The year 2008 was the historical and memorable year from the view of crude oil prices. Skyrocketing oil prices resulted from the following reasons. The first reason is the rapid economic growth in BRICs such as China and India. The second reason is rebirth of nationalism in major oil producing countries. The third reason is financial capitalism of crude oil future market. Now, the major players in NYMEX (New York Mercantile Exchange) are financial institutions, hedge funds and commodity index funds. The movement of crude oil prices depends on sentimental factors such as crude oil stock level and nuclear weapon development in Iran. On July 11 2008, crude oil prices reached recorded 147.27 dollars per barrel. And then, crude oil prices fell rapidly to 30 dollars per barrel after the bankrupt of the famous investment bank. I think neither 150 dollars per barrel nor 30 dollars per barrel are adequate prices which reflected the fundamental of world demand and supply of crude oil. The excessive fluctuation of crude oil prices gives the great damage to all consumers and companies. The future of world economy depends upon the success of green new deal policy of the United States.
Through the 4 years technical examination which consist of construction or utilization of facilities to correspond with Japanese regulation, logistics, rig modification, crew training, technical consideration of both mud cooling and well control, JAPEX were able to introduce first SBM in Japan in 2006 for Akebono SK-4D well. In the course of utilization of SBM in Akebono well, JAPEX demonstrated a total qualified engineering and operational capabilities. Due to lost return problem in Akebono well, hence actual operational days had to be lengthened in spite of being given a good hole condition compared with the past WBM experience. Since then, SBM were to be utilized for next two wells, Numanohata SK-7D and 8D, and JAPEX found several outcomes and have been still trying to solve some of concerns such as cutting handling and SBM storage in order to be further proficient in utilizing SBM and establish or prove its profits for future operation.
Deepwater drilling technology study group in the Drilling Technology Committee of JAPT is participated by a number of members and positively performing its activities including information research, internal studies and publicity of those study results via JAPT web-site. In this paper we will introduce the basics of deepwater drilling technologies and the recent topics among the deepwater drilling technologies including the Deepwater drilling technology study group's achievement.
LNG-FPSO (Floating Production Storage and Offloading Unit) is a floating system installed LNG plant on it and enables to export natural gas as LNG from offshore field to gas markets directly. This system may have significant advantages for development of deepwater, remote or small size offshore gas field for the following reasons: · Minimum Environmental Impact · Reduction in CAPEX/OPEX · Potential Reduction of Project Lead Time · Reduction in Abandonment Work & Cost INPEX Corporation (INPEX) has conducted several studies on LNG-FPSO to assess the feasibility since 2001. Based on the results, INPEX conducted Pre-FEED (Front End Engineering & Design) for LNG-FPSO for the INPEX's Abadi gas field located in the deepwater remote area in Arafura Sea Offshore Indonesia from September 2007 to July 2008. This presentation outlines the concept of LNG-FPSO for the Abadi gas field and discusses the applicability of LNG-FPSO.
Depressurization method is regarded as a promising gas production technique from methane hydrate reservoirs. There are three major factors that determine gas production rate by depressurization : kinetics of hydrate dissociation, gas flow through the reservoir, and heat transfer to the dissociating zone. As the gas productivity of methane hydrate wells significantly changes depending on the factors that determine the rate, it is important to find the most governing factor. To find such rate-determining factors, we developed a new method by introducing formulations of three potential methane fluxes generated by kinetics of hydrate dissociation, gas flow and heat transfer. The proposed method was applied to the analysis of hydrate dissociation and gas production behaviors in laboratory scale depressurization experiments on artificial methane hydrate cores. From the calculation and comparison of those rate-determining factors, we concluded that gas productions in those experiment cases were mainly limited by heat transfer. Hydrate dissociation rates during the experiments were well matched with the methane fluxes calculated by heat transfer, which increased in proportion to the heat flux into the core. According to the core experiment results, gas production rate from depressurization of high-permeability hydrate cores is probably limited by heat transfer. Future application of this method to the analysis for hydrate reservoir performance should give us a clue to finding a best production method.
This paper investigates the applicability of Artificial Neural Networks (ANN) in the optimization process of lifting rate allocation for ESP wells. In the optimization, oil recovery is maximized according to the production performance predicted using a reservoir simulation of a real oil field. Lufeng 13-1 Oil Field in the South China Sea has strong natural bottom-water drive and its development has reached a mature production stage. All production wells are currently operated with Electric Submersible Pumps (ESP) and the averaged water cut has exceeded 90%. The limited handling capacity of the surface processing facilities cannot allow all the wells to lift liquid at their ESPs' maximum rate. The lifting rate allocation to the wells, therefore, needs to be optimized for maximizing the oil recovery in a certain production period. Generally, a large number of reservoir simulation runs are required for solving this type of optimization problems since all possible cases of lifting rate allocations need to be examined. In this study, oil recovery volume was estimated with a trained ANN on the basis of allocated liquid rates at the wells as the initial screening. The ANN was constructed with back-propagation method treating the results of 100 simulation results as the training data sets. Two patterns of training data sets were examined; one with random rate allocation and the other with systematic rate allocation. Blind tests for the estimation accuracy presented that the ANN trained with the systematic data sets showed better results than that with the random ones. Oil recovery factors under all the possible cases with different liquid rate combinations were estimated using the trained ANN. The top fifty cases were selected for the final examination by numerical simulation. The best case yielded 5.5% increase of produced oil volume from the base case, in which lifting rates were equally reduced to 89% of the maximum lifting capacities to meet the facility capacity. The investigation results demonstrated that the efficiency of the optimization was remarkably improved with the use of ANN on the determination of optimum liquid lifting rates in terms of oil recovery.