利用成像测井自动判别礁滩储层沉积相和岩性

礁滩储层是我国海相碳酸盐岩油气勘探的重要目标,也是我国油气产能的重要接替领域之一。由于礁滩储层储集空间类型多样,非均质性极强,依靠常规测井判别其沉积相和岩性非常困难。成像测井能够清晰地反映礁滩储层的结构组分和沉积构造,为沉积相和岩性的判别提供了可靠的依据。在对大量礁滩储层的成像和岩心对比观察的基础上,我们提出了9种典型的成像解释模式,建立了利用成像解释模式自动判别礁滩储层沉积相和岩性的方法并研制了相应的处理软件。该方法在塔中和川东北地区礁滩储层的实际应用中取得了良好的效果。     

基于QAPM矿物模型遗传算法评价火成岩储层

在松辽盆地深层发现了含气火成岩储层。由于火成岩矿物组成复杂和含量的变化,使得选择用于测井评价的解释参数很困难。基于IUGS提出的QAPF分类方案,本文提出了采用遗传算法,利用测井数据确定火成岩矿物含量的方法。根据QAPF分类方案,将火成岩中的矿物分为五类:Q-石英;A-碱性长石;P-斜长石和方柱石;F-副长石(研究区未出现);M-铁镁矿物。本文提出用包括孔隙度在内的QAPM模型对储层进行分析。建立密度、视中子孔隙度、声波时差、自然伽玛和体积光电吸收截面指数的测井响应方程,各矿物参数从斯伦贝谢的矿物参数手册中得到。用遗传算法计算骨架中四种矿物的体积,根据四种矿物的体积含量,依据QAPF分类对火成岩命名。基于解释参数计算的孔隙度可与岩心分析的孔隙度相比,本文给出的火成岩命名与岩心化学分析的命名相一致。     

Evaluation of volcanic reservoirs with the “QAPM mineral model” using a genetic algorithm

Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups： Q（quartz）, A （Alkaline feldspar）, P （plagioclase）, M （mafic） and F （feldspathoid）. We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm （GA）. The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples.     

Fluid Property Discrimination in Dolostone Reservoirs Using Well Logs

The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study describes the litholog of Ma 5(Member 5 of Majiagou Formation)dolostones,and then analyzes the responses of various conventional well logs to the presences of natural gas.The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite.Natura...     

In-situ gas hydrate hydrate saturation estimated from various well logs at the Mount Elbert Gas Hydrate Stratigraphic Test Well, Alaska North Slope

In 2006, the U.S. Geological Survey (USGS) completed detailed analysis and interpretation of available 2-D and 3-D seismic data and proposed a viable method for identifying sub-permafrost gas hydrate prospects within the gas hydrate stability zone in the Milne Point area of northern Alaska. To validate the predictions of the USGS and to acquire critical reservoir data needed to develop a long-term production testing program, a well was drilled at the Mount Elbert prospect in February, 2007. Numerous well log data and cores were acquired to estimate in-situ gas hydrate saturations and reservoir properties.Gas hydrate saturations were estimated from various well logs such as nuclear magnetic resonance (NMR), P- and S-wave velocity, and electrical resistivity logs along with pore-water salinity. Gas hydrate saturations from the NMR log agree well with those estimated from P- and S-wave velocity data. Because of the low salinity of the connate water and the low formation temperature, the resistivity of connate water is comparable to that of shale. Therefore, the effect of clay should be accounted for to accurately estimate gas hydrate saturations from the resistivity data. Two highly gas hydrate-saturated intervals are identified - an upper ∼43 ft zone with an average gas hydrate saturation of 54% and a lower ∼53 ft zone with an average gas hydrate saturation of 50%; both zones reach a maximum of about 75% saturation.     

Identification of water-bearing fractures in hard rock terrain by electrical conductivity logs, India

For the delineation of water-bearing fractures in hard rock areas, a new hydro-chemical technique has been developed which is based on electrical conductivity (EC) logs. The EC logs were carried out in experimental shallow bore wells (≈50 m) in three different parts of India. A sharp variation in EC was observed near water-bearing fractures in hard rock areas. To access applications of this technique, different locations in India were selected and experimental bore wells (≈50 m) were drilled. These were:

Estimation of Geological Attributes from a Well Log: An Application of Hidden Markov Chains

Geophysical well logs used in petroleum exploration consist of measurements of physical properties (such as radioactivity, density, and acoustic velocity) that are digitally recorded at a fixed interval (typically half a foot) along the length of the exploratory well. The measurements are informative of the unobserved rock type alternations along the well, which is critical for the assessment of petroleum reservoirs. The well log data that are analyzed here are from a North Sea petroleum reservoir where two distinct strata have been identified from large scale seismic data. We apply a hidden Markov chain model to infer properties of the rock type alternations, separately for each stratum. The hidden Markov chain uses Dirichlet prior distributions for the Markov transition probabilities between rock types. The well log measurements, conditional on the unobserved rock types, are modeled using Gaussian distributions. Our analysis provides likelihood estimates of the parameters of the Dirichlet prior and the parameters of the measurement model. For fixed values of the parameter estimates we calculate the posterior distributions for the rock type transition probabilities, given the well log measurement data. We then propagate the model parameter uncertainty into the posterior distributions using resampling from the maximum likelihood model. The resulting distributions can be used to characterize the two reservoir strata and possible differences between them. We believe that our approach to modeling and analysis is novel and well suited to the problem. Our approach has elements in common with empirical Bayes methods in that unspecified parameters are estimated using marginal likelihoods. Additionally, we propagate the parameter uncertainty into the final posterior distributions.     

Investigation on influence factors of dual laterologs curve form

In order to investigate the influences of caliper, formation thickness and invaded zone on the form of dual laterologs, forward modeling technique were applied to calculate the dual laterologs for different cases. The result shows that the resistivity logs become smoother and lower as the borehole diameter increases, the increase of the contrast between mud resistivity and formation resistivity induce the logs to be more pointed. When the formation thickness is less than lm, the two-peak on the logs for resistive invasion vanished, and for thickness between 1 m and 4 m, the form of logs does not vary significantly. If the formation thickness is greater than 4 m, a platform appears on the logs at the middle of the formation. The thinner the invaded zone is, the more obvious the invasion feature on the laterologs is. For thick invaded zone the form of logs tend to be that of an uninvaded resistive formation. The form and amplitude of logs depend on the resistivity contrast between invaded zone, uninvaded formation and adjacentlayers.     

A randomization test for the comparison of ordered sequences

Several algorithms, based on dynamic programming techniques, for comparing, matching, or cross-correlating two ordered sequences of observations now exist. All such algorithms produce one or more optimal matchings, no matter what data are used. This paper presents an empirical method, based on a randomization test, for assessing how well the given sequences are matched or slotted together. The randomization test is designed to test the assumption, implicit in most cross-correlation algorithms, that both sequences comprise observations, at unknown but ordered times, from the same underlying yet unknown function of time. The test is intuitively appealing, easy to implement, works well on both artificial and real data, and requires no complicated parametric modelling.