Seismic facies discrimination incorporating relative rock physics

2021 ◽  
Vol 40 (10) ◽  
pp. 734-741
Author(s):  
Di Liu ◽  
Changchun Zou ◽  
Yihang Chang ◽  
Ping Yang ◽  
Zhonghong Wan ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Seismic Facies ◽  
Quantitative Interpretation ◽  
Property Variation ◽  
Seismic Reflectivity ◽  
Projection Angle ◽  
Key Steps

Seismic facies discrimination is usually performed based on a rock-physics-driven quantitative interpretation approach. The accuracy of the study of rock physics largely impacts the reservoir and fluid recognition. However, the study is commonly conducted with absolute well logs without removing the trend effect. Such an approach may introduce inappropriate low-frequency information and bias further analysis of seismic data (crossplotting, facies probability density function generation, and projection angle determination). By contrast, relative rock physics with the trend decomposed reflects the rock-property variation of the overburden and underlying formation. The relative portions are more consistent with the seismic reflectivity, providing an alternative tool to facies interpretation through a seismic inversion scheme. A workflow for seismic facies discrimination has been investigated that incorporates relative rock physics, long short-term memory-based nonlinear seismic inversion, and Bayesian classification. This workflow is employed in a case study from Songliao Basin in northeast China, through which the results of relative and absolute approaches in key steps are analyzed and compared. The consistency of facies, determined through relative and absolute methods with petrophysical interpretation, is calculated. The relative analysis exhibits improved agreement with petrophysical interpretation in overall facies and reservoir sand discrimination of the blind wells. This indicates the potential to minimize the trend bias by integrating relative rock physics in quantitative interpretation.

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.

Seismic Quantitative Interpretation for Uncertainty Reduction of Subsurface Modeling of the Deep Visean Reservoirs

2021 ◽  
Author(s):  
Pavlo Kuzmenko ◽  
Rustem Valiakhmetov ◽  
Francesco Gerecitano ◽  
Viktor Maliar ◽  
Grigori Kashuba ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs ◽  
Quantitative Interpretation ◽  
Structural Interpretation ◽  
Elastic Inversion ◽  
Subsurface Modeling

Abstract The seismic data have historically been utilized to perform structural interpretation of the geological subsurface. Modern approaches of Quantitative Interpretation are intended to extract geologically valuable information from the seismic data. This work demonstrates how rock physics enables optimal prediction of reservoir properties from seismic derived attributes. Using a seismic-driven approach with incorporated prior geological knowledge into a probabilistic subsurface model allowed capturing uncertainty and quantifying the risk for targeting new wells in the unexplored areas. Elastic properties estimated from the acquired seismic data are influenced by the depositional environment, fluid content, and local geological trends. By applying the rock physics model, we were able to predict the elastic properties of a potential lithology away from the well control points in the subsurface whether or not it has been penetrated. Seismic amplitude variation with incident angle (AVO) and azimuth (AVAZ) jointly with rock-derived petrophysical interpretations were used for stochastical modeling to capture the reservoir distribution over the deep Visean formation. The seismic inversion was calibrated by available well log data and by traditional structural interpretation. Seismic elastic inversion results in a deep Lower Carboniferous target in the central part of the DDB are described. The fluid has minimal effect on the density and Vp. Well logs with cross-dipole acoustics are used together with wide-azimuth seismic data, processed with amplitude control. It is determined that seismic anisotropy increases in carbonate deposits. The result covers a set of lithoclasses and related probabilities: clay minerals, tight sandstones, porous sandstones, and carbonates. We analyzed the influence of maximum angles determination for elastic inversion that varied from 32.5 to 38.5 degrees. The greatest influence of the far angles selection is on the density. AI does not change significantly. Probably the 38,5 degrees provides a superior response above the carbonates. It does not seem to damage the overall AVA behavior, which result in a good density outcome, as higher angles of incidence are included. It gives a better tie to the wells for the high density layers over the interval of interest. Sand probability cube must always considered in the interpretation of the lithological classification that in many cases may be misleading (i.e. when sand and shale probabilities are very close to each other, because of small changes in elastic parameters). The authors provide an integrated holistic approach for quantitative interpretation, subsurface modeling, uncertainty evaluation, and characterization of reservoir distribution using pre-existing well logs and recently acquired seismic data. This paper underpins the previous efforts and encourages the work yet to be fulfilled on this subject. We will describe how quantitative interpretation was used for describing the reservoir, highlight values and uncertainties, and point a way forward for further improvement of the process for effective subsurface modeling.

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.

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.

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.

From qualitative to quantitative interpretation: An interpreter’s guide to fluid prediction in Pliocene to Turonian deepwater turbidites from West Africa to Asia Pacific

2014 ◽  
Vol 2 (1) ◽  
pp. SA127-SA140
Author(s):  
Uwe Strecker ◽  
Paola Vera de Newton ◽  
Maggie Smith
Keyword(s):  
West Africa ◽  
Rock Physics ◽  
Seismic Inversion ◽  
P Wave ◽  
Asia Pacific ◽  
Quantitative Interpretation ◽  
S Wave ◽  
Field Development ◽  
Exploration Risk ◽  
Basin Floor

To mitigate exploration risk in deepwater settings, subsurface analysis increasingly has to rely on integration of qualitative with quantitative techniques. To predict pay in turbidite sandstones, proven statistical and analytical methods can routinely be run on well and seismic inversion data. However, quantitative interpretation (QI) should begin with a responsible audit of available well logs and seismic data, succeeded by data conditioning, proceeding with quality control, and placing elastic attribute responses within their geologic context. To address these issues, we evaluate geologic controls on porosity change as manifested by overpressure and compaction on calibration and analysis of elastic attributes. Following calibration of seismic inversion data, we provide tutorial-style interpretations of deepwater clastic reservoirs from the Gulf of Guinea, West Africa, to the Sabah trough, Borneo. Case study examples offer interpreters the potential to use workflows surrounding data mining in exploration or during field development. In our first example, a comparison of univariate statistics run on compressional- and shear-wave impedances and Poisson’s ratio is introduced to potentially data mine 3D seismic over turbidite fairways. Joint interpretation of P-wave and S-wave impedances is combined with innovative uses of bivariate statistical analysis for anomaly detection. Additionally, the geologic rationale of interpreting elastic relationships of calibrated attributes, such as Lambda Rho and Mu Rho, is discussed on the seismic scale of a single reservoir layer using a combination of statistical methods and rock physics. Here, qualitative interpretation, via application of principles from seismic stratigraphy and seismic geomorphology, ultimately unlocks ambiguity in rock-physics-driven, quantitative lithology determination, guiding application of QI routines toward correctly predicting the prevailing fluid type. Elastic calibration permits seismic lithofacies classification of Cretaceous turbidite sandstones deposited as middle to lower slope channels canyon-fill and basin-floor channel complexes.

Quantitative interpretation for gas hydrate accumulation in the eastern Green Canyon Area, Gulf of Mexico using seismic inversion and rock physics transform

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. B139-B150 ◽  
Author(s):  
Zijian Zhang ◽  
De-hua Han ◽  
Qiuliang Yao
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Pore Space ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Bright Spot ◽  
Quantitative Interpretation ◽  
Free Gas ◽  
Qualitative And Quantitative ◽  
Amplitude Data

Gas hydrate can be interpreted from seismic data through observation of bottom simulating reflector (BSR). It is a challenge to interpret gas hydrate without BSR. Three-dimensional qualitative and quantitative seismic interpretations were used to characterize gas hydrate distribution and concentration in the eastern Green Canyon area of the Gulf of Mexico, where BSR is absent. The combination of qualitative and quantitative interpretation reduces ambiguities in the estimation and identification of gas hydrate. Sandy deposition and faults are qualitatively interpreted from amplitude data. The 3D acoustic impedance volume was interpreted in terms of high P-impedance hydrate zones and low P-impedance free gas zones. Gas hydrate saturation derived from P-impedance is estimated by a rock physics transform. We interpreted gas hydrate in the sand-prone sediments with a maximum saturation of approximately 50% of the pore space. Sheet-like and some bright spot gas hydrate accumulations are interpreted. The interpretation of sheet-like gas hydrate within sand-prone sediments around faults suggests that fluid moves into the sand zones laterally by conduits. Variations in depths of interpreted gas hydrate zones imply nonequilibrium conditions. Low P-impedance free gas zones within high P-impedance gas hydrate zones imply possible coexistence of hydrate and free gas within the hydrate stability zone. We propose that gas hydrate distribution and concentration are associated with structures, buried sedimentary bodies, sources of gas, and fluid flux.

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.

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