Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data

Abstract Reservoir modelling plays a significant role in the investigation of reservoir heterogeneity related to the distribution of reservoir zones and lateral and vertical variations in geological and petrophysical properties of the reservoirs. Data and information derived from the core, well logs, and seismic could be used for facies and petrophysical modelling of these reservoirs. How to organize the data, how to apply the limited spatial offered by the data, and how to develop a comprehensive reservoir model by using only a few points of data are all different problems when using this data as the input for reservoir modelling. Using the Multi Point Statistics Method (MPS) to build the facies as similarly as possible to the geological feature is one of the best ways for modelling the facies. Therefore, the level of uncertainty could be reduced. However, the training image used to build the facies model must be accurate and precise. It is essential to gain as much geological insight as possible from the well logs, seismic, and core data before translated into the training image as a conceptual model. The data input could be optimized using modern tools and techniques, leading to excellent outcomes. This study discussed and dive into the various ways in which the MPS technique can be improved, such as in terms of grid stability, error reduction, well logs’ correlation to the training image, and minimize the uncertainty. The best and most effective techniques to portray a reservoir with good quality grid without scarifying the detail were determined by using the Representative Elementary Volume (REV) as the key parameter to determine layering and zoning. The result showed less discrepancy between the model and the well logs’ data, more stable grid, and will expect to be impacting the simulation progress by lowering the running time. Proper utilization of training images is also required for the geological feature to accurately record observations. It is shown that using REV for zoning and layering will improve the efficiency and accuracy of the 3D facies model. 

Representative Elementary Volume as Key Parameters to Improve Grid Stability on Multi-Point Statistic Based Reservoir Models

Training images, as an important modeling parameter in the multi-point geostatistics, directly determine the effect of modeling. It’s necessary to evaluate and select the candidate training image before using the multi-point geostatistical modeling. The overall repetition probability is not sufficient to describe the relationship of single data events in the training image. Based on the understanding, a new method was presented in this paper to select the training image. As is shown in the basic idea, the repetition probability distribution of a single data event was used to characterize the type and stationarity of the sedimentary pattern in the training image. The repetition probability mean value and deviation of single data event reflected the stationarity of the geological model of the training image; the rate of data event mismatching reflected the diversity of geological patterns in training images. The selection of optimal training image was achieved by combining the probability of repeated events and the probability of overall repetition of single data events. It’s illustrated in the simulation tests that a good training image has the advantages of high repetition probability compatibility, stable distribution of repeated probability of single data event, low probability mean value, low probability deviation and low rate of mismatching. The method can quickly select the training image and provide the basic guarantee for multi-point geostatistical simulations.

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A New Method to Select Training Images in Multi-Point Geostatistics

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A New Method to Select Training Images in Multi-Point Geostatistics