In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experimen...In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.展开更多
The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distributi...The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.展开更多
This paper is mainly about the calculation of reservoir parameters and theinterpretation method for identifying oil/water beds in Ke82 well areas of Junggar basin. It isdifficult to determine the reservoir parameters ...This paper is mainly about the calculation of reservoir parameters and theinterpretation method for identifying oil/water beds in Ke82 well areas of Junggar basin. It isdifficult to determine the reservoir parameters with common logging methods such as core calibrationlog because of the diversity of minerals and rocks and the complexity of pore structures in theconglomerate reservoir of Junggar basin. Optimization logging exploration is a good method todetermine the porosity by establishing the multi-mineral model with logging curves based on theintegration of geological, core and well testing data. Permeability is identified by BP algorithm ofneural network. Hydrocarbon saturation is determined by correlating Archie's and Simandouxformulas. Comparing the exploratory result and core data, we can see that these methods areeffective for conglomerate logging exploration. We processed and explained six wells in the Ke82well areas. And actual interpretation has had very good results, 85 % of which conform to welltesting data. Therefore, this technique will be effective for identifying conglomerate parameters.展开更多
文摘In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.
基金sponsored by the National Natural Science Foundation of China(42072187,42090025)CNPC Key Project of Science and Technology(2021DQ0405)。
文摘The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.
文摘This paper is mainly about the calculation of reservoir parameters and theinterpretation method for identifying oil/water beds in Ke82 well areas of Junggar basin. It isdifficult to determine the reservoir parameters with common logging methods such as core calibrationlog because of the diversity of minerals and rocks and the complexity of pore structures in theconglomerate reservoir of Junggar basin. Optimization logging exploration is a good method todetermine the porosity by establishing the multi-mineral model with logging curves based on theintegration of geological, core and well testing data. Permeability is identified by BP algorithm ofneural network. Hydrocarbon saturation is determined by correlating Archie's and Simandouxformulas. Comparing the exploratory result and core data, we can see that these methods areeffective for conglomerate logging exploration. We processed and explained six wells in the Ke82well areas. And actual interpretation has had very good results, 85 % of which conform to welltesting data. Therefore, this technique will be effective for identifying conglomerate parameters.
