The microtremor HVSR, also known as Nakamuras approach, is an empirical technique that is becoming more suitable for site response analysis. The main goal of this research is to develop microtremor zonation (microzonation) of the study area to examine the potential risk of blast and other vibration sources, determine the seismic vulnerability indices of the area based on the site response parameters, and develop the distribution of the site response parameters. Surface waves were recorded at 34 locations using a three-component geophone to obtain the microtremor data. According to the site response parameters, the natural peak frequency varied from 2 to 5Hz, the maximum period was less than 0.5seconds, and the maximum amplification factor (H/V ratio) was 5.50. Of the 34 locations, 26 recorded low vulnerability indices (K_g) in the range of 0.27 to 4.82 and, thus, classified as Zone 1 materials. This indicates that K_g is typically low throughout the research area, except for a few areas that had extremely high vulnerability. The high vulnerabilities can be attributed to dense, loose soils in the vicinity, which amplify ground vibrations. Since the average natural period is less than 0.5seconds, a seismic events impact would not be felt for a longer time unless it occurred in a region with extremely low frequencies (F₀1.0Hz).

Following the increased demand for oil and gas in recent times, reservoir characterization has become an important field of study as researchers attempt to appreciate the dynamics of the reservoir. This challenge brings to the fore the need to integrate various disciplines such as geology, geophysics, petrophysics, engineering, and Artificial Intelligence (AI) application to study the property of the reservoir. Integration of these disciplines demands a detailed grasp of past successes and failures, as well as a full comprehension of reservoir characterisation techniques and non-technical elements. Relevant information can be found by looking for similar projects published in the past. There are a few more recent review papers; however, they are dedicated to a specific aspect of the AI application in reservoir characterization. This review paper focuses on the evolution of AI algorithms in reservoir characterisation over the past years. It emphasises the empirical approach, traditional AI methods, and recent AI advancements (hybrid AI methods) for reservoir property prediction. The review also addresses some challenges and perspectives on the way forward for reservoir characterisation. <!----> <!----> <!----> <!---->