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Modeling is a computational process to determine the seismic response of a given geologic model and can be 1D, 2D and 3D and structural or stratigraphic. Synthetic seismogram is the most elementary and common example of one-dimensional (1D) modelled seismic response of the earth. Field of application, benefits and limitations of forward modeling are briefly stated with examples.

Fractures play an important role in most carbonate and unconventional reservoirs. The precise identification of fractures and their associated properties from seismic data have a significant impact on reservoir management and hydrocarbon recovery. Traditional seismic methods for fracture identification and characterization include shear birefringence and amplitude variations with offset and azimuth. These techniques are based on the equivalent medium theory, assuming that fracture dimensions and spacing are small relative to the seismic wavelength. Hence, the overall population of fractures is equivalent to a homogeneous anisotropic medium. We focus on discrete fracture sets, with spacing on the order of the seismic wavelength in which individual fractures have large areas but small apertures. Such fracture sets scatter seismic waves, and they are important to characterize because they affect fluid flow in the subsurface. Analyzing scattering-induced wave patterns observed in seismograms can provide crucial guidance for precise identification of fractures and their associated properties. We model seismic-wave propagation in fractured media following a published integral method with tetrahedral grid cells. This approach is derived from finite-element and finite-difference methods in heterogeneous media. It is flexible in modeling irregular interfaces and surface topography and has relatively low computational cost and memory requirements, which is essential for 3D simulations. The fractures are treated explicitly as interfaces with displacement discontinuities using the linear slip model. We implement the 3D explicit interface scheme using an irregular mesh. Our numerical examples demonstrate the effects of varying fracture height, spacing, and width on the seismic response. The wavefield scattering caused by the fractures is one of the key features. Accordingly, we establish a correlation between the scattering index and fracture spacing. Such an approach can be used to estimate fracture spacing with given fracture compliance and orientation.

Seismic inversion plays an important role in reservoir modelling and characterization due to its potential for assessing the spatial distribution of the subsurface petro-elastic properties. Seismic AVA inversion methodologies allow to retrieve P-wave and S-wave velocities and density individually allowing a better characterization of existing litho-fluid facies. We present an iterative geostatistical seismic AVA inversion algorithm that inverts pre-stack seismic data, sorted by angle gather, directly for: density; P-wave; and S-wave velocity models. The proposed iterative geostatistical inverse procedure is based on the use of stochastic sequential simulation and co-simulation algorithms as the perturbation technique of the model parameter space; and the use of a genetic algorithm as a global optimizer to make the simulated elastic models converge from iteration to iteration. All the elastic models simulated during the iterative procedure honour the marginal prior distributions of P-wave velocity, S-wave velocity and density estimated from the available well-log data, and the corresponding joint distributions between density versus P-wave velocity and P-wave versus S-wave velocity. We successfully tested and implemented the proposed inversion procedure on a pre-stack synthetic dataset, built from a real reservoir, and on a real pre-stack seismic dataset acquired over a deep-water gas reservoir. In both cases the results show a good convergence between real and synthetic seismic and reliable high resolution elastic subsurface Earth models. This article is protected by copyright. All rights reserved

A new stochastic seismic inversion method based on the local gradual deformation method is proposed, which can incorporate seismic data, well data, geology and their spatial correlations into the inversion process. Geological information, such as sedimentary facies and structures, could provide significant a priori information to constrain an inversion and arrive at reasonable solutions. The local a priori conditional cumulative distributions at each node of model to be inverted are first established by indicator cokriging, which integrates well data as hard data and geological information as soft data. Probability field simulation is used to simulate different realizations consistent with the spatial correlations and local conditional cumulative distributions. The corresponding probability field is generated by the fast Fourier transform moving average method. Then, optimization is performed to match the seismic data via an improved local gradual deformation method. Two improved strategies are proposed to be suitable for seismic inversion. The first strategy is that we select and update local areas of bad fitting between synthetic seismic data and real seismic data. The second one is that we divide each seismic trace into several parts and obtain the optimal parameters for each part individually. The applications to a synthetic example and a real case study demonstrate that our approach can effectively find fine-scale acoustic impedance models and provide uncertainty estimations.

In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.

Fractured reservoir prediction is risky and challenging because of the variability in fracture characteristics and the lack of direct observational data in the subsurface. To reduce the risk and challenge, we have developed an integrated workflow to predict fractured reservoirs based on 3D seismic data. The workflow begins with reservoir structure analysis from seismic reflection geometry, which is referred to as seismic structure analysis, to define fracture intensity and fracture orientation using maximum curvature and maximum flexure algorithms. Next, the workflow proceeds with reservoir texture analysis from seismic amplitude signal, which is referred to as seismic texture analysis, to evaluate fracture scale and reservoir facies using waveform regression and calibration algorithms. The results from seismic structure and texture analyses are then used for modeling reservoir properties and fracture networks. Each algorithmic method in the workflow is tested in a siliciclastic tight-sand reservoir in the Teapot Dome oil field (Powder River Basin) and in a carbonate reservoir in the South Pars gas field (Persian Gulf Basin). The test results reveal the previously unknown reservoir heterogeneity and anisotropy that are interpreted to be attributable to the variability in fracture characteristics. It is concluded that the integrated workflow based on seismic structure and texture analyses could potentially contribute to reducing the risk and challenge in characterizing fractured reservoirs in the subsurface.

Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.

Using seismic inversion technology the spatial distribution and geological section characterisitcs of productive hydrocarbons saturated reservoir in the Upper Carboniferous sediments in the oil field Academica Shpaka was predicted. The oil field characterized by complex seismological conditions with salt tectonics. Built maps of open porosity and oil saturation, determined acoustic zones that corresponds to the oil and gas saturated reservoir. Key words: seismic inversion, acoustic impedance, acoustic zones, porosity References Viken P.S.H., Silva M.D. 2004. Seismic inversion methods and their limitations. First Break. Vol. 22. P. 47-70.https://doi.org/10.3997/1365-2397.2004011 Kurochkin A.G., Borisenko Yu.D., Kalyina V.G. 2003. Inversion of seismic data in the parameters of the medium model. Geophysics. Special issue. «Technology seismic» II (In Russian). Kozlov E.A. 2006. Models environment in exploration seismology. Tver. Publishing house GERS. 480 P (In Russian). Becquey M., Lavergne M., Willm C. 1979. Acoustic impedance logs computed from seismic traces: Geophysics. № 44. Р.1485-1501.https://doi.org/10.1190/1.1441020 Berteussen K.A., Ursin B. 1983. Approximate computation of the acoustic impedance from seismic data: Geophysics. №48. Р. 1351-1358.https://doi.org/10.1190/1.1441415 Hill, S.J., 2005, Inversion-based thickness determination. The Leading Edge 24 (5), 477-480.https://doi.org/10.1190/1.1926799 Da Silva, M., M. Rauch, A. Soto Cuervo and P.C.H. Veeken, 2004, Data conditioning for a combined inversion and AVO reservoir characterisation study. EAGE 66th annual conference, Paris 2004, Extended Abstract, P306, 4 p. Lasseter, T.J. and S.T. Jackson, 2004, Improving integrated interpretation accuracy and efficiency using a single consistent reservoir model from seismic to simulation. The Leading Edge 23 (11), 1118-1121.https://doi.org/10.1190/1.1825932 Sena, A., G. Castillo, K. Chesser, S. Voisey, J. Estrada, J. Carcuz, E. Carmona, and P. Hodgkins, 2011, Seismic reservoir characterization in resource shale plays: stress analysis and sweet spot discrimination: The Leading Edge, v. 30 (July, 2011) p. 758-764.https://doi.org/10.1190/1.3609090

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.

Subsurface characterization and hydrocarbon resource evaluation were conducted using integrated well logs analysis and three-dimensional (3D) seismic-based reservoir characterization in an offshore field, western Niger Delta basin. Reservoir sands R1–R4 were delineated, mapped and quantitatively evaluated for petrophysical characteristics such as net-to-gross, volume of shale, water saturation, bulk water volume, porosity, permeability, fluid types and fluid contacts (GOC and OWC). The volume attributes aimed at extracting features associated with hydrocarbon presence detection, net pay evaluation and porosity estimation for optima reservoir characterization. Neural network (NN)-derived chimney properties prediction attribute was used to evaluate the integrity of the delineated structural traps. Common contour binning was employed for hydrocarbon prospect evaluation, while the seismic coloured inversion was also applied for net pay evaluation. The petrophysical properties estimations for the delineated reservoir sand units have the porosity range from 21.3 to 30.62%, hydrocarbon saturation 80.70–96.90 percentage. Estimated resistivity R t, porosity and permeability values for the delineated reservoirs favour the presence of considerable amount of hydrocarbon (oil and gas) within the reservoirs. Amplitude anomalies were equally used to delineate bright spots and flat spots; good quality reservoirs in term of their porosity models, and fluid content and contacts (GOC and OWC) were identified in the area through common contour binning, seismic colour inversion and supervised NN classification.

Thin hydrocarbon reservoir facies pose resolution challenges and waveform-signature opportunities in seismic reservoir characterization and prospect identification. In this study, we present a case study, where instantaneous frequency variation in response to a thin hydrocarbon pay zone is analyzed and integrated with other independent information to explain drilling results and optimize future drilling decisions. In Morrison NE Field, some wells with poor economics have resulted from well-placement incognizant of reservoir heterogeneities. The study area in Clark County, Kanas, USA, has been covered by a surface 3D seismic reflection survey in 2010. The target horizon is the Viola limestone, which continues to produce from 7 of the 12 wells drilled within the survey area. Seismic attributes extraction and analyses were conducted with emphasis on instantaneous attributes and amplitude anomalies to better understand and predict reservoir heterogeneities and their control on hydrocarbon entrapment settings. We have identified a higher instantaneous frequency, lower amplitude seismic facies that is in good agreement with distinct lithofacies that exhibit better (higher porosity) reservoir properties, as inferred from well-log analysis and petrographic inspection of well cuttings. This study presents a pre-drilling, data-driven approach of identifying sub-resolution reservoir seismic facies in a carbonate formation. This workflow will assist in placing new development wells in other locations within the area. Our low amplitude high instantaneous frequency seismic reservoir facies have been corroborated by findings based on well logs, petrographic analysis data, and drilling results.

History matched models have helped reservoir engineers obtain a better understanding of the reservoir and optimize the future productions. Recent years, seismic data is more often to be integrated to improve the reservoir models, however, seismic data has to be inverted to seismic impedance values first. In this study, we introduce a new way to improve the initial reservoir models -integrating the seismic first arrival times to estimate the fluid contacts and initial water saturation. The key aspect of this method that distinguishes it from other methods is that seismic data is integrated directly. As a result, traditional seismic inversion step is not needed, and the data integration are through the comparison of observed first arrival times and simulated first arrival times (raytracing results). In our paper, the feasibility of using seismic first arrival times to improve initial reservoir models is discussed, and the method is verified by using sand tank experiment data. The observed seismic first arrival times were collected during the experiment, and the simulated first arrival times were calculated by seismic raytracing program. Trust region method was used to adjust water-air contact depth and initial water saturation to minimize the difference between simulated and observed data. At last, a good estimation of initial condition is achieved, and it demonstrates a potential of integrating seismic data to improve the reservoir models without an inversion step.

A geostatistical inversion scheme was designed and performed for reservoir characterization of the UK Central North Sea (CNS) Huntington Oil field, with the goal of increasing the resolution of the seismic inversion and derived attributes, and to provide statistical measures of the uncertainties involved in the seismic facies prediction. The aim of the study was to fully understand lithology and pore-fluid rock properties in the Forties sandstone reservoir, Late Paleocene Sele Formation fan system of the UK CNS. In the Forties, multiple sand beds are separated by thin layers of silt and shale. Reprocessed prestack seismic volumes and considerable well-log data were input to an advanced stochastic simultaneous inversion method. This method combines the strengths of geostatistical inversion with sequential Gaussian simulation and Bayesian inversion. The approach allowed the inversion to be built on a fine-scale stratigraphic grid at a reservoir scale rather than at a seismic scale, and it enabled a detailed description of facies distribution and associated uncertainty. A sufficiently large number of stochastic solutions (100 realizations) enabled probabilistic models of lithofluid distribution to be generated. Facies geobody connectivity can also be derived, but this was not part of this work. As a consequence of our study, a sidetrack water injector well location was evaluated, and a potential undrilled hydrocarbon accumulation was identified, which could extend the field life of the Huntington asset.

Seismic inversion has been widely practiced in the oil and gas industry because it generates broad bandwidth of impedance data which maximizes vertical resolution and minimizes tuning effects. The lack of accurate prediction of lithology and fluid content of subtle features identified in seismic data acquired over the Sandfish field, Niger Delta, Nigeria necessitated the use of seismic inversion. In this paper, simultaneous seismic inversion is adopted to integrate seismic and well data for quantitative interpretation and uncertainty assessment of the subsurface reservoirs in the Sandfish field. Three Sandfish (Sfn) wells with the required petrophysical logs, check-shot data, high quality 3D seismic data of five angle stacks (6–12°, 12–18°, 18–26°, 26–32°, and 32–42°) were used for the analysis. A feasibility study including cross-plots of petrophysical and elastic properties from well data was first carried out to establish rock property relationships in the interval of interest. Biot-Gassmann fluid substitution analysis was also used to reveal sensitivity of rock properties to pore-fill type. Low frequency (0–2 Hz) models were generated from interpolation of high-cut-filtered P-sonic, S-sonic, and density logs guided by interpreted seismic horizons. The low frequency models were used to broaden the spectrum to estimate elastic volumes. The five partial angle stacks were simultaneously inverted using Jason’s Rock-Trace® inversion software which iterated trial inversions until the model sufficiently matched the seismic data. The inverted P-impedance (ZP), Simpedance (ZS), and density (ρ) were used to derive Poisson’s ratio (σ), volume of sand (Vsand), lambda-rho (λρ), and mu-rho (µρ). The cross-plot of λρ with µρ from well data looks similar to that from inverted results. Sands and shales are discriminated on the basis of sands having low values of µρ. Hydrocarbon-bearing sands are differentiated from water-bearing sands and shales on the basis of having lowest values of λρ. The Biot-Gassmann fluid substitution analysis at reservoir N-01 reveals typical class III amplitude variation with angle (AVA) responses for low-impedance hydrocarbon sands. The lithology and fluid prediction maps extracted from Vsand and σ at the N-01 seismic horizon show variation in lithology and fluid types for the entire volume. The inversion products reveal heterogeneities in the reservoirs away from well control validated by a blind well test. Hence, the study shows that rock-property model from a simultaneous inversion is an effective predictive tool for lithology and fluid types which in turn can guide well placement and predict reservoir development in the field of study.

Seismic inversions produce seismic models, which are 3-dimensional (3D) images of wave velocity of the entire planet retrieved by fitting seismic measurements made on records of past earthquakes or other seismic events. Computing power of the TeraFlop era, along with the dataflow from new, very dense, seismic arrays, has led to a new generation of 3D seismic Earth models with an unprecedented level of resolution. Here we compare two recent models of western United States from the Dynamic North America (DNA) seismic imaging effort. The two models only differ in the wave propagation that was used for their inversion: one is based on ray theory (RT), and the other on finite frequency (FF). We evaluate the two models using an independent numerical method and statistical tests. We show that they differ in how they produce seismic signals from a subset of earthquakes that were used in the original inversion and were recorded on the US array. This is especially true for measurements done in the Yellowstone area which has a large negative seismic anomaly. This result is of importance for seismologists who have been debating on the practical benefit of using FF in ill-posed Earth inversions. Model evaluation, such as the one reported here, represents an opportunity for collaboration between geophysical and statistical communities. More opportunities should arise with the upcoming Exascale era, which will provide enough computational power to explore together several sources of errors in models with thousands of parameters, opening the way of uncertainty quantification of seismic models.

We have developed a seismic inversion method for the joint estimation of facies and elastic properties from prestack seismic data based on a geostatistical approach. The objectives of our inversion methodology are to sample from the posterior distribution of seismic properties and to simultaneously classify the lithology conditioned by seismic data. The inversion algorithm is a sequential Gaussian mixture inversion based on Bayesian linearized amplitude variation with offset inverse theory and sequential geostatistical simulations. The stochastic approach to the inversion allows generating multiple elastic models that match the seismic data. To mathematically represent the multimodal behavior of elastic properties due to their variations within different lithologies, we adopt a Gaussian mixture distribution for the prior model of the elastic properties and we use the prior probability of the facies as weights of the Gaussian components of the mixture. The solution of the inverse problem is achieved by deriving the explicit analytical expression of the posterior distribution of the elastic properties and facies and by sampling from this distribution according to a spatial correlation model. The inversion methodology has been validated using well logs and synthetic seismic data with different noise levels, and it is then applied to a real 3D seismic data set in North Sea.

The Thrace Basin of Turkey has sandstone and carbonate reservoirs of Eocene and Oligocene age. Mapping undrained sweet spots from seismic data is currently a challenge, especially because there is no water drive after production. Time-lapse (4D) land seismic data was used to investigate undrained liquid gas pockets and to help with the planning of gas storage into the depleted reservoirs. Two vibroseis 3D baseline and monitor surveys were acquired in 2002 and 2011, respectively. The 4D seismic data has low repeatability and required conditioning to reduce the nonrepeatable 4D noise that is not related to production. Cross-equalization is applied to the base and the monitor data to bring out similarities (noise cancellation by subtracting seismic volumes), and the inverted rock properties primarily show changes due to production. We cross-equalized the corresponding angle-band stack cubes prior to prestack inversion as the available cross-equalization software was implemented for poststack seismic data. The cross-equalized angle-band stacks from the base and the monitor surveys were separately inverted to acoustic impedance (Zp), shear impedance, and density. The differences of inverted rock properties and other pore-fill indicators like Zp, P-wave velocity over S-wave velocity, and BulkRho cubes were created to map possible undrained zones after 10 years of production. A poststudy well that was drilled following the 4D analysis penetrated an undrained gas pocket.

This paper addresses the challenge of integrating 4D seismic data with production data in a quantitative manner in order to improve the forecasting ability of a reservoir model and reduce the associated uncertainty. It presents a history matching workflow that has been applied to production data and time lapse seismic data. In this procedure, the production data objective function is calculated by the conventional least squares misfit between the historical data and simulation predictions, while the seismic objective function uses the Current measurement metric between a binary image of saturation change. This approach is implemented on a real field data from the United Kingdom Continental Shelf (UKCS), where uncertain reservoir parameters which consist of global and local parameters are initially assessed. These parameters include flow based multipliers (permeability, transmissibility), volume based multipliers (net-to-gross, pore volume), as well as the end points of the relative permeability curves (critical saturation points). After the initial screening, sensitive parameters are selected based on the sensitivity analysis. An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that the primary control parameters for the binary seismic gas match are the permeability and critical gas saturation, while the volumetric parameters are important for the binary seismic water match in this particular reservoir. This approach is compared to seismic history matching using full seismic modelling, preserving all amplitudes. The results demonstrate that the binary approach gives a good match to gas saturation distribution and water saturation distribution, and the reservoir parameters converge towards a solution. The conventional approach does not capture some signals of hardening and softening in the seismic data, and hence in summary, the binary approach seems more suitable as a quick-look reservoir management tool. A unique feature of this study is the application of the binary approach using Current measurement metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.

Raw seismic data show the subsurface reflectors among the rock layers, while the acoustic-impedance section shows the layers. Therefore, acoustic impedance is closer to reality and more accurate than the raw seismic section. Also, acoustic-impedance sections are convertible to other reservoir characteristic sections such as porosity and permeatability, which provide data about these mentioned characteristics. Seismic inversion can be used as a reliable method for quantitative estimation of reservoir properties and to obtain quantified values of acoustic impedance from seismic data, well-logging data, and interpreted horizons. The primary aim of this research is to provide a reservoir acoustic-impedance model in one of the oil fields in southwestern Iran. Six 2D seismic sections obtained in the form of poststack seismic data and the results of the analysis of well logs obtained from two wells (A-1 and A-12) were interpreted in the study area. According to the results, the model-based inversion of seismic data, which is highly dependent on the initial model, produced good results compared to other methods. Regarding the results of inversion sections, it is recognized that the acoustic impedance of the reservoir and two well locations show small values, confirming good-quality reservoir on the Ghar Formation. Eventually, with the study of the obtained acoustic-impedance model, it is recognized that the lowest value of acoustic impedance in the region was related to the common-depth point of the 3370 location, so it can be used for future study.

Because the seismic wave propagates through the subsurface, part of the elastic energy eventually ends up as heat energy. This phenomenon is known as absorption (or anelastic attenuation). The factors causing anelastic attenuation include fluid movement and grain boundary friction. The seismic quality factor ([Formula: see text]) quantifies the anelastic attenuation and is commonly used in assisting reservoir characterization. However, current [Formula: see text]-estimation approaches are mainly implemented on a poststack seismic volume. The [Formula: see text]-estimation approaches applied to poststack seismic data assume that the seismic data are normal incident reflections, and they do not consider the effect of the travel path on seismic attenuation. In theory, the attenuation degree of the low-frequency component should differ from the attenuation degree of the high-frequency component for large-offset seismic data. We have developed a method to qualitatively estimate seismic attenuation in the prestack seismic domain. A continuous wavelet transform is used to extract the low- and high-frequency components for the common-reflection point gathers. The difference between the amplitude of the low-frequency component and the amplitude of the high-frequency component is used to measure the seismic attenuation factor. We have determined the effectiveness of our method by applying it to synthetic and real seismic data.

Time-lapse seismic “4D” close-the-loop (CtL) technology can play a significant role in creating robust static and dynamic models for deepwater reservoirs, as demonstrated by a case study in Equatorial Guinea, West Africa. Initial static and dynamic models for the field were built using well data and seismic attribute volumes, from prestack 3D and 4D simultaneous angle versus offset (AVO) inversions, in conjunction with rock-physics analysis. We then used seismic inversion technology to estimate static and dynamic reservoir properties over the field's life, including porosity, net-to-gross (NTG), and changes in both fluid saturations and pressures. To model changes, we used a baseline and two high-repeat 4D seismic surveys as input to the inversion work. The study shows how the many competing production effects and their combined nonunique 4D signatures posed a serious challenge to achieving history-matched dynamic models consistent with 4D seismic data. However, 4D CtL technology served as a guide for reservoir engineers updating the dynamic model simulations by providing direction and constraints for matching pressures, waterfronts, and gas breakout areas. Moreover, 4D CtL helped refine existing mental sweep models based on historic field performance and available tracer data. Previous models largely matched this data but used some undesirable manual edits in the simulation model. This has allowed for more geologically sound model adjustments, especially to the water-injection strategy, which obtained promising results. A number of targets were dropped based on 4D matched simulation models that showed elevated water saturations with a high chance for early water breakthrough and reduced well recovery. On the other hand, new targets similar to the one in this case study were identified and/or derisked. This successful case study demonstrates the potential of rock physics, especially when integrated with prestack AVO inversion techniques, to produce a set of attributes that accurately explain the time-lapse production effects observed on seismic.

Using an integrated approach of reservoir simulation, rock physics and seismic modeling, the present study analyzes the influence of changes in pressure and fluid saturation on synthetic time-lapse seismic sections. The forms and occasions in which these changes can be detected on 4D seismic are studied, to help in the characterization of the fluid flow inside the reservoir. To analyze these effects, reservoir production simulations were conducted in a geological model of a sandstone reservoir in the Campos Basin, offshore SE Brazil. Through direct petro-elastic and seismic modeling, 4D impedance volumes were created using simulation results, and corresponding synthetic seismic volumes were computed. Simulations of both water injection and gas injection scenarios were conducted in order to quantify the differences between oil-water and oil-gas substitutions and investigate how they affect the seismic amplitudes in a sandstone reservoir. The interpretations allow the definition of patterns to identify pressure and saturation related anomalies and to distinguish areas of water and gas injection.

Seismic facies analysis plays an important role in seismic stratigraphy. Seismic attributes have been widely applied to seismic facies analysis. One of the most important steps is to optimize the most sensitive attributes with regard to reservoir characteristics. Using different attribute combinations in multidimensional analyses will yield different solutions. Acoustic waves and seismic waves propagating in an elastic medium follow the same law of physics. The generation process of a speech signal based on the acoustic model is similar to the seismic data of the convolution model. We have developed the mel-frequency cepstrum coefficients (MFCCs), which have been successfully applied in speech recognition, as feature parameters for seismic facies analysis. Information about the wavelet and reflection coefficients is well-separated in these cepstrum-domain parameters. Specifically, information about the wavelet mainly appears in the low-domain part, and information about the reflection coefficients mainly appeared in the high-domain part. In the forward model, the seismic MFCCs are used as feature vectors for synthetic data with a noise level of zero and 5%. The Bayesian network is used to classify the traces. Then, classification accuracy rates versus different orders of the MFCCs are obtained. The forwarding results indicate that high accuracy rates are achieved when the order exceeds 10. For the real field data, the seismic data are decomposed into a set of MFCC parameters. The different information is unfolded in the parameter maps, enabling the interpreter to capture the geologic features of the target interval. The geologic features presented in the three instantaneous attributes and coherence can also be found in the MFCC parameter maps. The classification results are in accordance with the paleogeomorphy of the target interval as well as the known wells. The results from the synthetic data and real field data demonstrate the information description abilities of the seismic MFCC parameters. Therefore, using the speech feature parameters to extract information may be helpful for processing and interpreting seismic data.

Geostatistical seismic inversion methods are routinely used in reservoir characterization studies because of their potential to infer the spatial distribution of the petro-elastic properties of interest (e.g., density, elastic and acoustic impedances) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and global spatial continuity model as estimated from the available well-log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous and in many cases non-stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, application of a single distribution function and spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a 3D geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi-distribution functions and spatial continuity models under non-stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non-stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of a higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques. This article is protected by copyright. All rights reserved

This paper addresses seismic imaging of fault zones and analysis of the seismic data with the use of the fault facies model developed at Uni Research CIPR. Simulated and ideal seismic images were produced to generate a statistical ensemble of realizations of this model. The comparative analysis presented in this paper is focused on the statistical characteristics of the initial geological model and its seismic images. This paper analyzes cross-correlations between seismic images and ideal seismic images. The method proposed makes it possible to establish an exact correspondence between the initial fault facies models and the seismic images of these models.

In complex geologic settings with a great degree of heterogeneity in reservoir properties, such as submarine channel complexes as in the Nile Delta province, we face the challenge of characterizing the reservoir based on availability of different seismic attributes. Amplitude variation with offset (AVO) analysis and prestack inversion techniques show impressive results in delineating the gas-bearing reservoirs, especially in clastic systems. However, a shortage of available wells and/or seismic data leads to a challenge in applying AVO and any prestack seismic inversion approaches. In addition, quantitative prediction of water saturation (Sw) from seismic is ambiguous because of its independent nonlinear relationship with conventional seismic attributes and inversion products. Water-saturation prediction away from the well is essential in order to characterize the reservoirs effectively. Therefore, probabilistic neural network (PNN) analysis has been implemented to predict Sw 3D volume using full-stack seismic data and Sw logs. In this case study, we applied the proposed neural network workflow over one of the late-Pliocene gas-sandstone reservoirs, Sequoia Field, in the West Delta Deep Marine (WDDM) concession, offshore Nile Delta, Egypt. The resulting volume then was tested using two blind wells that haven't been used in the analysis. The predicted Sw volume contains fine details that were used with variance and spectral-decomposed volumes to understand the reservoir's internal architecture in terms of sand body geometries and connectivity. The resulting volumes were used to better define the reservoir and optimize a new development well location.

To find out the seismic wave field propagation principles in loess plateau near surface of the Ordos Basin and the seismic response characteristics of tight oil reservoirs, this study established a geological-geophysical model under the real conditions of ground surface of loess plateau, and launched full elastic seismic wave equation forward modeling and pre-stack elastic seismic inversion study. Comparison of modeling and real seismic data shows that, the loose and wavy loess plateau surface is the main reason for causing the problems of seismic static correction and interference wave. Tomographic static correction method with the constraint of traces near shot point can effectively solve the problem of seismic static correction in the loess plateau and enhance seismic imaging accuracy, S-wave impedance obtained from pre-stack seismic inversion can identify sandstone effectively, and Poisson's ratio can identify oil-bearing reservoirs. The seismic forward and inverse simulation and rock physical analysis provide a solid theoretical and experimental basis for the seismic prediction of tight oil reservoir, and have worked well in the oil exploration and development in the loess plateau of the Ordos Basin. Key words: loess plateau surface, tight reservoir, wave equation forward simulation, tomographic inversion static correction, pre-stack elastic inversion

Integration of 3D and time-lapse 4D seismic data into reservoir modelling and history matching processes poses a significant challenge due to the frequent mismatch between the initial reservoir model, the true reservoir geology, and the pre-production (baseline) seismic data. A fundamental step of a reservoir characterisation and performance study is the preconditioning of the initial reservoir model to equally honour both the geological knowledge and seismic data. In this paper we analyse the issues that have a significant impact on the (mis)match of the initial reservoir model with well logs and inverted 3D seismic data. These issues include the constraining methods for reservoir lithofacies modelling, the sensitivity of the results to the presence of realistic resolution and noise in the seismic data, the geostatistical modelling parameters, and the uncertainties associated with quantitative incorporation of inverted seismic data in reservoir lithofacies modelling. We demonstrate that in a geostatistical lithofacies simulation process, seismic constraining methods based on seismic litho-probability curves and seismic litho-probability cubes yield the best match to the reference model, even when realistic resolution and noise is included in the dataset. In addition, our analyses show that quantitative incorporation of inverted 3D seismic data in static reservoir modelling carries a range of uncertainties and should be cautiously applied in order to minimise the risk of misinterpretation. These uncertainties are due to the limited vertical resolution of the seismic data compared to the scale of the geological heterogeneities, the fundamental instability of the inverse problem, and the non-unique elastic properties of different lithofacies types.

Three-dimensional poststack and prestack seismic inversion results such as P- and S-impedance are commonly used for reservoir characterization. However, the frequency bandwidth of surface-based reflection seismic surveys usually ranges from 10 to 70 Hz, and these surveys have limited vertical resolution. The frequency bandwidth of vertical seismic profiling (VSP) and crosswell data is much wider than that of surface reflection seismic data, and it can give a detailed illumination of the subsurface around the borehole. We test a joint inversion method that integrated surface reflection seismic, VSP, and crosswell data. To better constrain the inversion results, we further integrate a posteriori information on the reflectivity obtained from petrophysics data into the inversion procedure. The a posteriori distribution we use is a modified-Cauchy distribution obtained from the statistical analysis of petrophysics data. To demonstrate the effectiveness of our algorithm, we applied our inversion strategy to a 2D synthetic model and a real seismic data set, and an uncertainty assessment was also performed. The joint inversion method can detect the thin layers that surface seismic inversion fail to, demonstrating the higher resolution of the method.

We have performed a deep seismic reflection study, DACIA-PLAN, based on the data recorded along a crooked line across the southeastern Romanian Carpathians. The signal-to-noise ratio (S/N) of these data varies along the seismic profile, and its variation is considered to be an effect of the rough topography, complex subsurface geology, and varying surface conditions encountered during seismic data acquisition. The migrated time section that covers the mountainous area is clear, without visible reflections, making the geologic interpretation very difficult. We used a seismic modeling technique to explain the poor S/N of the recorded data and to generate synthetic seismic sections that can be useful for the geologic interpretation of the field seismic section (migrated time section). We used ray-tracing modeling to obtain the expected seismic expression of horizons of interest. Subsurface illumination modeling indicates that the complex subsurface geology and irregularly deployed sources and receivers are responsible for the incomplete and/or uneven illumination of the subsurface and can lead to strong amplitude variations. We then used 2.5D acoustic finite-difference modeling to analyze the effect of a crooked line on seismic wave propagation. The synthetic shot gathers prove that crooked line arrival times for reflected and head waves contain static time shifts relative to a straight line regular sampling geometry. Some geologic interfaces of interest are not well-imaged on the synthetic seismic section, and this is considered to be an effect of poor positioning during seismic data acquisition. We used the velocity model from the tomographic inversion of first-arrival traveltimes and synthetic and field crooked line deep seismic reflection data to create a structural image for the southeastern Romanian Carpathians and the Focsani Basin, which tie well with the geologic model built for this area on the basis of geologic and well data only.

More and more seismic surveys produce 3D seismic images in the depth domain by using prestack depth migration methods, which can present a direct subsurface structure in the depth domain rather than in the time domain. This leads to the increasing need for applications of seismic inversion on the depth-imaged seismic data for reservoir characterization. To address this issue, we have developed a depth-domain seismic inversion method by using the compressed sensing technique with output of reflectivity and band-limited impedance without conversion to the time domain. The formulations of the seismic inversion in the depth domain are similar to time-domain methods, but they implement all the elements in depth domain, for example, a depth-domain seismic well tie. The developed method was first tested on synthetic data, showing great improvement of the resolution on inverted reflectivity. We later applied the method on a depth-migrated field data with well-log data validated, showing a great fit between them and also improved resolution on the inversion results, which demonstrates the feasibility and reliability of the proposed method on depth-domain seismic data.

This paper presents a comparison between subsurface impedance models derived from different deterministic and geostatistical seismic inversion methodologies applied to a challenging synthetic dataset. Geostatistical seismic inversion methodologies nowadays are common place in both industry and academia, contrasting with traditional deterministic seismic inversion methodologies that are becoming less used as part of the geo-modelling workflow. While the first set of techniques allows the simultaneous inference of the best-fit inverse model along with the spatial uncertainty of the subsurface elastic property of interest, the second family of inverse methodology has proven results in correctly predicting the subsurface elastic properties of interest with comparatively less computational cost. We present herein the results of a benchmark study performed over a realistic three-dimensional non-stationary synthetic dataset in order to assess the performance and convergence of different deterministic and geostatistical seismic inverse methodologies. We also compare and discuss the impact of the inversion parameterisation over the exploration of the model parameter space. The results show that the chosen seismic inversion methodology should always be dependent on the type and quantity of the available data, both seismic and well-log, and the complexity of the geological environment versus the assumptions behind each inversion technique. The assessment of the model parameter space shows that the initial guess of traditional deterministic seismic inversion methodologies is of high importance since it will determine the location of the best-fit inverse solution.

Rock physics provides a link between seismic and reservoir properties. It helps to understand the effects of fluid substitution and reservoir parameters on seismic signatures. Rock physics models are now an integral part of feasibility study of time lapse (4D) seismic surveys and their interpretation. There exist different approaches for the construction of rock physics models within the context of 4D seismic study. The objective of this study is to investigate the predictions of existing rock physics models for time lapse seismic studies using real well data. The pore-filled fluid properties used represent the in situ conditions, while the effective elastic moduli and bulk densities are calculated based on theoretical models. Finally the reflection and amplitude predictions from each model are compared over a range of water saturation. Our results suggest that only Krief’ s model is able to discriminate between porous and non-porous zones effectively. Furthermore, all of the investigated models show increase in velocity with replacement of oil with heavier fluid i.e., water except Wyllie’s model. There is a significant change in seismic amplitude when compared with in situ conditions for this particular lithology.

Comprehensive borehole seismic surveys hold the key to unlocking the knowledge contained within the long-offset full-azimuth surface seismic surveys that are fast becoming common in land exploration. This paper presents a case study of acquisition, processing and use of such survey for validation and calibration of processing parameters and inversion results of a recent point-source/point-receiver 3D surface seismic dataset acquired in the Cooper Basin. The 3D surface seismic data have been initially processed without significant borehole seismic data input. However, as the processing revealed gaps in knowledge, the need for borehole calibration was realised. This led to acquisition of a complex borehole seismic survey in a gas-discovery well comprising Multi-Azimuthal Walkaway (MAZ WVSP), Walkaround and Zero Offset Vertical Seismic Profiles (ZVSP). The acquired dataset shed light into the peg-leg multiple mechanisms as well as the VTI and azimuthal anisotropy. Advanced processing techniques such as calibrated piece-wise VTI inversion and azimuthal travel time fitting were applied to the MAZ WVSP data to validate the processing steps of the 3D surface seismic data and calibrate the results of its AVOaz inversion. Apart from showing some of the results of this study, this paper documents the various contact points between VSP and surface seismic datasets and how the results of processing complement each other. The final result comprised a calibrated seismic map of drilling targets.

Seismic inverse problems aim to infer the properties of subsurface geology, such as elastic and petrophysical properties. Existing seismic inversion methods for the joint estimation of these properties are mainly based either on Gassmann theory for prestack seismic data processed with stochastic optimisation techniques or on the Wyllie formula for poststack seismic data processed by deterministic optimisation techniques. The purpose of this study is to develop a strategy for the joint estimation of elastic and petrophysical properties from prestack seismic data based on Gassmann equations with deterministic optimisation techniques. Given poor-quality prestack seismic data, two regularisation parameters are introduced to control the trade-off between fidelity to the data and the smoothness of the solution. An appropriate linearised system of equations for the joint model update is derived from Newton’s method, which fits seismic data, the description of the rock physics medium and prior information, simult...

Seismic facies classification has been used to reduce risk in laterally heterogeneous reservoir prediction. Studies were focused on the Barrolka/Coolah/Durham Downs Trend in the south-west Queensland sector of the Cooper Basin. The primary reservoir targets are the fluvial channel sediments of the Toolachee Formation. Historically, well success rates across the area have been low, with highly variable reservoir development and connectivity identified as the limiting factors influencing well performance.Conventional seismic attribute analysis has typically yielded inconclusive results, often associated to the presence of thick coals that dominate the seismic response. However, recent drilling campaigns utilised seismic waveform classification mapping, which resulted in an increase in technical success rate of wells. This study aims to investigate the concepts behind the success of the waveform classification method and to determine alternate techniques to further delineate reservoir presence.Key outcomes f...

Time-lapse (4D) seismic data sets have proven to be extremely useful for reservoir monitoring. Seismic-derived impedance estimates are commonly used as a 4D attribute to constrain updates to reservoir fluid flow models. However, 4D seismic estimates of P-wave impedance can contain significant errors associated with the effects of seismic noise and the inherent instability of inverse methods. These errors may compromise the geological accuracy of the reservoir model leading to incorrect reservoir model property updates and incorrect reservoir fluid flow predictions. To evaluate such errors and uncertainties we study two time-lapse scenarios based on 1D and 3D reservoir model examples, thereby exploring a number of inverse theory concepts associated with the instability and error of coloured inversion operators and their dependence on seismic noise levels. In the 1D example, we show that inverted band-limited impedance changes have a smaller root-mean-square (RMS) error in comparison to their absolute broadband counterpart for signal-to-noise ratios 10 and 5 while for signal-to-noise ratio (S/N) = 3 both inversion methods present similarly high errors. In the 3D example we use an oilfield benchmark case based on the Namorado Field in Campos Basin, Brazil. We introduce a histogram similarity measure to quantify the impact of seismic noise on maps of 4D seismic amplitude and impedance changes as a function of S/N levels, which indicate that amplitudes are less sensitive to 4D seismic noise than impedances. The RMS errors in the estimates of water saturation changes derived from 4D seismic amplitudes are also smaller than for 4D seismic impedances, over a wide range of typical seismic noise levels. These results quantitatively demonstrate that seismic amplitudes can be more accurate and robust than seismic impedances for quantifying water saturation changes with 4D seismic data, and emphasize that seismic amplitudes may be more reliable to update fluid flow model properties in the presence of realistic 4D seismic noise.

Mutual relationships between geological and geophysical data obtained by using methods of different scale are presented for the Miocene sandy-shaly thin-bedded formation and for the Zechstein carbonate formation. The common basis of laboratory results, well logging and seismic data was a recognition of elastic and reservoir properties of rocks. The common basis of laboratory results, well logging and seismic data were elastic and reservoir properties of rocks. Seismic attributes calculated from acoustic full waveforms were a link between the considered data. Seismic attributes strongly depend on small changes observed in rock formation related to lithology variations, facies changes, structural events and petrophysical properties variability. The observed trends and relationships of high correlation coefficients in the analysed data proved the assumption made at the beginning of research that common physical basis is a platform for data scaling. Proper scaling enables expanding the relationships determined from laboratory and well logging of petrophysical parameters to a seismic scale.

The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator. Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.

Influence of uncertainty factors of input parameters on results of the estimation of seismic hazard has been researched. It is found that the largest deviations, from seismic hazard maps designed on the basis of average values of distribution of seismic mode and seismic load parameters, may arise due to the imprecise depth of earthquake sources (H), uncertain estimations of seismic potential (Мmax) and slope of recurrence curve (γ). The contribution of such uncertainty factors, like imprecise definition of seismic activity А10, incorrect choice of prevailing type of a motion in the source, using regional laws of attenuation of seismic load intensity in distance instead of local once are substantially small. For Eastern Uzbekistan, it was designed the seismic hazard map with the highest value which takes into account every possible factors of uncertainty in parameters of seismic mode and seismic load.

The forecasting and risk assessment of induced seismicity associated with fluid injection have considerable importance for subsurface energy development. We have developed a seismic evaluation method called the possible seismic moment (PoSeMo) model to assess the potential seismic moment that could be released in the future based on current seismic activity. The PoSeMo model assumes the existence of a representative parameter that can describe the seismic characteristics of a given field. This parameter is defined as the seismic moment density, which quantifies the seismic moment able to be released per rock volume. The rock volume presumed to be in critical condition because of stimulation is defined as the stimulated rock volume. The current stimulation condition for the PoSeMo model can be estimated from the product of these two parameters. The difference between the output of the PoSeMo model and the observed cumulative seismic moment corresponds to the cumulative seismic moment that could be released in the future. This value can be transformed into the possible maximum magnitude that has clear physical meaning and that can be used as feedback on the stimulation operation for seismic hazard assessment. We have applied this model to a microseismic data set from the Basel engineered geothermal system project. We have successfully estimated reasonable values for seismic moment density and stimulated rock volume. The PoSeMo model performed well, and it provided reasonable estimates of seismic moment. The maximum magnitude estimated by the PoSeMo model was almost identical to the largest event that had occurred previously. Thus, it was concluded that the PoSeMo model satisfactorily demonstrated its feasibility as a real-time seismic evaluation method, based on physical parameters derived from microseismic information.

Seismic apparition is a recent signal-processing advance that trades signal interference and aliasing between different spatial (and temporal) dimensions. In particular, an important application of seismic apparition is for simultaneous-source separation to better exploit the available data space in the frequency-wavenumber (f-k) domain with energy from different simultaneous sources. The introduction of periodic modulation functions in seismic acquisition produces an effect where parts of the energy of one or more sources are partially shifted to different empty parts within the f-k domain. This so-called apparated energy then can be used to perfectly predict (at low frequencies) the remaining part of the signal in the regions of the f-k domain where the wavefields from the different sources overlap and to deterministically separate the sources at predetermined (repeated) positions — a prerequisite for 4D seismic processing. At higher frequencies, the apparition separation is dealiased using directional information, taking full advantage of the perfect separation at lower frequencies to achieve the required low-error separation. To apparate seismic energy to different portions within the f-k domain, we introduce a periodic modulation function, consisting for instance of a small time delay/advance or an amplitude scaling to every second shot. Our simultaneous-source approach is thus opposite to established industry practices of maximizing randomness using dithered sources. A deterministic simultaneous-source-separation approach in which shot points and simultaneous-source patterns are repeated accurately is a major advantage over industry-standard stochastic simultaneous-source-acquisition approaches, particularly for 4D applications. We demonstrate the suitability of the seismic-apparition simultaneous-source technique to time-lapse seismic, having a permanent-reservoir-monitoring context in mind, by investigating two operational scenarios of single-vessel acquisition where two sources are excited simultaneously. Single-vessel simultaneous-source acquisition represents the most challenging case for shot separation in general and for time-lapse seismic applications in particular. The quantitative and qualitative wavefield decomposition results analyzed pre- and poststack are highly encouraging on synthetic data and warrant further testing of seismic-apparition technology in a real 4D seismic test case.

An integrated methodology is proposed to quantitatively evaluate interwell connectivity by uniting available data from the production and seismic domains, while simultaneously honouring reservoir geology. The Capacitance Model approach for interwell evaluation is selected initially to obtain prior understanding using well production and injection fluctuations. Then, to make proper use of 4D seismic data, we extend the newly developed “well2seis” technique to further predict the well-to-reservoir connectivity by correlating multiple seismic monitor surveys to the well behaviour data. Based on the prior information provided by the Capacitance Model, appropriate wells are selected to provide robust 4D seismic correlation. The final result is generated as a 3D attribute volume, which directly reveals spatial patterns of reservoir connectivity. The proposed methodology is firstly tested on a synthetic case, where it is shown that the well2seis correlation attribute can correctly identify key reservoir flow barriers and conduits. When applied to observed data from the Norne field, the pressure diffusion and fluid flow pathways from injectors to producers are detected, which are consistent with bottom-hole pressure measurements and observed sea water production breakthrough. We also discover a key fault barrier which was not considered in the reservoir model previously and successfully improves the history matching quality. The understanding of the reservoir connectivity is significantly improved compared to using conventional methods or the 4D seismic method independently.

Velocity model building is the first step of seismic inversion and the foundation of the subsequent processing and interpretation workflow. Velocity model building from surface seismic data only becomes severely underdetermined and nonunique when more than one parameter is needed to characterize the velocity anisotropy. The traditional seismic processing workflow sequentially performs seismic velocity model building, structural imaging/interpretation, and lithologic inversion, modifying the subsurface model in each step without verifications against the previously used data. We have developed an integrated model building scheme that uses all available information: seismic data, geologic structural information, well logs, and rock-physics knowledge. We have evaluated the accuracy of the anisotropic model in the image space, in which structural information is estimated. The lithologic inversion results from well logs and the dynamic seismic information (amplitude versus angle) are also fed back to the kinematic seismic inversion via a cross-parameter covariance matrix, which is a multivariate Gaussian approximation to the numerical distribution modeled from stochastic rock-physics modeling. The procedure of building the rock-physics prior information and the improvements using these extra constraints were tested on a Gulf of Mexico data set. The inverted vertical transverse isotropic model not only better focused the seismic image, but it also satisfied the geologic and rock-physics principles.

Stochastic sequential simulation is a common modelling technique used in Earth sciences and an integral part of iterative geostatistical seismic inversion methodologies. Traditional stochastic sequential simulation techniques based on bi-point statistics assume, for the entire study area, stationarity of the spatial continuity pattern and a single probability distribution function, as revealed by a single variogram model and inferred from the available experimental data, respectively. In this paper, the traditional direct sequential simulation algorithm is extended to handle non-stationary natural phenomena. The proposed stochastic sequential simulation algorithm can take into consideration multiple regionalized spatial continuity patterns and probability distribution functions, depending on the spatial location of the grid node to be simulated. This work shows the application and discusses the benefits of the proposed stochastic sequential simulation as part of an iterative geostatistical seismic inversion methodology in two distinct geological environments in which non-stationarity behaviour can be assessed by the simultaneous interpretation of the available well-log and seismic reflection data. The results show that the elastic models generated by the proposed stochastic sequential simulation are able to reproduce simultaneously the regional and global variogram models and target distribution functions relative to the average volume of each sub-region. When used as part of a geostatistical seismic inversion procedure, the retrieved inverse models are more geologically realistic, since they incorporate the knowledge of the subsurface geology as provided, for example, by seismic and well-log data interpretation.

This paper shows a history matching workflow with both production and 4D seismic data where the uncertainty of seismic data for history matching comes from Bayesian seismic waveform inversion. We use a synthetic model and perform two seismic surveys, one before start of production and the second after 1 year of production. From the first seismic survey, we estimate the contrast in slowness squared (with uncertainty) and use this estimate to generate an initial estimate of porosity and permeability fields. This ensemble is then updated using the second seismic survey (after inversion to contrasts) and production data with an iterative ensemble smoother. The impact on history matching results from using different uncertainty estimates for the seismic data is investigated. From the Bayesian seismic inversion, we get a covariance matrix for the uncertainty and we compare using the full covariance matrix with using only the diagonal. We also compare with using a simplified uncertainty estimate that does not come from the seismic inversion. The results indicate that it is important not to underestimate the noise in seismic data and that having information about the correlation in the error in seismic data can in some cases improve the results.

The travel time inversion of wide-angle seismic data is a technique commonly used in the deep seismic sounding. We propose an application of this technique to a smaller scale of a sedimentary layer, where the characteristics of seismic observations changes significantly. Field observations confirmed by synthetic analysis recognize the dominant amplitudes of wide-angle post-critical reflections. A case study is presented in this paper, of a joint interpretation of conventional reflection seismic with reflection imaging, combined with the wide-angle travel time inversion of additional full-spread observations. A joint interpretation results in a precise recognition of the seismic velocity distribution, that is further used for the seismic depth conversion with the uncertainty analysis of the depth of the reflecting horizons. Despite the salt layer in the studied structure this method is able to precisely recognize the seismic velocities of the sub-salt structures.

Seismic reservoir characterization of unconventional gas shales is challenging due to their heterogeneity and anisotropy. Rock properties of unconventional gas shales such as porosity, pore-shape distribution, and composition are important for interpreting seismic data amplitude variations in order to locate optimal drilling locations. The presented seismic reservoir characterization procedure applied a grid-search algorithm to estimate the composition, pore-shape distribution, and porosity at the seismic scale from the seismically inverted impedances and a rock-physics model, using the Haynesville Shale as a case study. All the proposed rock properties affected the seismic velocities, and the combined effects of these rock properties on the seismic amplitude were investigated simultaneously. The P- and S-impedances correlated negatively with porosity, and the V P/V S correlated positively with clay fraction and negatively with the pore-shape distribution and quartz fraction. The reliability of these estimated rock properties at the seismic scale was verified through comparisons between two sets of elastic properties: one coming from inverted impedances, which were obtained from simultaneous inversion of prestack seismic data, and one derived from these estimated rock properties. The differences between the two sets of elastic properties were less than a few percent, verifying the feasibility of the presented seismic reservoir characterization.

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation. However, information is mislaid in the stacking process when traditional texture attributes are extracted from post-stack data, which is detrimental to complex reservoir description. In this study, pre-stack texture attributes are introduced, these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset, anisotropy, and heterogeneity in the medium. Due to its strong ability to represent stratigraphics, a pre-stack-data-based seismic facies analysis method is proposed using the self-organizing map algorithm. This method is tested on wide azimuth seismic data from China, and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified, in addition to the method’s ability to reveal anisotropy and heterogeneity characteristics. The pre-stack texture classification results effectively distinguish different seismic reflection patterns, thereby providing reliable evidence for use in seismic facies analysis.

Successful use of an artificial neural network is shown to predict lateral variations of seismic velocity, density, thickness, and gamma rays associated with sand dune reservoirs identified on a previously interpreted seismic horizon. The work is presented in two main sections. Section one is a feasibility analysis based on synthetic data. A known geologic model is used, performed by pseudowells, in which lateral variations in seismic velocity, density, and gamma ray values are related to the dunes. The synthetic seismic model and the attributes derived are used as training input in the neural network. Section two is a real case example where the methodology is applied to a real seismic data set. Results indicate that using a set of data and attributes restricted to a time interval corresponding to a previously interpreted seismic horizon is more efficient than using a whole data cube, involving a very large volume of data. Fil: Cersósimo, Darío Sergio. GALP Energía; Portugal Fil: Ravazzoli, Claudia Leonor. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina Fil: García Martinez, Ramón. Universidad Nacional de Lanús; Argentina