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Well logging graphic of CCSD-MH lithology analysis in the depth Sect. 1700.0 ~ 1750.0 m. 1 Rutile eclogite, 2 phengite eclogite, 3 retrograded eclogite, 4 paragneiss, 5 orthogneiss, 6 amphibolites, 7 serpentinite, 8 chlorite amphibolites, 9 moyite and 10 fracture zone
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Lithology is one of the most important data in evaluating reservoir, and is mainly carried out by cores recovery in laboratory which is very expensive, and its interpretation is time consuming. Accurate identification of lithology is fundamentally crucial to evaluate reservoir from geophysical log data. Pattern recognition and statistical analysis...
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Citations
... In order to identify the lithology of all the formations which are demarcated by the performance of wavelet transform, a fast and practical K-means clustering algorithm (Yang et al., 2015) is employed to make a classification of these formations into 5 groups, which are orthogneiss, amphibolites, eclogite, paragneiss, ultramafic rocks (Zhang et al. 2000(Zhang et al. , 2003. ...
... Based on the traditional K-means clustering algorithm, Euclidean distance was replaced by Mahalanobis distance, and the initial cluster centers were acquired from the average of characteristic values, in addition, added weight value in each characteristic value of the objective function. After that, a lithology recognition model named modified K-means clustering is established (Yang et al., 2015). The objective function of modified K-means cluster algorithm can be defined as: Where w j ( = ⋅⋅⋅ j n 1, 2, , ) is the weight of each characteristic value, Σ is the covariance matrix, ...
... = [ ] X x k i j , ( = ⋅⋅⋅ i m 1, 2, , ; = ⋅⋅⋅ j n 1, 2, , ) are the indeterminate samples. The detail calculation of the parameters are seen Yang et al. (2015). ...
Lithology identification is an important part of reservoir evaluation and reservoir description when processing and interpreting geophysical record data. Clustering analysis refers to the analysis process of grouping a collection of physical or abstract objects into several classes composed of similar objects. K-means clustering algorithm is an iterative clustering analysis algorithm. In this paper, seven mechanical property parameters of 49 rock samples are selected as experimental data in an engineering survey, and the geophysical logging method of K-means dynamic clustering analysis is adopted. The rock samples are divided into three categories, and the classification results are matched by mechanical property parameter method. By changing the order of data grouping, the misjudgment rates were 0.021, 0.021 and 0.102, respectively. Therefore, it is feasible and effective to use k-means dynamic clustering analysis to classify lithology. The number of samples decreased to 15, and the misjudgment rate increased to 0.267 The results of K-means dynamic clustering analysis may be different from the actual situation of rock sample data selection.
The reservoir rocks in the volcanic weathered crust are characterized by plentiful dissolution pores, caverns and fractures. However, as an essential control factor of the reservoir distribution, volcanic dissolution is often ignored in quantitative studies. In this paper, we propose a complete artificial intelligence workflow to model and predict the volcanic dissolution distribution by integrating the image logging semi-quantitative analysis with seismic prediction. A synthetic reservoir property named DCV (Dissolution Comprehensive Values) is generated as logging curves from XRMI resistivity pseudo-pictures to indicate the intensity of dissolution. These curves are then used as the target for the seismic-based prediction in an artificial intelligence framework. A multi-layer artificial neural network (ANN) model is constructed in order to map the seismic attributes into the DCV. The methodology is demonstrated on a real case study from the Junggar basin in northwestern China. The maps and profiles from the prediction represent the dissolution values in the volcanic weathered crust. The predictions are consistent with the geological volcanic dissolution model provided by geological knowledge. It is concluded that the spatial dissolution distribution in volcanic weathered crust can be reliably predicted by this integrated method.
The manipulation of underwater bubbles is of great importance in both scientific research and industrial applications. In this work, the motion of underwater bubbles on a microholed polydimethylsiloxane (PDMS) surface with gradient wettability is studied by a high-speed camera. It was found that underwater bubbles self-transported directionally from the smaller area fraction (SAF) of the surface to the larger area fraction (LAF). Besides, the bubble motion was triggered by an effective depth range from hcr,min to hcr,max. Only the depth of bubble was within the range, would the self-transport motion occur. Otherwise the bubble would adhere on the surface eventually. The main cause for the motion is the trapped air inside the microholes which generates the torque Tb and the retention force Fr driving the bubble directionally. The mathematical model is established to reveal the motion mechanism, which is verified by the experimental results. The outcome of our work shed new light on the target transportation fields like drug delivery and submarine gas collection.
KNN (K-Nearest Neighbors) clustering in machine learning is a very efficient clustering method applied in lithology identification, reservoir type recognition, flow unit classification and so on, but it is not working consistently because of its inherent limitation, like its initial center selection and clustering center shift caused by outliers. Moreover, KNN clustering is based on a mathematical algorithm, and does not necessarily cluster the space due to the geological and petrophysical aspects. To address this issue, an optimized KNN clustering method based on weighed cosine distance is proposed to better fit the lithology model. To deal with the problem of the initial center selection and outliers, a set of statistic methods is conducted. A set of data, sampled by different logging series, is made use to feature information about lithology as much as possible. New distance algorithm selection based on geology model and well logging data similarity attribute is obtained when utilizing. Taking the Mesozoic strata in Gaoqing field as example, a procedure fitter for utilization of machine learning technique in lithology identification is carried on, and the overall accurate rate of the optimized K-means clustering in lithology identification raised nearly 10% compared with the traditional K-means clustering especially in the differentiation between fine-sand and coarse-grained clastic sand. The predicted lithology match the core plugs better than the traditional KNN clustering.
A large inherited paleouplift from Pre-Sinian to Early Cambrian, named Xuanhan-Kaijiang paleouplift, has been discovered based on multiple geological information in the eastern Sichuan Basin: firstly, onlap deposition of Upper Sinian Dengying Formation and Lower Cambrian is observed from peripheral zone to central Xuanhan-Kaijiang area from the seismic profile; secondly, Dengying Formation thickness distribution map shows that this formation is thinnest in Xuanhan-Kaijiang area, and thickens towards the surrounding areas; thirdly, terrigenous clastic rock is observed in Upper Sinian Dengying Formation on outcrops in Chengkou County and Wuxi County etc., indicating the development of a paleouplift. The analysis of the characteristics, formation and evolution of the paleouplift reveals that the plane distribution of the paleouplift shows an arched structure, stretching in a north-south trend. The area of the paleouplift is around 16 000 km². Vertically, the paleouplift can be divided into three parts: platform in the middle, steep slope in the west and ramp in the east. Originally, the paleouplift was an erosion-type uplift above the water level during the early stage of the Late Sinian; and gradually transformed into deposition-type uplift under water level during the late stage of the Late Sinian to the Early Cambrian. The formation of the paleouplift was controlled by several factors, including basement uplift, intracratonic rift, basement faults and regional uplifting. The paleouplift and its slope areas, favorable for the development of high energy facies and karst weathering reservoirs, are important exploration target areas in the eastern Sichuan Basin in the near future. © 2016 Research Institute of Petroleum Exploration & Development, PetroChina
