The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Mahendra K. Verma - One of the best experts on this subject based on the ideXlab platform.
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Reserve growth in Oil pools of alberta model and forecast
Bulletin of Canadian Petroleum Geology, 2010Co-Authors: Mahendra K. Verma, T A CookAbstract:Abstract Reserve growth is recognized as a major component of additions to Reserves in most Oil provinces around the world, particularly in mature provinces. It takes place as a result of the discovery of new pools/reservoirs and extensions of known pools within existing fields, improved knowledge of reservoirs over time leading to a change in estimates of original Oil-in-place, and improvement in recovery factor through the application of new technology, such as enhanced Oil recovery methods, horizontal/multilateral drilling, and 4D seismic. A Reserve growth study was conducted on Oil pools in Alberta, Canada, with the following objectives: 1) evaluate historical Oil Reserve data in order to assess the potential for future Reserve growth; 2) develop Reserve growth models/functions to help forecast hydrocarbon volumes; 3) study Reserve growth sensitivity to various parameters (for example, pool size, porosity, and Oil gravity); and 4) compare Reserve growth in Oil pools and fields in Alberta with those from other large petroleum provinces around the world. The reported known recoverable Oil exclusive of Athabasca Oil sands in Alberta increased from 4.5 billion barrels of Oil (BBO) in 1960 to 17 BBO in 2005. Some of the pools that were included in the existing database were excluded from the present study for lack of adequate data. Therefore, the known recoverable Oil increased from 4.2 to 13.9 BBO over the period from 1960 through 2005, with new discoveries contributing 3.7 BBO and Reserve growth adding 6 BBO. This Reserve growth took place mostly in pools with more than 125,000 barrels of known recoverable Oil. Pools with light Oil accounted for most of the total known Oil volume, therefore reflecting the overall pool growth. Smaller pools, in contrast, shrank in their total recoverable volumes over the years. Pools with heavy Oil (gravity less than 20° API) make up only a small share (3.8 percent) of the total recoverable Oil; they showed a 23-fold growth compared to about 3.5-fold growth in pools with medium Oil and 2.2-fold growth in pools with light Oil over a fifty-year period. The analysis indicates that pools with high porosity reservoirs (greater than 30 percent porosity) grew more than pools with lower porosity reservoirs which could possibly be attributed to permeability differences between the two types. Reserve growth models for Alberta, Canada, show the growth at field level is almost twice as much as at pool level, possibly because the analysis has evaluated fields with two or more pools with different discovery years. Based on the models, the growth in Oil volumes in Alberta pools over the next five-year period (2006–2010) is expected to be about 454 million barrels of Oil. Over a twenty-five year period, the cumulative Reserve growth in Alberta Oil pools has been only 2-fold compared to a 4- to- 5-fold increase in other petroleum producing areas such as Saskatchewan, Volga-Ural, U.S. onshore fields, and U.S. Gulf of Mexico. However, the growth at the field level compares well with that of U.S. onshore fields. In other petroleum provinces, the Reserves are reported at field levels rather than at pool levels, the latter basically being the equivalent of individual reservoirs.
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A New Reserve Growth Model for United States Oil and Gas Fields
Natural Resources Research, 2005Co-Authors: Mahendra K. VermaAbstract:Reserve (or field) growth, which is an appreciation of total ultimate Reserves through time, is a well-recognized phenomenon, particularly in mature petroleum provinces. The importance of forecasting Reserve growth accurately in a mature petroleum province made it necessary to develop improved growth functions, and a critical review of the original Arrington method was undertaken. During a five-year (1992–1996), the original Arrington method gave 1.03% higher than the actual Oil Reserve growth, whereas the proposed modified method gave a value within 0.3% of the actual growth, and therefore it was accepted for the development for Reserve growth models. During a five-year (1992–1996), the USGS 1995 National Assessment gave 39.3% higher Oil and 33.6% lower gas than the actual growths, whereas the new model based on Modified Arrington method gave 11.9% higher Oil and 29.8% lower gas than the actual growths. The new models forecast predict Reserve growths of 4.2 billion barrels of Oil (2.7%) and 30.2 trillion cubic feet of gas (5.4%) for the conterminous U.S. for the next five years (1997–2001).
Dequn Zhou - One of the best experts on this subject based on the ideXlab platform.
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stockpile strategy for china s emergency Oil Reserve a dynamic programming approach
Energy Policy, 2014Co-Authors: Y Bai, Carol Dahl, Dequn Zhou, Pei ZhouAbstract:Abstract China is currently accelerating construction of its strategic petroleum Reserves. How should China fill the SPR in a cost-effective manner in the short-run? How might this affect world Oil prices? Using a dynamic programming model to answer these questions, the objective of this paper is to minimize the stockpiling costs, including consumer surplus as well as crude acquisition and holding costs. The crude Oil acquisition price in the model is determined by global equilibrium between supply and demand. Demand, in turn, depends on world market conditions including China׳s stockpile filling rate. Our empirical study under different market conditions shows that China׳s optimal stockpile acquisition rate varies from 9 to 19 million barrels per month, and the optimal stockpiling drives up the world Oil price by 3–7%. The endogenous price increase accounts for 52% of total stockpiling costs in the base case. When the market is tighter or the demand function is more inelastic, the stockpiling affects the market more significantly and pushes prices even higher. Alternatively, in a disruption, drawdown from the stockpile can effectively dampen soaring prices, though the shortage is likely to leave the price higher than before the disruption.
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optimal path for china s strategic petroleum Reserve a dynamic programming analysis
Energy Economics, 2012Co-Authors: Dequn Zhou, Pei Zhou, Ling ZhangAbstract:This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum Reserve before 2020. The optimal Oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of Oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by Oil price and total inventory costs. While Oil supply disruption is not considered, China's optimal stockpile acquisition rate increases from 19.2 to 52million barrels from 2008 to 2020. If an Oil supply disruption occurs, the Oil acquisition rate will be reduced significantly. However, it may not be a good strategy to interrupt Oil Reserve activities in order to minimize the total costs for the entire planning period.
Ling Zhang - One of the best experts on this subject based on the ideXlab platform.
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optimal path for china s strategic petroleum Reserve a dynamic programming analysis
Energy Economics, 2012Co-Authors: Dequn Zhou, Pei Zhou, Ling ZhangAbstract:This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum Reserve before 2020. The optimal Oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of Oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by Oil price and total inventory costs. While Oil supply disruption is not considered, China's optimal stockpile acquisition rate increases from 19.2 to 52million barrels from 2008 to 2020. If an Oil supply disruption occurs, the Oil acquisition rate will be reduced significantly. However, it may not be a good strategy to interrupt Oil Reserve activities in order to minimize the total costs for the entire planning period.
Pei Zhou - One of the best experts on this subject based on the ideXlab platform.
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stockpile strategy for china s emergency Oil Reserve a dynamic programming approach
Energy Policy, 2014Co-Authors: Y Bai, Carol Dahl, Dequn Zhou, Pei ZhouAbstract:Abstract China is currently accelerating construction of its strategic petroleum Reserves. How should China fill the SPR in a cost-effective manner in the short-run? How might this affect world Oil prices? Using a dynamic programming model to answer these questions, the objective of this paper is to minimize the stockpiling costs, including consumer surplus as well as crude acquisition and holding costs. The crude Oil acquisition price in the model is determined by global equilibrium between supply and demand. Demand, in turn, depends on world market conditions including China׳s stockpile filling rate. Our empirical study under different market conditions shows that China׳s optimal stockpile acquisition rate varies from 9 to 19 million barrels per month, and the optimal stockpiling drives up the world Oil price by 3–7%. The endogenous price increase accounts for 52% of total stockpiling costs in the base case. When the market is tighter or the demand function is more inelastic, the stockpiling affects the market more significantly and pushes prices even higher. Alternatively, in a disruption, drawdown from the stockpile can effectively dampen soaring prices, though the shortage is likely to leave the price higher than before the disruption.
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optimal path for china s strategic petroleum Reserve a dynamic programming analysis
Energy Economics, 2012Co-Authors: Dequn Zhou, Pei Zhou, Ling ZhangAbstract:This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum Reserve before 2020. The optimal Oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of Oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by Oil price and total inventory costs. While Oil supply disruption is not considered, China's optimal stockpile acquisition rate increases from 19.2 to 52million barrels from 2008 to 2020. If an Oil supply disruption occurs, the Oil acquisition rate will be reduced significantly. However, it may not be a good strategy to interrupt Oil Reserve activities in order to minimize the total costs for the entire planning period.
S. H. Ayatollahi - One of the best experts on this subject based on the ideXlab platform.
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wettability alteration of carbonate rocks by surfactants a mechanistic study
Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2012Co-Authors: Kh Jarrahian, Omolbanin Seiedi, Vafaie M Sefti, Mehdi Sheykhan, S. H. AyatollahiAbstract:A considerable quantity of the world's Oil Reserve is located in naturally fractured carbonate reservoirs, with very low Oil recovery efficiency, due to their wettability and tightness of matrixes. Recovery efficiency can be improved considerably, if the reservoir rock wettability is changed from mostly Oil-wet to water-wet, thus enhancing water imbibition into the Oil saturated rock. In this experimental work, an extensive mechanistic study is performed utilizing different analytical tools to study the effects of surfactants on the sample rock's wettability. The results indicate that the surfactants act in different manners according to their structure. Cationic surfactant C12TAB, tends to irreversibly desorb stearic acid from the dolomite surface via ionic interaction. Nonionic surfactant TritonX-100 is adsorbed on the surface by the polarization of π electrons and ion exchange, releasing more stearic acid from the solid surface. The released stearic acid is then adsorbed as a new layer on the surface, through hydrophobic interaction between the tail of adsorbed surfactants and the non-polar part of the stearic acid. Anionic surfactants, such as SDS, are adsorbed on the surface via hydrophobic interaction between the tail of surfactant and the adsorbed acid, thus changing the wettability of the surface to a neutral wet condition.
