The Experts below are selected from a list of 8337 Experts worldwide ranked by ideXlab platform
Duane A Mcvay - One of the best experts on this subject based on the ideXlab platform.
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quantification of uncertainty in Reserve Estimation from decline curve analysis of production data for unconventional reservoirs
Journal of Energy Resources Technology-transactions of The Asme, 2008Co-Authors: Yueming Cheng, Duane A McvayAbstract:Decline curve analysis is the most commonly used technique to estimate Reserves from historical production data for the evaluation of unconventional resources. Quantifying the uncertainty of Reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in the analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of Reserve estimates with three confidence levels (P10, P50, and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic Reserve Estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for the probabilistic quantification of Reserve estimates using decline curve analysis. We examine the reliability of the uncertainty quantification of Reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in Reserve estimates changes with time as more data become available. We demonstrate that our method provides a more reliable probabilistic Reserve Estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.
Hoay Beng Gooi - One of the best experts on this subject based on the ideXlab platform.
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Spinning Reserve Estimation in Microgrids
IEEE Transactions on Power Systems, 2011Co-Authors: Mingqiang Wang, Hoay Beng GooiAbstract:In this paper, a probabilistic methodology for estimating spinning Reserve requirement in microgrids is proposed. The spinning Reserve amount is determined by a tradeoff between reliability and economics. The unreliability of units and uncertainties caused by load and nondispatchable units are both considered. In order to reduce computation burden, various uncertainties are aggregated. A multistep method is proposed to efficiently consider the combinatorial characteristic of unit outage events. The computation efficiency of spinning Reserve calculations can be greatly improved. The optimization is solved by mixed integer linear programming (MILP). Two case studies are carried out to illustrate the proposed method. Their results and discussions are also presented.
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spinning Reserve Estimation in microgrids
Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009Co-Authors: Mingqiang Wang, Hoay Beng GooiAbstract:In this paper, a probabilistic methodology for estimating the spinning Reserve in microgrids is proposed. The spinning Reserve amount is determined by maximizing the total profit while considering the unreliability of units and uncertainties caused by nondispatchable units and load. In order to reduce the computation burden, various uncertainties are aggregated and rounding to an equivalent distribution. The optimization is solved by mixed integer linear programming.
R.a. Bowden - One of the best experts on this subject based on the ideXlab platform.
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The Kayelekera Uranium Deposit, northern Malawi. Past exploration activities, economic geology and decay series disequilibrium
2016Co-Authors: R.a. Bowden, R. P. ShawAbstract:This paper describes the work carried out by the Central Electricity Generating Board (CEGB) on the Kayelekera Uranium Deposit in Northern Malawi between 1983 and 1991. This is one of the largest Karoo-age sandstone hosted uranium deposits yet discovered. Approximately 200 boreholes, about 60 % of which were fully cored, were drilled into the deposit during this evaluation. An important part of the ore Reserve Estimation undertaken by the CEGB at Kayelekera was gaining an understanding of the uranium decay series distribution within the deposit. Being located in a near surface environment the deposit is subject to weathering effects caused by oxidising groundwater. Three ore types are recognised, reduced facies ore, oxidised facies ore and transitional facies ore containing both oxidised and reduced material in varying proportions. Being more mobile under oxidising conditions uranium tends to be leached from the oxidised parts of the deposit and re-deposited in more reducing parts however its gamma emitting daughters tend to be less mobile in an oxidising environment leading to a marked disequilibrium between uranium and its daughters with the oxidized facies ore being depleted in uranium relative to its daughters and the reduced facies ore often showing relative enrichment
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The Kayelekera uranium deposit, northern Malawi : past exploration activities, economic geology and decay series disequilibrium
Applied Earth Science, 2007Co-Authors: R.a. Bowden, Richard ShawAbstract:AbstractThe present paper describes the exploration and evaluation work carried out by the Central Electricity Generating Board on the Kayelekera uranium deposit in Northern Malawi between 1983 and 1991. This is one of the largest Karoo age sandstone hosted uranium deposits yet discovered. Approximately 200 boreholes, ∼60% of which were fully cored, were drilled into the deposit during this evaluation. An important part of the ore Reserve Estimation undertaken by the Central Electricity Generating Board at Kayelekera was gaining an understanding of the uranium decay series distribution within the deposit. Being located in a near surface environment the deposit is subject to weathering effects caused by oxidising groundwater. Three ore types are recognised: reduced facies ore, oxidised facies ore and transitional facies ore containing both oxidised and reduced material in varying proportions. Being more mobile under oxidising conditions uranium tends to be leached from the oxidised parts of the deposit and...
Yueming Cheng - One of the best experts on this subject based on the ideXlab platform.
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quantification of uncertainty in Reserve Estimation from decline curve analysis of production data for unconventional reservoirs
Journal of Energy Resources Technology-transactions of The Asme, 2008Co-Authors: Yueming Cheng, Duane A McvayAbstract:Decline curve analysis is the most commonly used technique to estimate Reserves from historical production data for the evaluation of unconventional resources. Quantifying the uncertainty of Reserve estimates is an important issue in decline curve analysis, particularly for unconventional resources since forecasting future performance is particularly difficult in the analysis of unconventional oil or gas wells. Probabilistic approaches are sometimes used to provide a distribution of Reserve estimates with three confidence levels (P10, P50, and P90) and a corresponding 80% confidence interval to quantify uncertainties. Our investigation indicates that uncertainty is commonly underestimated in practice when using traditional statistical analyses. The challenge in probabilistic Reserve Estimation is not only how to appropriately characterize probabilistic properties of complex production data sets, but also how to determine and then improve the reliability of the uncertainty quantifications. In this paper, we present an advanced technique for the probabilistic quantification of Reserve estimates using decline curve analysis. We examine the reliability of the uncertainty quantification of Reserve estimates by analyzing actual oil and gas wells that have produced to near-abandonment conditions, and also show how uncertainty in Reserve estimates changes with time as more data become available. We demonstrate that our method provides a more reliable probabilistic Reserve Estimation than other methods proposed in the literature. These results have important impacts on economic risk analysis and on reservoir management.
Abubakarr Karim Barrie - One of the best experts on this subject based on the ideXlab platform.
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integrating artificial neural networks and geostatistics for optimum 3d geological block modeling in mineral Reserve Estimation a case study
International journal of mining science and technology, 2016Co-Authors: Abu Bakarr Jalloh, Sasaki Kyuro, Yaguba Jalloh, Abubakarr Karim BarrieAbstract:Abstract In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral Reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs. In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades. The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral Reserve evaluation method that could produce optimum block model for mine design.
