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Fractures contribute significantly to the permeability of a formation. It is important to understand the fracture distribution and fluid transmissivity. Though traditional well logs can image fractures intersecting the borehole, they provide little information on the lateral extent of the fractures, away from the borehole, or the fluid transmissivity. Experiments in the past demonstrated that fracture compliance can be a good proxy to fracture fluid conductivity. We describe a method to estimate fracture compliance from the attenuation of Stoneley waves across a fracture. Solving the dispersion relation in the fracture, transmission coefficient of Stoneley waves across a fracture is studied over all frequency ranges. Based on the observations from the model, we propose that measuring the transmission coefficient near a transition frequency can help constrain fracture compliance and aperture. Comparing attenuation across a finite fracture to that of an infinitely long fracture, we show that a bound on the lateral extent of the fracture can be obtained. Given the limitation on the bandwidth of acoustic logging data, we propose using the Stoneley waves generated during micro-seismic events for fracture characterization.

Yang, Di (MIT) | Zheng, Yingcai (MIT) | Fehler, Michael (MIT) | Malcolm, Alison (MIT)

Time-lapse seismic data are widely used for monitoring time-variant subsurface changes. Conventional analysis provides qualitative information by comparing results from consecutive surveys, whereas waveform inversion can retrieve quantitative estimates of reservoir properties through seismic waveform fitting. The quantitative evaluation of the physical parameters obtained by waveform inversion allows for better interpretation of fluid substitution and migration during processes like oil and gas production, and carbon sequestration. Since reservoir changes are localized and only part of the data are of interest, the time-lapse waveform inversion can be optimized in terms of computational cost and convergence rate. In this study, we propose a scheme of localized waveform inversion with computed datasets we refer to as virtual surveys. Both the model domain and trace duration in forward modeling are reduced by the reorganization of the data. We show a numerical example in which the recovery of the reservoir change is computationally faster and more robust to source-receiver locations than inversion with original survey.

SPE Disciplines:

We describe a methodology for quantitatively characterizing the fractured nature of a hydrocarbon or geothermal reservoir from surface seismic data under a Bayesian inference frame-work. Fractures provide pathways for fluid flow in a reservoir, and hence, knowledge about a reservoir's fractured nature can be used to enhance production of the reservoir. The fracture properties of interest in this study (to be inferred) are fracture orientation and excess compliance, where each of these properties are assumed to vary spatially over a 2D lateral grid which is assumed to represent the top of a reservoir. The Bayesian framework in which the inference problem is cast has the key benefits of (1) utilization of a prior model that allows geological information to be incorporated, (2) providing a straightforward means of incorporating all measurements (across the 2D spatial grid) into the estimates at each grid point, (3) allowing different types of measurements to be combined under a single inference procedure, and (4) providing a measure of uncertainty in the estimates. The observed data are taken from a 2D array of surface seismic receivers responding to an array of surface sources. Well understood features from the seismic traces are extracted and treated as the observed data, namely the P-wave reflection amplitude variation with acquisition azimuth (amplitude versus azimuth, or AvAz, data) and fracture transfer function (FTF) data. AvAz data are known to be more sensitive to fracture properties when the fracture spacing is significantly smaller than the seismic wavelength, whereas fracture transfer function data are more sensitive to fracture properties when the fracture spacing is on the order of the seismic wave-length. Combining these two measurements has the benefit of allowing inferences to be made about fracture properties over a larger range of fracture spacing than otherwise attainable. Geophysical forward models for the measurements are used to arrive at likelihood models for the data. The prior distribution for the hidden fracture variables is obtained by defining a Markov random field (MRF) over the lateral 2D grid where we wish to obtain fracture properties. The fracture variables are then inferred by application of loopy belief propagation (LBP) to yield approximations for the posterior marginal distributions of the fracture properties, as well as the

Oilfield Places:

- North America > United States > Wyoming > Green River Basin (0.99)
- North America > United States > Utah > Green River Basin (0.99)

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Technology:

- IT > AI > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- IT > AI > Machine Learning > Bayesian Networks (1.00)

Mapping, localization, and general characterization of problems in reservoir fracture systems is one of the most important in oil, gas, and geothermal energy production. One way to study and monitor these fracture systems is to analyze the microearthquakes triggered during hydraulic fracturing or stimulation, as these events generally occur along newly created and preexisting fractures. Thus, the location of the microseismic events can be used to characterize the properties of the fracture system. There are many different methods for localizing microearthquakes and, in general, these methods yield different locations, velocity models, and event origin times, due to differences in algorithms and input models. This makes it very difficult to know which one gives the most accurate and consistent results in practice. The goal of this work is to use basic concepts from seismic interferometry for estimating constraints on the P and S traveltimes between two microearthquake locations. Information obtained through seismic interferometry pertains to only the Earth parameters between two receivers or, by reciprocity, two sources. This information is also less dependent on the velocity model, and less susceptible to errors in arrival time picking and noise in the data due to averaging over receivers. This information can then be used to evaluate and compare different sets of results obtained through different localization methods. Here we illustrate this comparison method by comparing localization results from two different methods. For our data set, in particular, seismic interferometry cannot give hard constrains but it gives bounds that can be used to asses results from different localization methods.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)

Fang, Xinding (Massachusetts Institute of Technology) | Fehler, Michael (Massachusetts Institute of Technology) | Zhu, Zhenya (Massachusetts Institute of Technology) | Chen, Tianrun (Massachusetts Institute of Technology) | Brown, Stephen (Massachusetts Institute of Technology) | Toksöz, M. Nafi (Massachusetts Institute of Technology) | Cheng, Arthur (Halliburton)

Formation elastic properties near a borehole may be altered from their original state due to the stress concentration around the borehole. This could result in a biased estimation of formation properties but could provide a means to estimate

SPE Disciplines:

Geophysical imaging and subsurface characterization for offshore petroleumexploration and reservoir characterization face increasingly demandingrequirements for reliability and for providing increased information about theearth's subsurface. New methods for data acquisition, data processing andsimultaneous analysis of multiple types of geophysical datasets (e.g. seismic,EM, gravity) are helping to meet these challenges but there is need to testthese methods and to quantify their robustness. Testing and evaluation of newmethods can be done using simulated benchmark datasets provided the simulationsare calculated using realistic models and that the simulation methodology iswell validated. The benchmark datasets must not only be reliably calculated butthey must be sufficiently large to mimic state-of-the-art fieldacquisitions.

The SEG Advanced Modeling Project (SEAM), using funding provided by theResearch Partnership to Secure Energy for America (RPSEA), has developed amodel for a deepwater region that contains a major salt body and severalpetroleum reservoirs located around and beneath the salt. Constructing andconducting geophysical simulations on the model is a challenge for currenthigh-performance computing technology. A suite of geophysical simulations isbeing conducted on the model including acoustic-wave, Tilted TransverseIsotropic acoustic, Gravity, Controlled Source Electromagnetic, Magnetotelluricand Elastic. Geophysicists are actively using these datasets to facilitatetheir development and testing of new algorithms and acquisition schemes forbetter subsurface characterization. The access to large multidisciplinarydatasets calculated on a single realistic model developed for a deepwaterregion like the Gulf of Mexico has long been desired to allow testing andbenchmarking of geophysical techniques, testing of new data acquisitionschemes, and assessing the value of multi-disciplinary inversion approaches.SEAM is seeking to provide these datasets for the geophysical community.

SPE Disciplines: Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)