Assessment of different approaches to rock-physics modeling: A case study from offshore Nile Delta

The estimation of a reliable rock-physics model (RPM) plays a crucial role in reservoir characterization studies. We assess different methods in deriving a reliable RPM that will be used in conjunction with amplitude-versus-angle inversion for the characterization of a clastic reservoir located in offshore Nile Delta. The reservoir zone is located in gas-saturated sand channels surrounded by shale sequences within a depth interval ranging between 2.3 and 2.7 km. One theoretical and three empirical approaches to derive a RPM are analyzed: The theoretical RPM is established using the well-known rock-physics equations valid for granular materials, whereas the empirical RPMs are derived using one multilinear stepwise regression and two nonlinear regression procedures based on neural networks (NNs) and genetic algorithms (GAs). A proper calibration and validation of the derived RPMs is conducted by using the extensive log suite of four existing wells drilled over an area of 100 km2. For the investigated reservoir interval and for the encasing shales, all the analyzed methods give a final RPM that is able to reliably predict the elastic attributes (P-wave velocity, S-wave velocity, and density) from the petrophysical properties of interest (porosity, water saturation, and shaliness). Among the empirical approaches, the RPM predicted by the multilinear regression is characterized by a prediction capability very similar to the RPMs predicted by the nonlinear GA method, thus demonstrating that in the investigated zone, the relation linking the petrophysical properties to the elastic attributes can be conveniently described by a multilinear model. Differently, the NN method seems to be affected by the overfitting problem that produces a RPM with a lower prediction capability than the RPMs estimated by the other methods. The theoretical method yields predictions of elastic properties very similar to those produced by multilinear regression

A Machine Learning and Data-Driven Prediction and Inversion of Reservoir Brittleness from Geophysical Logs and Seismic Signals: A Case Study in Southwest Pennsylvania, Central Appalachian Basin

In unconventional reservoir sweet-spot identification, brittleness is an important parameter that is used as an easiness measure of production from low permeability reservoirs. In shaly reservoirs, production is realized from hydraulic fracturing, which depends on how brittle the rock is–as it opens natural fractures and also creates new fractures. A measure of brittleness, brittleness index, is obtained through elastic properties of the rock. In practice, problems arise using this method to predict brittleness because of the limited availability of elastic logs. To address this issue, machine learning techniques are adopted to predict brittleness at well locations from readily available geophysical logs and spatially using 3D seismic data. The geophysical logs available as input are gamma ray, neutron, sonic, photoelectric factor, and density logs while the seismic is a post-stack time migrated data of high quality. Support Vector Regression, Gradient Boosting, and Artificial Neural Network are used to predict the brittleness from the geophysical logs and Texture Model Regression to invert the brittleness from the seismic data. The Gradient Boosting outperformed the other algorithms in predicting brittleness. The result of this research further demonstrates the application of machine learning, and how these tools can be leveraged to create data-driven solutions to geophysical problems. Also, the seismic inversion of brittleness shows promising results that will be further investigated in the future

Estimation Of Reservoir Properties From Seismic Data By Smooth Neural Networks

Traditional joint inversion methods reqnire an a priori prescribed operator that links the reservoir properties to the observed seismic response. The methods also rely on a linearized approach to the solution that makes them heavily dependent on the selection of the starting model. Neural networks provide a useful alternative that is inherently nonlinear and completely data-driven, but the performance of traditional back-propagation networks in production settings has been inconsistent due to the extensive parameter tweaking needed to achieve satisfactory results and to avoid overfitting the data. In addition, the accuracy of these traditional networks is sensitive to network parameters, such as the network size and training length. We present an approach to estimate the point-values of the reservoir rock properties (such as porosity) from seismic and well log data through the use of regularized back propagation and radial basis networks. Both types of networks have inherent smoothness characteristics that alleviate the nonmonotonous generalization problem associated with traditional networks and help to avert overfitting the data. The approach we present therefore avoids the drawbacks of both the joint inversion methods and traditional back-propagation networks. Specifically, it is inherently nonlinear, requires no a priori operator or initial model, and is not prone to overfitting problems, thus requiring no extensive parameter experimentation.Massachusetts Institute of Technology. Borehole Acoustics and Logging ConsortiumMassachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation ConsortiumSaudi Aramc