Quantitative identification and prediction of mixed lithology, Bohai Sea, China

The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

Quantitative identification of relatively highlevel radioactive sandstone in the Upper Triassic Yanchang Formation of the Ordos Basin, China

Most relatively high-level radioactive sandstone(HRSS)reservoir has considerable oil(or gas)resource potential.HRSS is often wrongly identified due to its similar logging response characteristics as mudstone,which leads to the omission of effective reservoirs.In this paper,a quantitative identification method for HRSS is proposed after the analyzing of the response characteristics and relationship between spontaneous potential log and natural gamma-ray log in conventional sandstone and mudstone strata.Take the Upper Triassic Yanchang Formation in Ordos Basin as an example:the responses of spontaneous potential log and the responses of natural gamma-ray log are synchronized and positively correlated in conventional sandstone and mudstone strata,but they are not synchronized in HRSS.Quantitative identification of HRSS was realized based on this synchronization feature,and a"virtual compensation"of natural gamma-ray log was performed.At the same time,logging evaluation method about HRSS has been discussed.The final results shows that this identification method work effectively,and can reduce the misjudgment and omission of effective reservoirs.

Quantitative identification of coal-type gas and oil-type gas in source-mixed gas at the northern margin of Qaidam Basin

The current study tested the gas component and carbon isotopic composition of gas samples from 6 oilgas fields at the northern margin of Qaidam Basin, and established a chart to quantitatively identify the mixing ratio of source-mixed gas. Besides, this research quantitatively investigated the natural gas generated by different types of organic matter. The results show that different ratios of source-mixed gas exist in the 6 oil-gas fields at the northern margin of Qaidam Basin. Among them, Mabei has the highest mixing ratio of coal-type gas, followed by Nanbaxian, Mahai, Lenghu-4, Lenghu-3 and Lenghu-5, with the ratios of coal-type gas 91%, 87%, 83%, 66%, 55% and 36%, respectively. Lenghu-3 and Lenghu-4 oil-gas fields were mainly filled by coal-type gas earlier. For Lenghu-3, the gas was mainly generated from low matured source rocks in lower Jurassic Series of Lengxi sub-sag. For Lenghu-4, the gas was mainly generated from humus-mature source rocks in lower Jurassic Series of the northern slope of Kunteyi sub-sag. Gas in Lenghu-5 was mainly later filled oil-type gas, which was generated from high matured sapropelics in lower Jurassic Series of Kunteyi sub-sag. Earlier filled coal-type gas was the main part of Mahai, Nanbaxian and Mabei oil-gas fields. Gas source of Mahai was mainly generated from high mature humics in lower Jurassic Series of Yibei sub-sag; for Nanbaxian, the gas was mainly generated from high matured humics in middle-lower Jurassic Series of Saishiteng sub-sag; for Mabei, the gas was mainly generated from humus-mature source rocks in middle Jurassic Series of Yuqia sub-sag.

Artificial Neural Network Method Based on Expert Knowledge and Its Application to Quantitative Identification of Potential Seismic Sources

In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).

A new spectral index for the quantitative identification of yellow rust using fungal spore information

Yellow rust(Puccinia striiformis f.sp.Tritici)is a frequently occurring fungal disease of winter wheat(Triticum aestivum L.).During yellow rust infestation,fungal spores appear on the surface of the leaves as yellow and narrow stripes parallel to the leaf veins.We analyzed the effect of the fungal spores on the spectra of the diseased leaves to find a band sensitive to yellow rust and established a new vegetation index called the yellow rust spore index(YRSI).The estimation accuracy and stability were evaluated using two years of leaf spectral data,and the results were compared with eight indices commonly used for yellow rust detection.The results showed that the use of the YRSI ranked first for estimating the disease ratio for the 2017 spectral data(R^(2)=0.710,RMSE=0.097)and outperformed the published indices(R^(2)=0.587,RMSE=0.120)for the validation using the 2002 spectral data.The random forest(RF),k-nearest neighbor(KNN),and support vector machine(SVM)algorithms were used to test the discrimination ability of the YRSI and the eight commonly used indices using a mixed dataset of yellow-rust-infested,healthy,and aphid–infested wheat spectral data.The YRSI provided the best performance.

Research on Identification Method of Argillaceous Interlayer in Compound Sand

For the compound sand body, the interlayer is an important factor affecting the adjustment of oil production structure and remaining oil distribution. According to the origin of argillaceous interlayer, the interlayer is divided into three types, including barriers between two single layers, intercalations between two single sands and intercalations in a single sand. In this study, the upper limit of physical properties of interlayer was obtained by analyzing the relationship between physical parameters and production index per-meter. The discriminant index and comprehensive discriminant chart of interlayer were obtained by grey correlation method, which realize the quantitative identification of different types of interlayer. The intercalations between two single sands in the target area are distributed almost in the whole area, which is one of the most important factors influencing the mining effect of compound sand, so the planar distribution is mainly aimed at it. Firstly, through cross-well comparison, we summarize three interlayer patterns, then establish their forward modeling, so as to obtain the vertical seismic characteristics of different patterns. Secondly, according to the thickness of intercalations between two single sands, we take the top of bottom sand as the baseline, extract the average amplitude attribute from the upper and lower 3 ms, then, according to the seismic section and planar characteristics of the well, the interlayer structures represented by different seismic section and planar characteristics are summarized. Finally, starting from the real drilling interlayer of the well, the planar spread of interlay can be obtained according to their variation trend and distribution.

Identification of Quantitative Trait Loci Controlling Seed Physical and Nutrient Traits in Cotton

Cotton(Gossypium spp.) is the leading fiber crop,and an important source of the important edible oil and protein meals in the world.Complex genetics and strong environmental effects hinder

Analysis of Channeling-Path Phenomena in a Complex Fault-Block Reservoir with Low Recovery Factor and High Water-Cut Ratio

Current methods for the analysis of channeling-path phenomena in reservoirs cannot account for the influence of time and space on the actual seepage behavior.In the present study,this problem is addressed considering actual production data and dynamic characteristic parameters quantitatively determined in the near wellbore area by fitting the water-cut curve of the well.Starting from the dynamic relationship between injection and production data,the average permeability is determined and used to obtain a real-time quantitative characterization of the seepage behavior of the channeling-path in the far wellbore area.For the considered case study(Jidong oilfield),it is found that the seepage capacity of the channeling-path in the far wellbore area is far less(10 times smaller)than that of the channeling-path in the near wellbore area.The present study and the proposed model(combining near wellbore area and far wellbore area real-time data)have been implemented to support the definition of relevant adjustment measures to ultimately improve oil recovery.