Integration of Microseismic With Rock Properties From Multi-Component Seismic Data, Mississippi Lime Play, North-Central Oklahoma

Deep neural networks (DNNs) have the potential to streamline the integration of seismic data for reservoir characterization by providing estimates of rock properties that are directly interpretable by geologists and reservoir engineers instead of elastic attributes like most standard seismic inversion methods. However, they have yet to be applied widely in the energy industry because training DNNs requires a large amount of labeled data that is rarely available. Training set augmentation, routinely used in other scientific fields such as image recognition, can address this issue and open the door to DNNs for geophysical applications. Although this approach has been explored in the past, creating realistic synthetic well and seismic data representative of the variable geology of a reservoir remains challenging. Recently introduced theory-guided techniques can help achieve this goal. A key step in these hybrid techniques is the use of theoretical rock-physics models to derive elastic pseudologs from variations of existing petrophysical logs. Rock-physics theories are already commonly relied on to generalize and extrapolate the relationship between rock and elastic properties. Therefore, they are a useful tool to generate a large catalog of alternative pseudologs representing realistic geologic variations away from the existing well locations. While not directly driven by rock physics, neural networks trained on such synthetic catalogs extract the intrinsic rock-physics relationships and are therefore capable of directly estimating rock properties from seismic amplitudes. Neural networks trained on purely synthetic data are applied to a set of 2D poststack seismic lines to characterize a geothermal reservoir located in the Dogger Formation northeast of Paris, France. The goal of the study is to determine the extent of porous and permeable layers encountered at existing geothermal wells and ultimately guide the location and design of future geothermal wells in the area.

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Rock Physics Guided Data Science Technique to Improve Neural Network Training to Accurately Quantify Rock Properties from Seismic Data

10.2118/202685-ms ◽

2020 ◽

Author(s):

Shraddha Chatterjee ◽

Tanya Colwell ◽

Jon Downton ◽

Olivia Collet ◽

Beth Rees

Keyword(s):

Neural Network ◽

Seismic Data ◽

Data Science ◽

Rock Physics ◽

Rock Properties ◽

Neural Network Training ◽

Network Training

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CRPP - Comprehensive Rock Properties Predictor - A New Method for Predicting Rock Properties from the Seismic Data

74th EAGE Conference and Exhibition incorporating EUROPEC 2012 ◽

10.3997/2214-4609.20148583 ◽

2012 ◽

Author(s):

M. Gruszczyk

Keyword(s):

Seismic Data ◽

New Method ◽

Rock Properties

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A fresh look at Gulf of Mexico tectonics: Testing rotations and breakup mechanisms from the perspective of seismically constrained potential-fields modeling and plate kinematics

Interpretation ◽

10.1190/int-2019-0256.1 ◽

2020 ◽

Vol 8 (4) ◽

pp. SS31-SS45

Author(s):

Daniel Minguez ◽

E. Gerald Hensel ◽

Elizabeth A. E. Johnson

Keyword(s):

Gulf Of Mexico ◽

Seismic Data ◽

Gravity Data ◽

Seafloor Spreading ◽

Plate Motion ◽

Rock Properties ◽

Satellite Gravity ◽

Spreading Model ◽

Breakup Mechanism ◽

Mantle Exhumation

Interpretation of recent, high-quality seismic data in the Gulf of Mexico (GOM) has led to competing hypotheses regarding the basin’s rift to drift transition. Some studies suggest a fault-controlled mechanism that ultimately results in mantle exhumation prior to seafloor spreading. Others suggest voluminous magmatic intrusion accommodates the terminal extension phase and results in the extrusion of volcanic seaward dipping reflectors (SDRs). Whereas it has been generally accepted that the plate motions between the rift and drift phases of the GOM are nearly perpendicular to each other, it has not been greatly discussed if the breakup mechanism plays a role in accommodating the transition in plate motion. We have developed a plate kinematic and crustal architecture hypothesis to address the transition from rift to drift in the GOM. We support the proposition of a fault-controlled breakup mechanism, in which slip on a detachment between the crust and mantle may have exhumed the mantle. However, we stress that this mechanism is not exclusive of synrift magmatism, though it does imply that SDRs observed in the GOM are not in this case indicative of a volcanic massif separating attenuated continental and normal oceanic crust. We support our hypothesis through a geometrically realistic 2D potential field model, which includes a magnetic seafloor spreading model constrained by recent published seismic data and analog rock properties. The 2D model suggests that magnetic anomalies near the continent-ocean transition may be related to removal of the lower continental crust during a phase of hyperextension prior to breakup, ending in mantle exhumation. The kinematics of breakup, derived from recent satellite gravity data and constrained by our spreading model and the global plate circuit, suggests that this phase of hyperextension accommodated the change in plate motion direction and a diachronous breakup across the GOM.

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Fast-Track Rock Properties From Broadband Seismic Data

10.2523/17911-ms ◽

2014 ◽

Author(s):

Axel Geisslinger ◽

Azim Salleh

Keyword(s):

Seismic Data ◽

Fast Track ◽

Rock Properties

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Seismic inversion for geologic fractures and fractured media

Geophysics ◽

10.1190/geo2016-0123.1 ◽

2017 ◽

Vol 82 (5) ◽

pp. C145-C161 ◽

Cited By ~ 3

Author(s):

Xiaoqin Cui ◽

Edward S. Krebes ◽

Laurence R. Lines

Keyword(s):

Seismic Data ◽

Wave Reflection ◽

Rock Properties ◽

Reflection And Transmission ◽

Transmission Coefficients ◽

Avo Inversion ◽

Impedance Contrast ◽

Fractured Medium ◽

Pp Wave ◽

Contact Part

Amplitude variation with offset (AVO) inversion attempts to use the available surface seismic data to estimate the density, P-wave velocity, and S-wave velocity of the earth model. Under linear slip interface theory, synthetic seismograms for models with fractures prove that fractures are also reflection generators. Consequently, observed reflections are not necessarily due to lithologic variations only, but they could be due in part to the effect of fractures. To obtain approximate equations for AVO inversion for fractured media, denoted by AVO with fracture (AVOF), we derived new equations for PP-wave reflection and transmission coefficients that are based on nonwelded contact boundary conditions. In particular, along with the fracture compliances, azimuth has also been taken into account in the equations because the fractures can have any orientation. The new approximate AVOF equations for a horizontally fractured medium with impedance contrast are developed by simplifying the equations for the new PP-wave reflection and transmission coefficients. In the new approximate AVOF equations, the reflection coefficients are divided into a welded contact part (a conventional impedance contrast part) and a nonwelded contact part (a fracture part). This makes the equations flexible enough to separately invert for the rock properties of the fracture and the background medium in the case of a fractured medium with impedance contrast. The new approximate AVOF equations state that fractures could cause the seismic reflectivity to be frequency dependent, and that the fractures not only influence the wave amplitude but also change the wave phase. The linear least-squares and nonlinear conjugate gradient inversion algorithms are applied to estimate the elastic reflectivity using the new approximate AVOF equations. The inverted results for seismic data for a horizontally fractured medium with impedance contrast are evaluated to find a more accurate delineation of the subsurface rock properties.

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Reservoir Prediction Under Control of Sedimentary Facies

Journal of Computational Acoustics ◽

10.1142/s0218396x17500229 ◽

2017 ◽

Vol 25 (03) ◽

pp. 1750022

Author(s):

Xiuwei Yang ◽

Peimin Zhu

Keyword(s):

Seismic Data ◽

Low Frequency ◽

Prediction Method ◽

Rock Physics ◽

Real Data ◽

Sedimentary Facies ◽

Seismic Inversion ◽

Rock Properties ◽

Reservoir Prediction ◽

Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

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