A stochastic approach for integrating market and technical uncertainties in economic evaluations of

来源 :Petroleum Science | 被引量 : 0次 | 上传用户:tshy65655
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The paper presents a stochastic and economic analysis for petroleum development under uncertain market and technical environments.Mean-reversion with jumps for price forecasting is used to consider market uncertainty,while various scenarios for the reservoir properties and cost are employed to consider technical uncertainty.Monte Carlo simulation is carried out to obtain the feasible range of net present values and internal rates of return.The influence of stochastic parameters is examined through correlation coefficients. The stochastic approach yields more reliable evaluation and effectively investigates the characteristics of development.The integration of uncertainties and contractual terms results in an irregular tendency in the future cash flow and reveals that a larger reserve does not guarantee a greater profit.The reserve and the well rate affect the economic values whereas the parameters for price prediction don’t.The research confirms the necessity of qualifying uncertainties for realistic decision-making at the initial stage of development. The paper presents a stochastic and economic analysis for petroleum development under uncertain market and technical environments. Man-reversion with jumps for price forecasting is used to consider market uncertainty, while various scenarios for the reservoir properties and costs are employed to consider technical uncertainty. Monte Carlo simulation is carried out to obtain the feasible range of net present values ​​and internal rates of return the influence of stochastic law leads is more reliable evaluation and effective investigates the characteristics of development. The integration of uncertainties and contractual terms results in an irregular tendency in the future cash flow and reveals that a larger reserve does not guarantee a greater profit. the reserve and the well rate affect the economic values ​​while the parameters for price prediction don’t. the research confirms the necessity of qualifying uncertaint ies for realistic decision-making at the initial stage of development.
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