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.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Prediction of residual oil saturation by using the ratio of amplitude attributes of time-lapse seismic data

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. IM1-IM12 ◽  
Author(s):  
Meng Li ◽  
Zhen Liu ◽  
Minzhu Liu ◽  
Huilai Zhang
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Seismic Interpretation ◽  
Impedance Change ◽  
Residual Oil ◽  
Data Set ◽  
Oil Saturation ◽  
Residual Oil Saturation ◽  
Amplitude Difference ◽  
Fluid Saturation

Subtraction of baseline and monitoring seismic data is a common step in highlighting reservoir changes in time-lapse seismic interpretation. However, ambiguity exists in the interpretation of the amplitude difference, which is controlled by fluid change and reservoir thickness. To estimate the residual oil saturation quantitatively, we have developed a time-lapse seismic interpretation method that uses the ratio of amplitude attributes extracted from the baseline and monitoring seismic data. The relationship between impedance change and the ratio of the baseline and monitoring amplitude attributes is determined to avoid the influence of reservoir thickness. Subsequently, the fluid saturation is calculated from the impedance change by using a proper petrophysical relationship. We have tested our new method on a real time-lapse seismic data set from a water-flooded reservoir in the deepwater area of West Africa. The water-flooded area determined from the amplitude difference does not completely match the production logs because of the influence of variations in the reservoir thickness. However, the residual oil distribution calculated with the proposed method matches the production logs well. The connectivity of sandstone bodies is also evaluated based on an integrated interpretation of estimated oil saturation. With its simple principles and easy accessibility, our method improves the accuracy of time-lapse seismic data interpretation in water-flooded oil reservoirs. Furthermore, the quantitative interpretation of fluid change enables the time-lapse seismic technology to guide reservoir development directly.

Discrimination between pressure and fluid saturation changes from time‐lapse seismic data

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 836-844 ◽  
Author(s):  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Time Lapse ◽  
Seismic Survey ◽  
Baseline Survey ◽  
Water Contact ◽  
Data Set ◽  
Seismic Surveys ◽  
Fluid Saturation ◽  
Oil Water ◽  
Pressure Changes

Explicit expressions for computing saturation‐ and pressure‐related changes from time‐lapse seismic data have been derived and tested on a real time‐lapse seismic data set. Necessary input is near‐and far‐offset stacks for the baseline seismic survey and the repeat survey. The method has been tested successfully in a segment where pressure measurements in two wells verify a pore‐pressure increase of 5 to 6 MPa between the baseline survey and the monitor survey. Estimated pressure changes using the proposed relationships fit very well with observations. Between the baseline and monitor seismic surveys, 27% of the estimated recoverable hydrocarbon reserves were produced from this segment. The estimated saturation changes also agree well with observed changes, apart from some areas in the water zone that are mapped as being exposed to saturation changes (which is unlikely). Saturation changes in other segments close to the original oil‐water contact and the top reservoir interface are also estimated and confirmed by observations in various wells.

Petro-electric modeling for CSEM reservoir characterization and monitoring

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. E9-E20 ◽  
Author(s):  
Alireza Shahin ◽  
Kerry Key ◽  
Paul Stoffa ◽  
Robert Tatham
Keyword(s):  
Electrical Resistivity ◽  
Water Saturation ◽  
Fluid Pressure ◽  
Time Lapse ◽  
Petroleum Exploration ◽  
Water Flooding ◽  
Algorithm Analysis ◽  
Production Time ◽  
Data Set ◽  
Fluid Saturation

The controlled-source electromagnetic (CSEM) method has been successfully applied to petroleum exploration; however, less effort has been made to highlight the applicability of this technique for reservoir monitoring. This work appraises the ability of time-lapse CSEM data to detect the changes in fluid saturation during water flooding into an oil reservoir. We simulated a poorly consolidated shaly sandstone reservoir based on a prograding near-shore depositional environment. Starting with an effective porosity model simulated by Gaussian geostatistics, dispersed clay and dual water models were efficiently combined with other well-known theoretical and experimental petrophysical correlations to consistently simulate reservoir properties. The constructed reservoir model was subjected to numerical simulation of multiphase fluid flow to predict the spatial distributions of fluid pressure and saturation. A geologically consistent rock physics model and a modified Archie’s equation for shaly sandstones were then used to simulate the electrical resistivity, showing up to 60% decreases in electrical resistivity due to changes in water saturation during 10 years of production. Time-lapse CSEM data were simulated at three production time steps (zero, five, and ten years) using a 2.5D parallel adaptive finite element algorithm. Analysis of the time-lapse signal in the simulated multicomponent and multifrequency data set demonstrates that a detectable time-lapse signal after five years and a strong time-lapse signal after ten years of water flooding are attainable using current CSEM technology.

Anisotropic correction of sonic logs in wells with large relative dip

Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. E1-E5 ◽  
Author(s):  
Lev Vernik
Keyword(s):  
Reservoir Characterization ◽  
Low Frequency ◽  
P Wave ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Avo Inversion ◽  
Pore Pressure Prediction ◽  
Siliciclastic Rocks ◽  
Dip Angle ◽  
Sonic Logs

Seismic reservoir characterization and pore-pressure prediction projects rely heavily on the accuracy and consistency of sonic logs. Sonic data acquisition in wells with large relative dip is known to suffer from anisotropic effects related to microanisotropy of shales and thin-bed laminations of sand, silt, and shale. Nonetheless, if anisotropy parameters can be related to shale content [Formula: see text] in siliciclastic rocks, then I show that it is straightforward to compute the anisotropy correction to both compressional and shear logs using [Formula: see text] and the formation relative dip angle. The resulting rotated P-wave sonic logs can be used to enhance time-depth ties, velocity to effective stress transforms, and low-frequency models necessary for prestack seismic amplitude variation with offset (AVO) inversion.

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.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

scholarly journals Time-Lapse Seismic Monitoring of Onshore Reservoirs in Niger Delta, Field ‘K’ as a Case Study

2017 ◽  
Vol 1 (1) ◽  
pp. 23-27 ◽  
Author(s):  
A. Ogbamikhumi ◽  
T. Tralagba ◽  
E. E. Osagiede
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Niger Delta ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Survey ◽  
Impedance Change ◽  
Data Set ◽  
Reservoir Fluid ◽  
4D Seismic

Field ‘K’ is a mature field in the coastal swamp onshore Niger delta, which has been producing since 1960. As a huge producing field with some potential for further sustainable production, field monitoring is therefore important in the identification of areas of unproduced hydrocarbon. This can be achieved by comparing production data with the corresponding changes in acoustic impedance observed in the maps generated from base survey (initial 3D seismic) and monitor seismic survey (4D seismic) across the field. This will enable the 4D seismic data set to be used for mapping reservoir details such as advancing water front and un-swept zones. The availability of good quality onshore time-lapse seismic data for Field ‘K’ acquired in 1987 and 2002 provided the opportunity to evaluate the effect of changes in reservoir fluid saturations on time-lapse amplitudes. Rock physics modelling and fluid substitution studies on well logs were carried out, and acoustic impedance change in the reservoir was estimated to be in the range of 0.25% to about 8%. Changes in reservoir fluid saturations were confirmed with time-lapse amplitudes within the crest area of the reservoir structure where reservoir porosity is 0.25%. In this paper, we demonstrated the use of repeat Seismic to delineate swept zones and areas hit with water override in a producing onshore reservoir.

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