Rock Physics Guided Data Science Technique to Improve Neural Network Training to Accurately Quantify Rock Properties from Seismic Data

Abstract

Abstract Historically, machine learning techniques and particularly neural networks have been applied in the domain of Geophysics to discern rock properties through a quantitative approach from seismic data and associated seismic attributes. However, the success or failure of neural network is dependent on amount of available data. In general, more labelled data during training of the neural network results in accurate predictions. Therefore, seismic based rock property prediction models and their calibration needs to be fed with labelled data coming from well logs in adequate amount. The fact that the number of wells are often limited can be a huge challenge in adopting neural networks predictions. Downton and Hampson (2019) developed a Hybrid Theory-guided Data Science (TGDS) technique to improve the training of the network by augmenting the amount of data used for training. This technique aims at overcoming the issue of scarcity of the well log data. By using the outputs of theory based components like Rock Physics theory as input to the data science component of augmenting the amount of the data. In this method, Rock Physics is applied to model the elastic response due to the changes in rock and fluid properties of the available well control to create a huge dataset of pseudo wells. Then, synthetic seismic gathers are modeled at the pseudo well locations to train a Deep Feed-Forwards Neural Network (DFNN). The parameters estimated during DFNN training with synthetic seismic gathers are then applied on a real seismic dataset. The final predicted P-impedance from this formerly described method was compared to conventional inverted Pre-Stack Elastic Inversion. This paper shows the application of the method of TGDS based synthetic seismic catalog generation followed by DFNN applied on real seismic dataset for a commercially oil producing field in North Sea. The reservoirs are injectite sands which are difficult to image but can be key pay zones with quite high porosity and permeability. Well log curves e.g. P-Impedance, Porosity and Water Saturation serve as guidelines to create the synthetic seismic data. Additionally, with the added advantage of increased synthetic data, pseudo wells were broken into training and validation datasets. So, the DFNN training is carried out on a portion of the training data subset. During the process of training the DFNN, the seismic data remains blind. The estimated DFNN non-linear operator is applied to the seismic data, however, it needs to be scaled to the real data as the training was performed on the synthetic seismic data. The final result of the data science and machine learning approach driven derived P-impedance showed higher frequency and better lateral continuity than the conventional theory based approach of Pre-Stack Elastic Inversion. Augmenting the amount of training data by generating synthetic data based on the statistics of from nearby well control and rock physics relationships is prime differentiator of this workflow. By using rock physics theory a large number of pseudo wells can be generated that gives a range of expected geologic scenarios. Key wells from nearby fields, that do not necessarily tie the seismic under consideration, can be incorporated into the analysis and also, if required additional wells could be incorporated into the analysis as drilled.