Seismic Reservoir Characterisation of Tyumen Formation in Frolov Megadepression

The article discusses the approaches and methods to study the Middle Jurassic deposits of the Tyumen Formation within the Frolov megadepression (West Siberian oil and gas province), which have high hydrocarbon potential. The materials refer to several areas with available 3D seismic data and several dozen oil wells. The problems of seismic interpretation and its application for geological modeling are considered. We also propose several ways to overcome them.

Seismic reservoir characterization of gas-sand deposits in the Kutei basin, Indonesia

Kutei Basin has the second largest hydrocarbon reserve in Indonesia. In addition to the Miocene inversion related structural traps, slope-fan and channel stratigraphic traps are also important traps in this basin. To guide stratigraphic traps explorations in the basin, the seismic stratigraphy, attributes, and AI inversion methods are integrated to identify and map the reservoir seismic facies, porosity, and pore-fluid. Well data indicates that the studied reservoirs are filled by gas. Seismic data shows that there are two main gas-sand reservoirs corresponding to strong amplitude anomaly. Seismic stratigraphy analysis, guided by seismic attributes, shows that these gas-sand reservoirs were deposited in the channel and local fan facies. The AI inversion is applied to identify and map the porosity and pore-fluid of these two sand reservoirs. Future well locations are identified by integrating the facies, porosity, and pore-fluid maps.

Seismic Soundoff

In this episode, Andrew Geary speaks with Ali Tura about his upcoming Distinguished Lecture, “Recent advances in seismic reservoir characterization and monitoring.” Tura provides an overview of the three advances he highlights in his lecture and shares a few that didn't make the list. In addition, he explains why carbon sequestration is the most important issue facing the industry and why geophysics is well positioned to support sequestration for enhanced oil recovery. Hear the full episode at https://seg.org/podcast/post/12481 .

SeReMpy: Seismic Reservoir Modeling Python library

Seismic reservoir characterization is a subfield of geophysics that combines seismic and rock physics modeling with mathematical inverse theory to predict the reservoir variables from the measured seismic data. An open-source comprehensive modeling library that includes the main concepts and tools is still missing. We present a Python library named SeReMpy with the state of the art of seismic reservoir modeling for reservoir properties characterization using seismic and rock physics models and Bayesian inverse theory. The most innovative component of the library is the Bayesian seismic and rock physics inversion to predict the spatial distribution of petrophysical and elastic properties from seismic data. The inversion algorithms include Bayesian analytical solutions of the linear-Gaussian inverse problem and Markov chain Monte Carlo (McMC) numerical methods for non-linear problems. The library includes four modules: geostatistics, rock physics, facies, and inversion, as well as several scripts with illustrative examples and applications. We present a detailed description of the scripts that illustrate the use of the functions of module and describe how to apply the codes to practical inversion problems using synthetic and real data. The applications include a rock physics model for the prediction of elastic properties and facies using well log data, a geostatistical simulation of continuous and discrete properties using well logs, a geostatistical interpolation and simulation of two-dimensional maps of temperature, an elastic inversion of partial stacked seismograms with Bayesian linearized AVO inversion, a rock physics inversion of partial stacked seismograms with McMC methods, and a two-dimensional seismic inversion.