Reservoir Prediction Under Control of Sedimentary Facies

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.

Stuck between a rock and a reflection: A tutorial on low-frequency models for seismic inversion

2017 ◽  
Vol 5 (2) ◽  
pp. B17-B27 ◽  
Author(s):  
Mark Sams ◽  
David Carter
Keyword(s):  
Elastic Properties ◽  
Model Building ◽  
Seismic Velocity ◽  
Low Frequency ◽  
Rock Physics ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Seismic Velocities ◽  
Frequency Model

Predicting the low-frequency component to be used for seismic inversion to absolute elastic rock properties is often problematic. The most common technique is to interpolate well data within a structural framework. This workflow is very often not appropriate because it is too dependent on the number and distribution of wells and the interpolation algorithm chosen. The inclusion of seismic velocity information can reduce prediction error, but it more often introduces additional uncertainties because seismic velocities are often unreliable and require conditioning, calibration to wells, and conversion to S-velocity and density. Alternative techniques exist that rely on the information from within the seismic bandwidth to predict the variations below the seismic bandwidth; for example, using an interpretation of relative properties to update the low-frequency model. Such methods can provide improved predictions, especially when constrained by a conceptual geologic model and known rock-physics relationships, but they clearly have limitations. On the other hand, interpretation of relative elastic properties can be equally challenging and therefore interpreters may find themselves stuck — unsure how to interpret relative properties and seemingly unable to construct a useful low-frequency model. There is no immediate solution to this dilemma; however, it is clear that low-frequency models should not be a fixed input to seismic inversion, but low-frequency model building should be considered as a means to interpret relative elastic properties from inversion.

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.

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.

Accurate poststack acoustic-impedance inversion by well-log calibration

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. R59-R67 ◽  
Author(s):  
Igor B. Morozov ◽  
Jinfeng Ma
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Joint Inversion ◽  
Low Frequency ◽  
Real Data ◽  
Data Sets ◽  
Frequency Model ◽  
Physical Constraints ◽  
Impedance Inversion ◽  
Reliable Solution

The seismic-impedance inversion problem is underconstrained inherently and does not allow the use of rigorous joint inversion. In the absence of a true inverse, a reliable solution free from subjective parameters can be obtained by defining a set of physical constraints that should be satisfied by the resulting images. A method for constructing synthetic logs is proposed that explicitly and accurately satisfies (1) the convolutional equation, (2) time-depth constraints of the seismic data, (3) a background low-frequency model from logs or seismic/geologic interpretation, and (4) spectral amplitudes and geostatistical information from spatially interpolated well logs. The resulting synthetic log sections or volumes are interpretable in standard ways. Unlike broadly used joint-inversion algorithms, the method contains no subjectively selected user parameters, utilizes the log data more completely, and assesses intermediate results. The procedure is simple and tolerant to noise, and it leads to higher-resolution images. Separating the seismic and subseismic frequency bands also simplifies data processing for acoustic-impedance (AI) inversion. For example, zero-phase deconvolution and true-amplitude processing of seismic data are not required and are included automatically in this method. The approach is applicable to 2D and 3D data sets and to multiple pre- and poststack seismic attributes. It has been tested on inversions for AI and true-amplitude reflectivity using 2D synthetic and real-data examples.

Accuracy of wavelets, seismic inversion, and thin-bed resolution

2017 ◽  
Vol 5 (4) ◽  
pp. T523-T530
Author(s):  
Ehsan Zabihi Naeini ◽  
Mark Sams
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Frequency Content ◽  
Frequency Model ◽  
North West ◽  
The North ◽  
Well Data

Broadband reprocessed seismic data from the North West Shelf of Australia were inverted using wavelets estimated with a conventional approach. The inversion method applied was a facies-based inversion, in which the low-frequency model is a product of the inversion process itself, constrained by facies-dependent input trends, the resultant facies distribution, and the match to the seismic. The results identified the presence of a gas reservoir that had recently been confirmed through drilling. The reservoir is thin, with up to 15 ms of maximum thickness. The bandwidth of the seismic data is approximately 5–70 Hz, and the well data used to extract the wavelet used in the inversion are only 400 ms long. As such, there was little control on the lowest frequencies of the wavelet. Different wavelets were subsequently estimated using a variety of new techniques that attempt to address the limitations of short well-log segments and low-frequency seismic. The revised inversion showed greater gas-sand continuity and an extension of the reservoir at one flank. Noise-free synthetic examples indicate that thin-bed delineation can depend on the accuracy of the low-frequency content of the wavelets used for inversion. Underestimation of the low-frequency contents can result in missing thin beds, whereas underestimation of high frequencies can introduce false thin beds. Therefore, it is very important to correctly capture the full frequency content of the seismic data in terms of the amplitude and phase spectra of the estimated wavelets, which subsequently leads to a more accurate thin-bed reservoir characterization through inversion.

Quantitative interpretation using facies-based seismic inversion

2017 ◽  
Vol 5 (3) ◽  
pp. SL1-SL8 ◽  
Author(s):  
Ehsan Zabihi Naeini ◽  
Russell Exley
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Quantitative Interpretation ◽  
Frequency Model ◽  
Amplitude Variation With Offset ◽  
Limited Bandwidth ◽  
Seismic Amplitude ◽  
Primary Signal

Quantitative interpretation (QI) is an important part of successful exploration, appraisal, and development activities. Seismic amplitude variation with offset (AVO) provides the primary signal for the vast majority of QI studies allowing the determination of elastic properties from which facies can be determined. Unfortunately, many established AVO-based seismic inversion algorithms are hindered by not fully accounting for inherent subsurface facies variations and also by requiring the addition of a preconceived low-frequency model to supplement the limited bandwidth of the input seismic. We apply a novel joint impedance and facies inversion applied to a North Sea prospect using broadband seismic data. The focus was to demonstrate the significant advantages of inverting for each facies individually and iteratively determine an optimized low-frequency model from facies-derived depth trends. The results generated several scenarios for potential facies distributions thereby providing guidance to future appraisal and development decisions.

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.

High-resolution acoustic impedance inversion to characterize turbidites at Marlim Field, Campos Basin, Brazil

2014 ◽  
Vol 2 (3) ◽  
pp. T143-T153 ◽  
Author(s):  
Tatiane M. Nascimento ◽  
Paulo T. L. Menezes ◽  
Igor L. Braga
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Structural Information ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Background Model ◽  
Rock Properties ◽  
High Porosity ◽  
Campos Basin

Seismic inversion is routinely used to determine rock properties, such as acoustic impedance and porosity, from seismic data. Nonuniqueness of the solutions is a major issue. A good strategy to reduce this inherent ambiguity of the inversion procedure is to introduce stratigraphic and structural information a priori to better construct the low-frequency background model. This is particularly relevant when studying heterogeneous deepwater turbidite reservoirs that form prolific, but complex, hydrocarbon plays in the Brazilian offshore basins. We evaluated a high-resolution inversion workflow applied to 3D seismic data at Marlim Field, Campos Basin, to recover acoustic impedance and porosity of the turbidites reservoirs. The Marlim sandstones consist of an Oligocene/Miocene deepwater turbidite system forming a series of amalgamated bodies. The main advantage of our workflow is to incorporate the interpreter’s knowledge about the local stratigraphy to construct an enhanced background model, and then extract a higher resolution image from the seismic data. High-porosity zones were associated to the reservoirs facies; meanwhile, the nonreservoir facies were identified as low-porosity zones.

Modeling processed seismic data to improve seismic facies prediction

2015 ◽  
Vol 3 (4) ◽  
pp. SAC91-SAC98 ◽  
Author(s):  
Adrian Pelham
Keyword(s):  
Seismic Data ◽  
Confusion Matrix ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Facies ◽  
Reservoir Modeling ◽  
Elastic Parameters ◽  
Frequency Model ◽  
Lithology Prediction ◽  
Well Data

Interpreters need to screen and select the most geologically robust inversion products from increasingly larger data volumes, particularly in the absence of significant well control. Seismic processing and inversion routines are devised to provide reliable elastic parameters ([Formula: see text] and [Formula: see text]) from which the interpreter can predict the fluid and lithology properties. Seismic data modeling, for example, the Shuey approximations and the convolution inversion models, greatly assist in the parameterization of the processing flows within acceptable uncertainty limits and in establishing a measure of the reliability of the processing. Joint impedance facies inversion (Ji-Fi®) is a new inversion methodology that jointly inverts for acoustic impedance and seismic facies. Seismic facies are separately defined in elastic space ([Formula: see text] and [Formula: see text]), and a dedicated low-frequency model per facies is used. Because Ji-Fi does not need well data from within the area to define the facies or depth trends, wells from outside the area or theoretical constraints may be used. More accurate analyses of the reliability of the inversion products are a key advance because the results of the Ji-Fi lithology prediction may then be quantitatively and independently assessed at well locations. We used a novel visual representation of a confusion matrix to quantitatively assess the sensitivity and uncertainty in the results when compared with facies predicted from the depth trends and well-elastic parameters and the well-log lithologies observed. Thus, using simple models and the Ji-Fi inversion technique, we had an improved, quantified understanding of our data, the processes that had been applied, the parameterization, and the inversion results. Rock physics could further transform the elastic properties to more reservoir-focused parameters: volume of shale and porosity, volumes of facies, reservoir property uncertainties — all information required for interpretation and reservoir modeling.

Application of Seismic Inversion Technique Base on Geostatistics in Prediction of Thin Sand Body Reservoir

2014 ◽  
Vol 1030-1032 ◽  
pp. 724-727
Author(s):  
Chun Lei Li ◽  
Wen Qi Zhang ◽  
Zhao Hui Xia ◽  
Ming Zhang ◽  
Liang Chao Qu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Inversion Method ◽  
Reservoir Model ◽  
Reservoir Prediction ◽  
Inversion Technique ◽  
Pulse Inversion ◽  
Sand Body ◽  
Geostatistical Inversion

Seismic inversion methods include constrained sparse pulse inversion and band limit inversion, etc. Although resolution of the seismic inversion results is higher than seismic data, it does not identify thin interbedding sand body and confirm the development of reservoirs. In this paper, in A block of Indonesia adopted geostatistical inversion in reservoir prediction, which is a method of seismic inversion combining geological statistics simulation and seismic inversion. This inversion method can establish various 3D geological model with the same probability of rock properties and lithology and it obey all seismic, logging and geological data. Using statistical regularity and seismic inversion technique we can obtain more fine reservoir model and finally reach the purpose of identification of single thin sand layer.

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