Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 988-1001 ◽  
Author(s):  
T. Mukerji ◽  
A. Jørstad ◽  
P. Avseth ◽  
G. Mavko ◽  
J. R. Granli
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
Rock Physics ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Training Data ◽  
Well Log ◽  
Statistical Rock Physics

Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

scholarly journals Seismic Interpretation and Inversion Leading to an Accurate Reservoir Characterization in a Carbonate Environment

2021 ◽  
Vol 2 (12) ◽  
pp. 1229-1230
Author(s):  
Yasir Bashir ◽  
Nordiana Mohd Muztaza ◽  
Nur Azwin Ismail ◽  
Ismail Ahmad Abir ◽  
Andy Anderson Bery ◽  
...  
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Seismic Interpretation ◽  
And Inversion

Seismic data acquired in the field show the subsurface reflectors or horizon among the geological strata, while the seismic inversion converts this reflector information into the acoustic impedance section which shows the layer properties based on lithology. The research aims to predict the porosity to identify the reservoir which is in between the tight layer. So, the output of the seismic inversion is much more batter than the seismic as it is closer to reality such as geology. Seismic inversion is frequently used to determine rock physics properties, for example, acoustic impedance and porosity.

Seismic inversion of a carbonate buildup: A case study

2017 ◽  
Vol 5 (4) ◽  
pp. T641-T652 ◽  
Author(s):  
Mark Sams ◽  
Paul Begg ◽  
Timur Manapov
Keyword(s):  
Seismic Data ◽  
Probability Distributions ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Accurate Estimation ◽  
Amplitude Analysis ◽  
Simultaneous Inversion ◽  
Band Limited ◽  
Using Data

The information within seismic data is band limited and angle limited. Together with the particular physics and geology of carbonate rocks, this imposes limitations on how accurately we can predict the presence of hydrocarbons in carbonates, map the top carbonate, and characterize the porosity distribution through seismic amplitude analysis. Using data for a carbonate reef from the Nam Con Son Basin, Vietnam, the expectations based on rock-physics analysis are that the presence of gas can be predicted only when the porosity at the top of the carbonate is extremely high ([Formula: see text]), but that a fluid contact is unlikely to be observed in the background of significant porosity variations. Mapping the top of the carbonate (except when the top carbonate porosities are low) or a fluid contact requires accurate estimates of changes in [Formula: see text]. The seismic data do not independently support such an accurate estimation of sharp changes in [Formula: see text]. The standard approach of introducing low-frequency models and applying rock-physics constraints during a simultaneous inversion does not resolve the problems: The results are heavily biased by the well control and the initial interpretation of the top carbonate and fluid contact. A facies-based inversion in which the elastic properties are restricted to values consistent with the facies predicted to be present removes the well bias, but it does not completely obviate the need for a reasonably accurate initial interpretation in terms of prior facies probability distributions. Prestack inversion improves the quality of the facies predictions compared with a poststack inversion.

scholarly journals Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India

2021 ◽  
Author(s):  
Richa ◽  
S. P. Maurya ◽  
Kumar H. Singh ◽  
Raghav Singh ◽  
Rohtash Kumar ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Gamma Ray ◽  
Seismic Inversion ◽  
Reservoir Rock ◽  
Well Log ◽  
Log Data ◽  
Petrophysical Parameters ◽  
Model Based

AbstractSeismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation.

Seismic reservoir mapping from 3‐D AVO in a North Sea turbidite system

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 1157-1176 ◽  
Author(s):  
P. Avseth ◽  
T. Mukerji ◽  
A. Jørstad ◽  
G. Mavko ◽  
T. Veggeland
Keyword(s):  
Monte Carlo ◽  
North Sea ◽  
Seismic Data ◽  
Rock Physics ◽  
Well Log ◽  
Pore Fluids ◽  
Seismic Properties ◽  
Feeder Channel ◽  
Turbidite System ◽  
Seismic Amplitudes

We present a methodology for estimating uncertainties and mapping probabilities of occurrence of different lithofacies and pore fluids from seismic amplitudes, and apply it to a North Sea turbidite system. The methodology combines well log facies analysis, statistical rock physics, and prestack seismic inversion. The probability maps can be used as input data in exploration risk assessment and as constraints in reservoir modeling and performance forecasting. First, we define seismic‐scale sedimentary units which we refer to as seismic lithofacies. These facies represent populations of data (clusters) that have characteristic geologic and seismic properties. In the North Sea field presented in this paper, we find that unconsolidated thick‐bedded clean sands with water, plane laminated thick‐bedded sands with oil, and pure shales have very similar acoustic impedance distributions. However, the [Formula: see text] ratio helps resolve these ambiguities. We establish a statistically representative training database by identifying seismic lithofacies from thin sections, cores, and well log data for a type well. This procedure is guided by diagnostic rock physics modeling. Based on the training data, we perform multivariate classification of data from other wells in the area. From the classification results, we can create cumulative distribution functions of seismic properties for each facies. Pore fluid variations are accounted for by applying the Biot‐Gassmann theory. Next, we conduct amplitude‐variation‐with‐offset (AVO) analysis to predict seismic lithofacies from seismic data. We assess uncertainties in AVO responses related to the inherent natural variability of each seismic lithofacies using a Monte Carlo technique. Based on the Monte Carlo simulation, we generate bivariate probability density functions (pdfs) of zero‐offset reflectivity [R(0)] versus AVO gradient (G) for different facies combinations. By combining R(0) and G values estimated from 2‐D and 3‐D seismic data with the bivariate pdfs estimated from well logs, we use both discriminant analysis and Bayesian classification to predict lithofacies and pore fluids from seismic amplitudes. The final results are spatial maps of the most likely facies and pore fluids, and their occurrence probabilities. These maps show that the studied turbidite system is a point‐sourced submarine fan in which thick‐bedded clean sands are present in the feeder‐channel and in the lobe channels, interbedded sands and shales in marginal areas of the system, and shales outside the margins of the turbidite fan. Oil is most likely present in the central lobe channel and in parts of the feeder channel.

Incorporation of geology with rock physics enables subsalt poststack inversion: A case study in the Gulf of Mexico

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB53-WB65 ◽  
Author(s):  
Huyen Bui ◽  
Jennifer Graham ◽  
Shantanu Kumar Singh ◽  
Fred Snyder ◽  
Martiris Smith
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Blind Test ◽  
Frequency Model ◽  
Layer Sequence

One of the main goals of seismic inversion is to obtain high-resolution relative and absolute impedance for reservoir properties prediction. We aim to study whether the results from seismic inversion of subsalt data are sufficiently robust for reliable reservoir characterization. Approximately [Formula: see text] of poststack, wide-azimuth, anisotropic (vertical transverse isotropic) wave-equation migration seismic data from 50 Outer Continental Shelf blocks in the Green Canyon area of the Gulf of Mexico were inverted in this study. A total of four subsalt wells and four subsalt seismic interpreted horizons were used in the inversion process, and one of the wells was used for a blind test. Our poststack inversion method used an iterative discrete spike inversion method, based on the combination of space-adaptive wavelet processing to invert for relative acoustic impedance. Next, the dips were estimated from seismic data and converted to a horizon-like layer sequence field that was used as one of the inputs into the low-frequency model. The background model was generated by incorporating the well velocities, seismic velocity, seismic interpreted horizons, and the previously derived layer sequence field in the low-frequency model. Then, the relative acoustic impedance volume was scaled by adding the low-frequency model to match the calculated acoustic impedance logs from the wells for absolute acoustic impedance. Finally, the geological information and rock physics data were incorporated into the reservoir properties assessment for sand/shale prediction in two main target reservoirs in the Miocene and Wilcox formations. Overall, the poststack inversion results and the sand/shale prediction showed good ties at the well locations. This was clearly demonstrated in the blind test well. Hence, incorporating rock physics and geology enables poststack inversion in subsalt areas.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

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.

Characterization of a carbonate geothermal reservoir using rock-physics-guided deep neural networks

2021 ◽  
Vol 40 (10) ◽  
pp. 751-758
Author(s):  
Fabien Allo ◽  
Jean-Philippe Coulon ◽  
Jean-Luc Formento ◽  
Romain Reboul ◽  
Laure Capar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Deep Neural Networks ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Geothermal Reservoir ◽  
Rock Properties ◽  
Hybrid Techniques ◽  
Geothermal Wells

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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