Zhan Shifan

Reservoir property prediction using the dynamic radial basis function network

Summary The nonlinear relationship between seismic attributes and reservoir property can be estimated by many statistical methods, such as machine learning and neural networks. However, an adaptive process for parameters learning cannot be implemented in most statistical methods. In this paper, we propose a dynamic radial basis function (D-RBF) network method to predict the reservoir property from seismic attributes. The main feature of this method is that the nonlinear relationship between the reservoir property and seismic attributes can be determined adaptively without interpreter intervention. In fact, if the current RBF network cannot reduce the prediction errors in the system, the proposed method will increase or decrease the hidden units in the RBF network to re-estimate the relationship between the reservoir property and the seismic attributes. We illustrate the proposed method using two real 3D seismic data sets. The results show that models based on D-RBF networks can predict reservoir property with high accuracy.

Detecting carbonate‐karst reservoirs using the directional amplitude gradient difference technique

This kind of carbonate reservoirs is abundant, they consist mainly of tectonic fractures, diagenetic fractures, stylolite, matrix porosity, dissolution porosity, and karst caves. Among them, the highly heterogeneous karst cave is the main reservoir space for hydrocarbon accumulation. In seismic section, it exhibits a strong heterogeneity no matter horizontally or vertically and shows as “a string of beads” (Figure 1). Moreover, unlike carbonate reservoirs in other areas, Ordovician carbonate-karst reservoirs in the Tarim Basin are deeply buried, and their depth can reach 6 to 7 km.

Converted Wave Seismic Exploration & Static Correction

More and more attentions have been paid to the applications of converted wave exploration in assessing oil and gas reservoirs. Experimental study has also been fulfilled in China for over 10 years. Due to the difficulties in acquisition, statics, data processing and interpretation, the technology has been in a slow progress. In the past few years, a series of studies have been fulfilled aiming to the technologies of converted wave acquisition, static correction and processing in Sulige Gas Field, Erdos Basin. The converted wave section processed is in a high quality; and the interpretation result correlates with the drilling result wonderfully

Joint poststack P- and PS-wave impedance inversion and an example from northern China

The main structure of the SLG gas field, in Ordos Basin in northern China, is a mild monocline with a dip angle of less than 1°. Tectonic movement is minimal with some nose-like structures having relief of less than 20 m (Figure 1).

3C/3D Seismic Exploration Technology And Application Results

Multicomponent seismic survey can provide a variety of information for development of reservoirs, reduce the multiresolution of predicting reservoirs and increase the successful drilling rate. In the past two years, BGP has imported digital seismograph and geophones to research the techniques in 3C/3D seismic data acquisition, processing and interpretation, design & demonstration of 3D converted-wave geometry, multiwave uphole survey and surface survey & 3D converted wave static corrections, rotation of horizontal component data & wave field separation, computing 3D common converted bins of converted wave, 3D converted wave residual static correctoins, CCP & iterative velocity analysis, converted wave prestack time migration by Kirchhoff integration, horizon labeling of converted wave & P-SV wave and Pwave joint interpretation, using converted wave to analyze physical property of reservoir and predict the lithology & oil/gas reservoir etc., forming a complete set of 3C/3D seismic technology that has seen a visible effect in application to Sulig Gas Field. Using the 3-componet data to predict gas-bearing character of reservoir made the 4 exploration drillings presented total success. The matching ration of the survey grid with the drilling completion is over 80%.

Automatic Geological Body Identification Using the Modified Rival Penalized Competitive Learning Clustering Algorithm

In the past few years, the use of multi-attribute seismic volume classification has been widely studied in reservoir analysis and interpretation workflows to identify abnormal geologic characteristics. Generally speaking, the identifycation process can be very difficult and time-consuming. In this paper, we present an unsupervised approach based on the modified rival penalized competitive learning (MRPCL) theory to identify 3D geologic characteristics automatically. In the proposed algorithm, a new cost function and some parameter learning methods will be introduced to operate the identification of geologic characteristics effectively and to determine the number of seismic facies automatically. By clustering the multiattribute seismic data, different geologic bodies can be extracted directly from the 3D seismic data volume. Finally, this proposed approach is demonstrated on a simple 2D synthetic data set and on two real 3D seismic data sets.