Quantitative Seismic Interpretation Research Articles

Prediction of porosity and gas saturation for deep-buried sandstone reservoirs from seismic data using an improved rock-physics model

In recent years, many important discoveries have been made in global deep oil and gas exploration, which indicates that deep exploration has gradually become one of the most important areas in current and future hydrocarbon exploration. However, the prediction of deep reservoirs is very challenging due to their low porosity and complex pore structure characteristics caused by the burial depth and diagenesis. Rock physics provides a link between the geologic reservoir parameters and seismic elastic properties and has evolved to become a key tool of quantitative seismic interpretation. Based on the mineral component and pore structure analysis of studied rocks, we propose an improved rock-physics model by introducing a third feldspar-related pore for deep-buried sandstone reservoirs to the traditional Xu–White model. This modelling process consists of three steps: first, rock matrix modelling using time-average equations; second, dry rock modelling using a multi-pore analytical approximation; and third, fluid-saturated rock modelling using a patchy distribution. It has been used in total porosity estimation, S-wave velocity prediction and rock-physics template establishment. The applicability of the improved rock-physics model is verified by a theoretical quartz-water model test and a real data total porosity estimation compared with the traditional Xu–White model and the density method. Then, a rock-physics template is generated by the improved rock-physics model for porosity and gas saturation prediction using seismic data. This template is carefully calibrated and validated by well-log data at both the well-log scale and seismic scale. Finally, the feasibility of the established rock-physics template for porosity and gas saturation prediction is validated by a deep-buried sandstone reservoir application in the East China Sea.

Application of the power mean to modeling the elastic properties of reservoir rocks

The relationship between seismic velocity and porosity is one of the most important research topics in exploration geophysics. A strong correlation, if it exists, can be very useful for quantitative seismic interpretation of direct hydrocarbon indicators. The Voigt and Reuss bounds are often plotted alongside the data for quality control and interpretation. The relative position of the data within the bounds may supply important information regarding the pore shape, stress condition, lithology, and diagenesis of porous rocks. It is found that the Voigt bound and Reuss bound are actually special cases of the weighted power mean, with the power parameters being 1 and −1, respectively. The geometric mean is a special case of the power mean with the power parameter being zero. The power mean is a monotonic function of the power parameter. For the construction of a rock physics template, the region between the Voigt and Reuss bounds can be evenly divided by varying the power parameter between −1 and 1. The power mean can be a very useful tool for modeling the elastic properties of rocks. We have demonstrated the potential applications of the power mean in the studies of velocity-porosity relation, effective media, and pore fluids effect on seismic velocities.

English

The Niger Delta is a sedimentary deposit with continuing sediment deposition to date. In a stratified earth, seismic waves tend to propagate faster along layers than across layer boundaries. Shales, due to the clay minerals they contain, exhibit similar behavior. As a result, velocities derived from surface seismic are often faster than well-derived velocities, causing depths of structures from surface seismic interpretations to be much shallower than their true depths. Reservoir characterization workflows such as amplitude-versus offset (AVO) analysis, seismic inversion and pore pressure prognosis, which make use of seismic velocities to accurately delineate and define exploration targets, would be seriously impacted if velocity anisotropy is not quantified and the seismically-derived velocities corrected. Accurate quantification and correction of velocity anisotropy requires the use of offset vertical seismic profile (VSP) data to aid the estimation of the Thomsen’s anisotropic parameters, and . Unfortunately, such data are rarely acquired in many exploration projects and when they are available, they are often limited in areal coverage. In this study, an integrated approach using well and seismic data, based on the degree to which check shot and stacking interval velocity trends diverge with increasing depths, was utilized to quantify and correct seismically-derived velocity in AMFO field for improved quantitative seismic interpretation. Estimated anisotropy is between 2 and 22%, and becomes apparent at about 1,100 m true vertical depth subsea (TVDss). Key words: Velocity anisotropy, interval velocity, vertical seismic profile (VSP), anisotropic parameters, well velocity.

A seismic reservoir characterization and porosity estimation workflow to support geological model update: pre-salt reservoir case study, Brazil

Abstract Quantitative seismic interpretation plays an important and growing role for reservoir characterization, as seismic data become increasingly reliable as a result of the latest advances in acquisition and processing techniques.

Integrating statistical rock physics and pressure and thermal history modeling to map reservoir lithofacies in the deepwater Gulf of Mexico

We have developed an approach integrating statistical rock physics with pressure and thermal history modeling for quantitative seismic interpretation (QSI). Extending the training data for lithofacies classification and deriving distributions for scenarios not available in the original training require knowledge about geologic processes affecting the elastic properties in the subsurface. We model pressure and thermal history and corresponding smectite to illite diagenesis with a basin model across Thunder Horse minibasin in the Gulf of Mexico. By comparing the mapped lithofacies with and without basin-modeling extrapolations against the results of a reference workflow, we found the value of integrating basin modeling results and statistical rock physics with QSI workflows. The reference workflow uses all available data from two wells in the QSI. The first workflow performs the same lithofacies classification with data from only a single well and does not account for spatial trends away from the well. In the second workflow, we use data from only a single well, the same well as in the first workflow, and bring in extrapolation from the basin and petroleum system modeling at the location of the second well. Results for the first workflow indicate significant differences with the reference workflow in the training data, the quality of the inverted impedance volumes, and the classified reservoir lithofacies. In the second workflow, the guided extrapolation of the training data accounts for spatial trends away from the well and the quality of the impedance inversion significantly improves. The predicted lithofacies map in this scenario shows only minor differences from the reference workflow, and the posterior probabilities of lithofacies show less uncertainty compared with the first workflow. The superiority of the second workflow demonstrates the added value of the integration workflow to QSI in cases of spatially limited well control.

An enhanced broad-frequency-band apparatus for dynamic measurement of elastic moduli and Poisson's ratio of rock samples.

We built a broad-frequency-band measurement system for rock elastic parameters based on the stress-strain method following Batzle et al., Geophysics 71, N1-N9 (2006). The system gives strain amplitude anomalies at some measurement frequencies. These anomalies put limitations on the range of the measurement frequency and jeopardize the credibility of the measurement results over a broad frequency band. To overcome these limitations, we investigated the cause of these anomalous strains by numerical model simulations with a finite element method based on the experimental apparatus. Through the systematic analysis of the modeling results, we conclude that the resonances caused by non-axial perturbations lead to such anomalous measurement results. Based on the analysis, we give a solution to reduce the effect of the resonances and shift the first resonance frequency beyond the frequency band of 1-2000 Hz. The enhanced measurement system can provide robust and reliable measurements on the elastic parameters of rocks between 1 and 2000 Hz, which is crucial for a quantitative study of the frequency-dependent phenomenon related to fluid effects. This in turn will provide a powerful tool for the experimental characterization of elastic properties of oil/gas reservoir rocks, thus laying a solid foundation for low-frequency rock physics analysis and quantitative seismic interpretation.

Multiphysics characterization of reservoir prospects in the Hoop area of the Barents Sea

The inherent nonuniqueness of geophysical analysis can mean that interpretations based only on a single geophysical measurement can be ambiguous or uncertain. We have developed a case study from the Hoop area of the Barents Sea, in which prestack seismic, well-log, and controlled-source electromagnetic (CSEM) data were integrated within a rock-physics framework to provide a more robust assessment of the prospectivity of the area than could be obtained by seismic analysis alone. In this example, although quantitative seismic interpretation identified potentially hydrocarbon-bearing sands, the saturation was uncertain. In this area and at shallow depths, the main focus is on (very) high oil saturations. Adding the CSEM data in this setting allows us to distinguish between high saturations ([Formula: see text]) and low and medium saturations ([Formula: see text]): It is clear that saturations similar to those observed at the nearby Wisting well ([Formula: see text]) are not present in this area. However, because of limitations on the sensitivity of the CSEM data in this high-resistivity environment, it is not possible to distinguish between low and medium saturations. This remains an uncertainty in the analysis. Based on the resulting downgrade of the main prospect Maya and the limited additional high-risk prospectivity at other stratigraphic levels, the partnership agreed to surrender the license.

Quantitative seismic interpretation integrated with well-test analysis in turbidite and presalt reservoirs

Advances in acquisition techniques and in processing workflows have made seismic amplitude more reliable, and, consequently, the quantitative information has become more trustworthy. Rock physics and seismic inversion are essential tools for reservoir characterization. Rock physics improves our knowledge about how the variations of reservoir properties affect the seismic response. Seismic inversion is a tool to obtain 3D volume of elastic properties. Combining the static information from seismic and well data is the first approach for reservoir characterization. Data from core, well log, and laboratory tests help to mitigate the risk associated with seismic interpretation. Additionally, geophysicists may rely on dynamic data such as pressure, production rates, saturation, temperature, and fluid properties whenever available. The pressure transient analysis obtained from well tests brings deeper reservoir insights. Estimations of the permeability profile, flow barrier character, and hydraulic compartmentation are some of the possible outcomes. Although the seismic signal and well test have different responses from the reservoir behavior, both can be combined to reduce the ambiguity in seismic interpretation and to evaluate different reservoir scenarios. Assessments among different facies and pore-volume scenarios are also compatible with the dynamic and static data. Turbidite and presalt reservoirs are presented as evidence of how joining geophysical, geologic, and reservoir engineering information, from static and dynamic sources, reduces the risk in quantitative seismic interpretation, thereby leading to more reliable reservoir production forecasting.

The value of constrained conjugate-gradient least-squares migration in seismic inversion: Application to a fractured-basement play, Texas Panhandle

Seismic inversion has become almost routine in quantitative 3D seismic interpretation. To ensure the quality of the seismic inversion, the input seismic data need to have a high signal-to-noise ratio. With the current low oil price environment, seismic reprocessing is often preferred over reacquisition to improve data quality. Common filter pairs include forward and inverse [Formula: see text]-[Formula: see text] and Radon transforms. Forward and inverse migrations (i.e., migration and demigration) are a more recently introduced transform pair that, when used together in an iterative workflow, results in a least-squares migration algorithm. Least-squares migration compensates for surface variation in data density and, when combined with a filter applied to prestack migrated images, suppresses the operator and data aliasing. We apply a least-squares migration workflow to a fractured-basement data set from the Texas Panhandle to demonstrate the enhancement in signal-to-noise ratio, the reduction in acquisition footprint and migration artifacts, and the improvement in the P-impedance inversion result.

Introduction to special section: Seismic inversion — Conventional seismic impedance inversion and advanced seismic inversion techniques: Developments, workflow, and case studies

Seismic inversion is a key component of the quantitative seismic interpretation workflows for hydrocarbons exploration and production. In exploration with limited well control, seismic inversion for acoustic impedance helps to understand the spatial variations and identify potential “pay zones

Seismic rock physics

Rock physics studies the relationship between physical and elastic properties of rocks and is the basis of quantitative seismic interpretation. It has arguably given exploration geophysicists a solid quantitative basis to their interpretation of seismic data. It can be tackled at different scales of investigation by people with various backgrounds — from geophysicists to civil engineers, from mathematicians to petrophysicists.

Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification

Pattern recognition-based seismic facies analysis techniques are commonly used in modern quantitative seismic interpretation. However, interpreters often treat techniques such as artificial neural networks and self-organizing maps (SOMs) as a “black box” that somehow correlates a suite of attributes to a desired geomorphological or geomechanical facies. Even when the statistical correlations are good, the inability to explain such correlations through principles of geology or physics results in suspicion of the results. The most common multiattribute facies analysis begins by correlating a suite of candidate attributes to a desired output, keeping those that correlate best for subsequent analysis. The analysis then takes place in attribute space rather than ([Formula: see text], [Formula: see text], and [Formula: see text]) space, removing spatial trends often observed by interpreters. We add a stratigraphy layering component to a SOM model that attempts to preserve the intersample relation along the vertical axis. Specifically, we use a mode decomposition algorithm to capture the sedimentary cycle pattern as an “attribute.” If we correlate this attribute to the training data, it will favor SOM facies maps that follow stratigraphy. We apply this workflow to a Barnett Shale data set and find that the constrained SOM facies map shows layers that are easily overlooked on traditional unconstrained SOM facies map.

Seismic inversion as a predictive tool for porosity and facies delineation in Paleocene fluvial/lacustrine reservoirs, Melut Basin, Sudan

The Melut Basin is a rift basin in the interior Sudan linked to the Mesozoic-Cenozoic Central and Western African Rift System. The Paleocene Yabus Formation is the main reservoir deposited in heterogeneous fluvial/lacustrine environment. Delineation of channel sandstone from shale is a challenge in reservoir exploration and development. We demonstrate a detailed 3D quantitative seismic interpretation approach that integrates petrophysical properties derived from well logs analysis. A porosity transform of acoustic impedance inversion provided a link between elastic and rock properties. Thus, we used seismic porosity to discriminate between different facies with appropriate validation by well logs. At the basin scale, the results revealed lateral and vertical facies heterogeneity in the Melut Basin. Good reservoir quality is observed in the Paleocene Yabus Formation. The sand facies indicated high porosity (20%) corresponding to low acoustic impedance (20000–24000 g ft/(cm3.s)). However, lower quality reservoir is observed in the Cretaceous Melut Formation. The porosity of sand/shale facies is low (5%), corresponding to high acoustic impedance (29000–34000 g ft/(cm3.s)). This suggests that the Yabus Sandstone is potentially forming a better reservoir quality than Melut Formation. At the reservoir scale, we evaluated the facies quality of Yabus Formation subsequences using petrophysical analysis. The subsequences YB1 to YB3, YB4 to YB7 and YB8 to YB10 showed relatively similar linear regressions, respectively. The subsequence of YB4 to YB7 is considered the best reservoir with higher porosity (25%). However, subsequence YB1 to YB3 showed lower reservoir quality with higher shale volume (30%). This attributed to floodplain shale deposits in this subsequence. Similarly, the high porosity (20%) recognized in deeper subsequences YB6 to YB9 is due to clean sand facies. We learnt a lesson that appropriate seismic preconditioning, exhaustive petrophysical analysis and well log validation are important keys for improved reservoir quality prediction results in fluvial/lacustrine basins.

Reservoir-property inversion — A method for quantitative interpretation of seismic-inversion results

With improved seismic data quality, prestack inversion has become a routine process for quantitative seismic interpretation. However, direct products from traditional seismic inversion usually are P-impedance (PI), S-impedance (SI), and, in some cases, density. These elastic properties are only an indirect description of subsurface geology. A bridge must be established from inverted PI, SI, and density to more understandable reservoir properties: lithology, porosity, and water saturation. Reservoir-property inversion is a model-based inversion process to transform PI, SI, and density to lithology, porosity, and water saturation. The inversion is performed in two steps: (1) well-log inversion on log data to estimate optimal elastic properties of rock-grain constituents and (2) reservoir-property inversion to estimate reservoir properties from PI, SI, and density. The underlying rock-physics models are the same for both inversions including mass–balance equation, Gassmann equation, Voigt-Reuss-Hill average, and Krief's relationship, or, optionally, the Xu-White velocity model. The solution of the inversion is considered as optimal in terms of minimum misfit of PI, SI, and density modeled with inverted reservoir properties compared to the input PI, SI, and density. The inversion results honor all the interrelationships between various elastic properties, reservoir properties, and rock-grain properties. A limitation of the proposed inversion includes a requirement for lithology with only two solid constituents, such as sand mixed with shale. It also requires a density volume as one of the primary input data for the inversion. Due to the inversion's sensitivity to fluid contents, estimated water saturation in many cases may not be reliable. This paper presents the inversion methodology and the inversion results from a set of modeled data as well as a real case study to demonstrate the inversion's capability.

Defining the 3D geometry of thin shale units in the Sleipner reservoir using seismic attributes

Defining the 3D geometry and internal architecture of reservoirs is important for prediction of hydrocarbon volumes, petroleum production and storage potential. Many reservoirs contain thin shale layers that are below seismic resolution, which act as impermeable and semi-permeable layers within a reservoir. Predicting the storage volume of a reservoir with thin shale layers from conventional seismic data is an issue due to limited seismic resolution. Further, gas chimneys indicative of gas migration pathways through thin shale layers, are not easily defined by conventional seismic data. Additional information, such as borehole data, can be used to aid mapping of shale layers, but making lateral predictions from 1D borehole data has high uncertainty. This paper presents an integrated workflow for quantitative seismic interpretation of thin shale layers and gas chimneys in the Utsira Formation of the Sleipner reservoir. The workflow combines the use of attribute and spectral analysis to add resolution to conventional seismic amplitude data. Detailed interpretation of these analyses reveals the reservoirs internal thin shale architecture, and the presence of gas chimneys. The comprehensive interpretation of the reservoirs internal structure is used to calculate a new reservoir storage volume. This is done based on the distribution of sand and interpreted shale layers within the study area, for this active CO2 storage site.

Quantitative interpretation using inverse rock-physics modeling on AVO data

Quantitative seismic interpretation has become an important and critical technology for improved hydrocarbon exploration and production. However, this is typically a resource-demanding process that requires information from several well logs, building a representative velocity model, and, of course, high-quality seismic data. Therefore, it is very challenging to perform in an exploration or appraisal phase with limited well control. Conventional seismic interpretation and qualitative analysis of amplitude variations with offset (AVO) are more common tools in these phases. Here, we demonstrate a method for predicting quantitative reservoir properties and facies using AVO data and a rock-physics model calibrated with well-log data. This is achieved using a probabilistic inversion method that combines stochastic inversion with Bayes' theorem. The method honors the nonuniqueness of the problem and calculates probabilities for the various solutions. To evaluate the performance of the method and the quality of the results, we compare them with similar reservoir property predictions obtained using the same method on seismic-inversion data. Even though both approaches use the same method, the input data have some fundamental differences, and some of the modeling assumptions are not the same. Considering these differences, the two approaches produce comparable predictions. This opens up the possibility to perform quantitative interpretation in earlier phases than what is common today, and it might provide the analyst with better control of the various assumptions that are introduced in the work process.

Sensitivity of P- and S-impedance to the presence of kerogen in the Eagle Ford Shale

The amplitude variation with offset (AVO) of seismic waves reflected from an interface between two geologic layers is a function of the P-velocity, S-velocity, and density of the layers and can be used to obtain information on lithology, porosity, fluid content, and other formation parameters. Density, however, often is poorly resolved. Parameterization of AVO in terms of P-impedance, S-impedance, and density is better than parameterization in terms of P-velocity, S-velocity, and density because uncertainty in the density estimate has a greater impact on errors in estimates of P- and S-velocity than on errors in P- and S-impedance. For this reason, quantitative seismic interpretation makes use of rock-physics templates formulated in terms of P- and S-impedance. An investigation of the ability of rock-physics templates to distinguish between organic-rich and organic-lean shales uses log data from the Eagle Ford Shale and shows the effect of kerogen on in situ stress determination.

Reservoir property estimation using only dual-sensor seismic data – a case study from the West of Shetlands, UKCS

Marine broadband towed streamer seismic analysis and case studies in recent years have revealed several benefits of extended seismic bandwidth for the reservoir geoscientist – both for structural and quantitative seismic interpretation. Since 2007, dual-sensor towed streamer seismic technology (Tenghamn et al., 2007) has provided industry-wide access to seismic data with a significantly broader range of low and high frequencies. Benefits on the interpretation side include improved vertical resolution, enhanced geological texture, cleaner event continuity and character, sharper fault plane truncations and improved seismic-to-well correlations. The key benefit for quantitative seismic interpretation (QI) is a significant reduction of the low frequency model requirement and this allows seismic inversion to be derived from the seismic data rather than from a model extrapolation of a priori information. In addition, the quality and prediction of the reservoir properties derived from seismic data, without well log information, has significantly improved – resulting in the potential de-risking of prospects and discoveries. This paper describes the data analysis of dual-sensor 3D seismic from the West of Shetlands area and specifically focuses on wavelet extraction and low frequency phase stability. The study explores the relevance of these aspects to the inversion and investigates the estimation of reservoir properties, including porosity prediction, without the direct use of well information. The match of results from seismic only inversion to ‘blind wells’ demonstrates the potential of dual-sensor pre-stack data to reliably estimate elastic and reservoir properties in an exploration setting. Estimating reliable absolute reservoir properties away from wells has been a continuous challenge for reservoir geoscientists. Where it can be achieved, the value of the information can be significant in de-risking an opportunity and/or better characterising a prospect. In addition, in appraisal, development and reservoir optimisation, any reliable elastic information extracted from seismic data can potentially assist in optimising a well location and its trajectory for maximum recovery.

Estimating subsurface uncertainties by combining seismic interpretation with earth modeling

Earth models are routinely used in the oil and gas industry to integrate multidisciplinary data for subsurface property predictions. Even though most earth models predict reasonably well at the field scale, they often fail to accurately predict the subsurface conditions at a specific location, especially in geologically complex reservoirs. Earth models can become more predictive by integrating information routinely extracted from 3D seismic such as faults, stratigraphy, facies, and rock properties. But their integration into earth models is often done without accounting for their uncertainties, potentially leading to the misprediction of the subsurface properties. We aimed to review several seismic interpretation techniques that provided useful input to better constrain earth models and to suggest ways to account for the interpretation uncertainty in these models. We determined the pitfalls and practical solutions for a successful quantitative seismic interpretation that can lead to more predictive earth models.

Quantitative seismic interpretations to detect biogenic gas accumulations: a case study from Qaidam Basin, China

Abstract Quantitative seismic interpretation can be used to identify lithology and detect petroleum accumulations by integrating rock properties and attributes derived from advanced seismic inversion methods with existing petrophysical data and geological knowledge. We use quantitative seismic interpretations for detection of shallow biogenic gas accumulations in the Qaidam Basin, China, employing an integrated workflow that incorporates petrophysical data, seismic attribute analysis, Constrained Simultaneous Inversion (C-SI) and Bayesian-based Support Vector Machine (B-SVM) inference. Previous petrophysical studies have shown that it is challenging to effectively identify gas-bearing intervals using parameters such as impedance, Poisson’s ratio and porosity, because the reservoir sediments are unconsolidated and at shallow depths. The resistivity well-log response remains as an effective tool for estimating gas saturation and identifying gas-bearing intervals. In this study, we propose the use of the petroleum pore-volume, which is defined as the product of reservoir porosity and gas saturation, to detect biogenic gas accumulations seismically. Rock properties inferred from seismic inversion, such as compressional velocity (Vp), shear velocity (Vs) and density, cannot be used directly for petroleum pore-volume estimation. Therefore, we employ a Bayesian-based support vector machine approach to cross-link well-log properties, seismic AVO attributes and seismic rock properties to quantitatively predict petroleum pore-volume in 2D and 3D seismic dataset. Because seismic information is crucial to statistical inference, we propose C-SI to infer the Vp, Vs and density from seismic elastic impedance gathers, which can be generated from seismic gathers using a traditional recursive seismic inversion method. The C-SI procedures use the Interior-Point algorithm to optimize and solve elastic impedance equations. The Interior-Point method is a popular method for handling constrained non-convex, non-linear optimization problems that involve simultaneously inverting the seismic properties with thousands of seismic samples. This case study indicates that the integrated study workflow is useful for quantitatively predicting petroleum pore-volume, especially in the depth-domain, and that it is an excellent potential indicator for biogenic gas accumulations in complicated geological settings.