Search

Seismic inversion, complemented by machine learning algorithms, significantly improves the accuracy and efficiency of subsurface parameter estimation from seismic data. In this comprehensive study, a comparative analysis of machine learning techniques is conducted to predict subsurface parameters within the inter-well region. The objective involves employing three separate machine learning algorithms namely Probabilistic Neural Network (PNN), multilayer feedforward neural network (MLFNN), and Radial Basis Function Neural Network (RBFNN). The study commences by generating synthetic data, which is then subjected to machine learning techniques for inversion into subsurface parameters. The results unveil exceptionally detailed subsurface information across various methods. Subsequently, these algorithms are applied to real data from the Blackfoot field in Canada to predict porosity, density, and P-wave velocity within the inter-well region. The inverted results exhibit a remarkable alignment with well-log parameters, achieving an average correlation of 0.75, 0.77, and 0.86 for MLFNN, RBFNN, and PNN algorithms, respectively. The inverted volumes portray a consistent pattern of impedance variations spanning 7000–18000 m/s*g/cc, porosity ranging from 5 to 20%, and density within the range of 1.9–2.9 g/cc across the region. Importantly, all these methods yield mutually corroborative results, with PNN displaying a slight edge in estimation precision. Additionally, the interpretation of the inverted findings highlights anomalous zones characterized by low impedance, low density, and high porosity, seamlessly aligning with well-log data and being identified as sand channel. This study underscores the potential for seismic inversion, driven by machine learning techniques, to swiftly and cost-effectively determine critical subsurface parameters like acoustic impedance and porosity. [ABSTRACT FROM AUTHOR]

In this study, seismic inversion is carried out using a genetic algorithm (GA) as well as a hybrid genetic algorithm (HGA) approach to optimize the objective function designed for the inversion. An HGA is a two steps coupled process, where a local optimization algorithm is applied to the best model obtained from each generation of the GA. The study aims to compare the qualitative as well as the quantitative performance of both methods to delineate the reservoir zone from the non-reservoir zone. Initially, the developed algorithm is tested on synthetic data followed by its application to real data. It is found that the HGA for synthetic data is providing more accurate and high-resolution subsurface information as compared with the conventional GA although the time taken later is less as compared with the former methods. The application to real data also shows very high-resolution subsurface acoustic impedance information. The interpretation of the impedance section shows a low impedance anomaly zone at (1055–1070) ms time interval with impedance ranging from (7500 to 9500) m s−1*g cc−1. The correlation between seismic and well data shows that the low impedance zone is characterized as a clastic glauconitic sand channel (reservoir zone). In seismic inversion using an HGA, one can delineate the areal extent of the reservoir zone from the non-reservoir zone more specifically as compared to the GA-derived impedance. The convergence time of HGA is 4.4 per cent more than GA and can be even more for larger seismic reflection data sets. Further, for a more detailed analysis of the reservoir zone and to cross-validate inverted results, an artificial neural network (ANN) is applied to data, and porosity volume is predicted. The analysis shows that the low impedance zone interpreted in inversion results are correlating with the high porosity zone found in ANN methods and confirm the presence of the glauconitic sand channel. This study is important in the aspect of qualitative as well as quantitative comparison of the performance of the GA and HGA to delineate sand channels. [ABSTRACT FROM AUTHOR]

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region. [ABSTRACT FROM AUTHOR]