gas analysis method significantly affected the accuracy of the gas content values. The Direct Method analysis of conventional whole core, reservoir temperature gas desorption data provided the most accurate gas content values. Using ambient temperature gas desorption conditions rather than reservoir temperature conditions is the greatest source of error in gas content estimates. Introduction Coal seam gas reservoirs hold a significant portion, approximately 14 percent (135 Tcf), of U.S. natural gas reserves and currently account for approximately 4 percent (800 Bcf) of annual, domestic gas production. This new gas resource still holds a significant, untapped gas deliverability potential. In the years to come, production from this new gas play will be critical to providing adequate gas supplies at reasonable prices. Gas deliverability from coal seam reservoirs depends upon the amount of gas-in-place and the coal's gas sorption storage and production mechanisms. There are no standard, petroleum industry procedures for performing gas content analysis of coal seam reservoirs. Current methods used for determining coal seam reservoir gas content values require that coal samples be obtained at the time of drilling exploration or development wells. The freshly cut coal samples are used in gas desorption experiments that are started on location as soon as possible after sample collection. The need for on-site gas desorption experiments results from two limitations of available technology. First, accurate coal seam gas content estimates cannot be obtained from wireline log data without calibration by core derived sorbed gas content data. This limitation occurs since the presence of gas has little affect upon the bulk properties of the coal. For example, an in- situ methane content of 400 scf/ton increases the density of a pure coal sample of density equal to 1.25 g/cm3 by 0.010 g/cm3 or 0.8%. P. 327