First Break - Volume 30, Issue 7, 2012

Peter Geiser, Alfred Lacazette and Jan Vermilye introduce tomographic fracture imaging (TFI) as a new semblance-based tool using surface seismic networks to directly map both the natural and induced fracture systems of the Earth’s brittle crust. In contrast to microseismic data consisting solely of hypo-centres TFI is said to have the ability to obtain images of the actual fractures themselves.

S. C. Williams-Stroud, W. B. Barker and K. L. Smith present a case study of a microseismic monitoring result that illustrates the significant impact that natural fractures in the reservoir can have on the stimulation and mapping results.

Gisela Viegas, Adam Baig, Wade Coulter and Ted Urbancic show how enhancing a seismic network with high-sensitivity low-frequency accelerometers make it possible to correctly extend the recording range from –Mw3 to +Mw3 and properly assess the behaviour of reservoir-induced seismicity.

V. Oye, J. Albaric, N. Langet, M. Hasting, I. Lecomte, M. Messeiller and P. Reid explain the process of locating micro-earthquakes that are associated with the creation and reactivation of the fracture network in an enhanced geothermal system and demonstrate the necessity of a reliable velocity model. The example comes for a project in South Australia.

Miroslav Hallo, Leo Eisner and Mohammed Y. Ali review theories and case studies suitable for estimating the maximum seismic event induced by fluid injection or more generally volumetric changes and show that theory proposed for the mining industry is applicable to fluid injection as an upper bound for induced seismicity.

Nick Martakis, Katerina Polychronopoulou and G-Akis Tselentis describe recent developments in passive seismic tomography with a number of case studies showing the progress made in this emerging technology.

Although the general distribution of facies and corresponding reservoir properties should be predicted from geological understanding, the particular characteristics and heterogeneity of a field may not be readily anticipated, especially if well coverage is limited. Seismic data offer the possibility to provide additional constraints on the spatial distribution of facies. Seismic data on their own do not provide the complete solution, so a thorough integration of seismic, well and geological input, and any other available information, is required to reduce uncertainty and produce meaningful and predictive models. This has three serious implications for facies model building. Firstly, the seismic, well and geological constraints must be applied simultaneously to ensure a consistent and unbiased integration of all data. Secondly, the seismic and reservoir properties must be related through a predictive rock physics model that should be established through analysis of well log data and include facies-dependent depth trends. Thirdly, the facies definition must be meaningful in all domains: elastic, reservoir and geological. Naturally, the facies should also be identifiable from petrophysical well log data to ensure that wells can be used to constrain the models. Bayesian stochastic inversion provides a framework that is well suited to achieve all these goals.

The objective of this paper is to illustrate the relative importance of the controlling factors in estimating seismic net pay from colour-inverted band-limited impedance data. It was achieved by using a new automated implementation of the process, allowing the sensitivity of each of the key controls on the resulting net pay to be found quickly and easily. This automated workflow was run over a synthetic dataset, varying different input parameters. The chi angle and the low frequencies used in building the tuning wedge were found to have the largest effect on the final volume. Using this technique on real datasets could greatly improve understanding of the possible range of pay volumes, reducing risk and highlighting the key input parameters.

We introduce a simplified way of using 4D seismic amplitude differences, without explicit full-physics modelling, in a history matching workflow that includes conditioning to both well production data and flooded-zone patterns derived from time-lapse seismic surveys. This approach reduces the complexity of the integration workflow, and speeds up the integration cycle. Examples from both synthetic and field cases demonstrate the success of the workflow.