Establishing Spatial Pattern Correlations Between Water Saturation Time-Lapse and Seismic Amplitude Time-Lapse

2006 ◽  
Vol 45 (11) ◽  
Author(s):  
J. Wu ◽  
A.G. Journel ◽  
T. Mukerji
Keyword(s):  
Spatial Pattern ◽  
Water Saturation ◽  
Time Lapse ◽  
Saturation Time ◽  
Seismic Amplitude

Time‐lapse (4-D) seismic monitoring of primary production of turbidite reservoirs at South Timbalier Block 295, offshore Louisiana, Gulf of Mexico

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 351-367 ◽  
Author(s):  
Tucker Burkhart ◽  
Andrew R. Hoover ◽  
Peter B. Flemings
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Hydrocarbon Production ◽  
Seismic Surveys ◽  
Seismic Amplitude ◽  
Fluid Properties ◽  
Multiple Reservoir ◽  
Gas Drive

Two seismic surveys acquired over South Timbalier Block 295 field (offshore Louisiana) record significant differences in amplitude that are correlated to hydrocarbon production at multiple reservoir levels. The K8 sand, a solution‐gas‐drive reservoir, shows increases in seismic amplitude associated with gas exsolution. The K40 sand, a water‐drive reservoir, shows decreases in seismic amplitude associated with increases in water saturation. A methodology is presented to optimize the correlation between two seismic surveys after they have been individually processed (poststack) This methodology includes rebinning, crosscorrelation, band‐pass filtering, and cross‐equalization. A statistical approach is developed to characterize the correlation between the seismic surveys. This statistical analysis is used to discriminate seismic amplitude differences that record change in rock and fluid properties from those that could be the result of miscorrelation of the seismic data. Time‐lapse seismic analysis provides an important new approach to imaging hydrocarbon production; it may be used to improve reservoir characterization and guide production decisions.

Estimating saturation and density changes caused by CO2 injection at Sleipner — Using time-lapse seismic amplitude-variation-with-offset and time-lapse gravity

2017 ◽  
Vol 5 (2) ◽  
pp. T243-T257 ◽  
Author(s):  
Martin Landrø ◽  
Mark Zumberge
Keyword(s):  
Gravity Data ◽  
Time Lapse ◽  
Co2 Injection ◽  
Injection Point ◽  
Amplitude Variation With Offset ◽  
Seismic Method ◽  
Seismic Amplitude ◽  
Gravity Measurements ◽  
Measured Time ◽  
Best Fit

We have developed a calibrated, simple time-lapse seismic method for estimating saturation changes from the [Formula: see text]-storage project at Sleipner offshore Norway. This seismic method works well to map changes when [Formula: see text] is migrating laterally away from the injection point. However, it is challenging to detect changes occurring below [Formula: see text] layers that have already been charged by some [Formula: see text]. Not only is this partly caused by the seismic shadow effects, but also by the fact that the velocity sensitivity for [Formula: see text] change in saturation from 0.3 to 1.0 is significantly less than saturation changes from zero to 0.3. To circumvent the seismic shadow zone problem, we combine the time-lapse seismic method with time-lapse gravity measurements. This is done by a simple forward modeling of gravity changes based on the seismically derived saturation changes, letting these saturation changes be scaled by an arbitrary constant and then by minimizing the least-squares error to obtain the best fit between the scaled saturation changes and the measured time-lapse gravity data. In this way, we are able to exploit the complementary properties of time-lapse seismic and gravity data.

scholarly journals Estimating reservoir permeability with borehole radar

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. H51-H60
Author(s):  
Feng Zhou ◽  
Iraklis Giannakis ◽  
Antonios Giannopoulos ◽  
Klaus Holliger ◽  
Evert Slob
Keyword(s):  
Water Saturation ◽  
Time Lapse ◽  
Drilling Mud ◽  
Invasion Depth ◽  
Initial Water ◽  
Oil Drilling ◽  
Reservoir Evaluation ◽  
Reservoir Permeability ◽  
Borehole Radar ◽  
Porosity And Permeability

In oil drilling, mud filtrate penetrates into porous formations and alters the compositions and properties of the pore fluids. This disturbs the logging signals and brings errors to reservoir evaluation. Drilling and logging engineers therefore deem mud invasion as undesired and attempt to eliminate its adverse effects. However, the mud-contaminated formation carries valuable information, notably with regard to its hydraulic properties. Typically, the invasion depth critically depends on the formation porosity and permeability. Therefore, if adequately characterized, mud invasion effects could be used for reservoir evaluation. To pursue this objective, we have applied borehole radar to measure mud invasion depth considering its high radial spatial resolution compared with conventional logging tools, which then allows us to estimate the reservoir permeability based on the acquired invasion depth. We investigate the feasibility of this strategy numerically through coupled electromagnetic and fluid modeling in an oil-bearing layer drilled using freshwater-based mud. Time-lapse logging is simulated to extract the signals reflected from the invasion front, and a dual-offset downhole antenna mode enables time-to-depth conversion to determine the invasion depth. Based on drilling, coring, and logging data, a quantitative interpretation chart is established, mapping the porosity, permeability, and initial water saturation into the invasion depth. The estimated permeability is in a good agreement with the actual formation permeability. Our results therefore suggest that borehole radar has significant potential to estimate permeability through mud invasion effects.

A practical workflow using quantitative interpretation of seismic amplitudes to derisk infill wells in deepwater Gulf of Mexico: The Auger example

2019 ◽  
Vol 38 (10) ◽  
pp. 754-761 ◽  
Author(s):  
Liqin Sang ◽  
Uwe Klein-Helmkamp ◽  
Andrew Cook ◽  
Juan R. Jimenez
Keyword(s):  
Gulf Of Mexico ◽  
Time Lapse ◽  
Quantitative Interpretation ◽  
Efficient Manner ◽  
Data Set ◽  
Seismic Amplitude ◽  
Imperfect Data ◽  
Avo Inversion ◽  
Fluid Saturation ◽  
Single Well

Seismic direct hydrocarbon indicators (DHIs) are routinely used in the identification of hydrocarbon reservoirs and in the positioning of drilling targets. Understanding seismic amplitude reliability and character, including amplitude variation with offset (AVO), is key to correct interpretation of the DHI and to enable confident assessment of the commercial viability of the reservoir targets. In many cases, our interpretation is impeded by limited availability of data that are often less than perfect. Here, we present a seismic quantitative interpretation (QI) workflow that made the best out of imperfect data and managed to successfully derisk a multiwell drilling campaign in the Auger and Andros basins in the deepwater Gulf of Mexico. Data challenges included azimuthal illumination effects caused by the presence of the Auger salt dome, sand thickness below tuning, and long-term production effects that are hard to quantify without dedicated time-lapse seismic. In addition, seismic vintages with varying acquisition geometries led to different QI predictions that further complicated the interpretation story. Given these challenges, we implemented an amplitude derisking workflow that combined ray-based illumination assessments and prestack data observations to guide selection of the optimal seismic data set(s) for QI analysis. This was followed by forward modeling to quantify the fluid saturation and sand thickness effects on seismic amplitude. Combined with structural geology analysis of the well targets, this workflow succeeded in significantly reducing the risk of the proposed opportunities. The work also highlighted potential pitfalls in AVO interpretation, including AVO inversion for the characterization of reservoirs near salt, while providing a workflow for prestack amplitude quality control prior to inversion. The workflow is adaptable to specific target conditions and can be executed in a time-efficient manner. It has been applied to multiple infill well opportunities, but for simplicity reasons here, we demonstrate the application on a single well target.

A new method for connate water saturation calculation using time-lapse logging data

2006 ◽  
Vol 50 (3-4) ◽  
pp. 204-210 ◽  
Author(s):  
Jianmeng Sun ◽  
Shiyi Zheng ◽  
Jianhai Ma
Keyword(s):  
Water Saturation ◽  
Time Lapse ◽  
New Method ◽  
Connate Water

Time-lapse seismic signal analysis for enhanced oil recovery at Cranfield CO2 sequestration site, Cranfield field, Mississippi

2013 ◽  
Vol 1 (2) ◽  
pp. T157-T166 ◽  
Author(s):  
Julie Ditkof ◽  
Eva Caspari ◽  
Roman Pevzner ◽  
Milovan Urosevic ◽  
Timothy A. Meckel ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Time Lapse ◽  
Seismic Signal ◽  
Injection Well ◽  
Saturation Curve ◽  
Seismic Surveys ◽  
Seismic Amplitude ◽  
The Difference ◽  
Seismic Volumes ◽  
4D Seismic

The Cranfield field in southwest Mississippi has been under continuous [Formula: see text] injection by Denbury Onshore LLC since 2008. Two 3D seismic surveys were collected in 2007 and 2010. An initial 4D seismic response was characterized after three years of injection, where more than three million tons of [Formula: see text] remain in the subsurface. This interpretation showed coherent seismic amplitude anomalies in some areas that received large amounts of [Formula: see text] but not in others. To understand these effects better, we performed Gassmann substitution modeling at two wells: the 31F-2 observation well and the 28-1 injection well. We aimed to predict a postinjection saturation curve and acoustic impedance (AI) change through the reservoir. Seismic volumes were cross-equalized, well ties to seismic were performed, and AI inversions were subsequently carried out. Inversion results showed that the change in AI is higher than Gassmann substitution predicted for the 28-1 injection well. The time-lapse AI difference predicted by the inversion is similar in magnitude to the difference inferred from a time delay along a marker horizon below the reservoir.

Establishing spatial pattern correlation for interpreting time lapse seismic amplitudes

2004 ◽  
Author(s):  
Jianbing Wu ◽  
Tapan Mukerji ◽  
Andre G. Journel
Keyword(s):  
Spatial Pattern ◽  
Time Lapse ◽  
Pattern Correlation ◽  
Seismic Amplitudes
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