This paper is an attempt to find the interdependence existing between petrophysical properties and ultrasonic wave velocities. Porosity and permeability, apart from other properties, are the two fundamental physical properties of rock responsible for storing and producing hydrocarbon. Understanding the elastic properties of such rocks is essential in developing a deep understanding about the rock and fluid models that describe the seismic response to realistic hydrocarbon reservoirs under different environmental conditions. Therefore, in this study, a detailed state-of-the-art review of the existing association between compressional and shear wave (also known as P wave and S wave) velocities (V p and V s) and different petrophysical properties (porosity, density, permeability, water absorption and clay content) has been summarized for carbonate and sandstone rock types of different regions. The relationships discussed are based on datasets measured in laboratory by various researchers under ambient conditions. An effort is made to propose a general trend (global trends) for porosity versus V p and bulk density versus V p , which is independent of the type of datasets. However, it is clear that trends do exist, but the prediction is difficult. The empirical relationships derived by various researchers are valid only to the particular dataset for which the relationship was derived. The influence of other factors like mineralogy, rock framework, pore geometry has not been studied by the researchers in their respective studies. Nevertheless, these relationships and correlations can be useful in hydrocarbon exploration industry where direct measurements may not be possible. Moreover, an accurate reservoir description can only be achieved by the integration of geological, petrophysical and geotechnical data.

No abstract

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

No abstract

No abstract