Seismic inversion based on ANN: an advanced approach towards porosity model construction in the Algerian Saharan petroleum field

Abstract

Seismic inversion holds significant potential for providing crucial lithostratigraphic information in hydrocarbon reservoir characterisation and in identifying new traps. However, one of the major challenges in achieving reliable reservoir models in Algeria stems from the inherent uncertainties associated with seismic inversion algorithms and the non-linear relationship between petrophysical measurements. Due to their usefulness, several Artificial Neural Network algorithms have been developed and employed for seismic inversion and reservoir characterisation in the last few years. Nevertheless, only few researchers have addressed this issue in terms of optimisation of Multilayer FeedForward Neural Network (MLFN) architecture. In this case study, the use of an MLFN to address these challenges is proposed. The primary contribution of this research lies in the optimisation of the MLFN architecture based on trial and error procedures. The goal is to ensure that the computational demands are manageable within the constraints of available computing resources and that the process is time-efficient for geo-modellers. This practical approach is particularly valuable when applied at the reservoir scale. MLFN supervised training is conducted using logging data, where measured log curves serve as inputs, and core porosity, obtained from laboratory analysis, serves as target output. Moreover, coloured inversion is employed to generate a 3D seismic acoustic impedance cube, which, in turn, serves as input for a model-based inversion method designed to calculate porosity volume using the trained network. Furthermore, the usefulness of the resulting density cube is demonstrated through the correlation with density logs and core density values at wells 1, 2, and 3. Thence, the obtained correlation ranges validate the reliability of the obtained porosity volume in enhancing the characterisation of the targeted reservoir within the Algerian Saharan field.