Angle Stacks Research Articles

Seismic-scale petrophysical interpretation and gas-volume estimation from simultaneous impedance inversion

Simultaneous impedance inversion was performed to obtain the seismic-scale P- and S-wave impedances as well as the bulk density volumes from seismic angle stacks at a siliciclastic turbidite offshore gas reservoir. To translate these variables into seismic-scale total porosity (φ), clay content, and water saturation (Sw), we used a deterministic rock-physics model established at the well. A crucial input into this model is the bulk modulus (Kf) of the brine/gas system. We need to link it to Sw at the seismic scale. This link can be one of simple mixing laws, such as harmonic and arithmetic. Alternately, it can be derived at the well by using upscaled variables and ensuring that they obey the well-data-driven rock-physics model. These different Kf-Sw relations produce different interpretations for φ and Sw thus allowing one to assess the variability in the estimates of the gas volume in place that is proportional to the product of φ and (1 − Sw).

One-dimensional stochastic inversion for seismic reservoir characterization — A case study

Estimation of reservoir properties and facies from seismic data is a well-established technique, and numerous methods are in common usage. We demonstrate that our 1D stochastic inversion process, based on matching large numbers of pseudowells to color-inverted angle stacks, can produce accurate estimations of facies and reservoir properties and, importantly, the associated uncertainties. Using a case study from an offshore siliciclastic field, we show that a thorough understanding of all input well data and a detailed validation of the parameters input to the inversion process are crucial to obtaining a good result. We also illustrate some of the various products that can be output from such an inversion process to help constrain subsequent geostatistical reservoir modeling.

Contribution of integrating seismic coherency and AVO attributes in delineating sand bars reservoirs, Offshore Nile Delta, Egypt, A case study

The geometry and extension of Offshore Nile Delta sand bar complex deposited in a structurally controlled shallow marine basin is delineated by integrating seismic coherency and AVO attributes. Coherency is the imaging of discontinuities within the seismic data, where faults, sand bar boundaries and fluid contacts (GWC) were mapped, using an optimum analysis window of 40×40×64, and instantaneous amplitude of time slice image map at 1652ms. AVO attributes are applied to reservoir sands of significant bright spot response. AVO attributes volumes, include the (A*B), (A+B), and (A−B) of the intercept and gradient, generated from near, mid, and far angle stacks. The seismic coherency volume delineated two major bounding faults of the reservoir, that converged at the western end of the field and open out to the east where, the reservoir is dip-closed. Four boundaries between five sand bars oriented in NW-SE direction are detected. GWC is delineated at the deepest part of the reservoir towards the southeastern part.AVO attributes indicated that the reservoir top is characterized by positive bright spots, negative Poisson’s ratio, and increasing in the scaled S-reflectivity. Meanwhile, the bottom is characterized by bright spots, positive Poisson’s ratio, and decreasing in the scaled S-reflectivity. AVO volumes were consistent, and show the typical response of Class III gas sands. Five high positive amplitude sand bars oriented NW-SE are confirmed. Three of them were successfully tested. The two prolific southern sand bars exhibit higher amplitude haven’t been tested.

Simultaneous impedance inversion and interpretation for an offshore turbiditic reservoir

By using simultaneous impedance inversion, we obtained P- and S-wave impedance ([Formula: see text] and [Formula: see text]) volumes from angle stacks at a siliciclastic turbidite oil reservoir offshore northwest Australia. The ultimate goal was to interpret these elastic variables for fluid, porosity, and mineralogy. This is why an essential part of our workflow was finding the appropriate rock-physics model based on well data. The model-corrected S-wave velocity [Formula: see text] in the wells was used as an input to impedance inversion. The inversion parameters were optimized in small vertical sections around two wells to obtain the best possible match between the seismic impedances and the upscaled impedances measured at the wells. Special attention was paid to the seismically derived [Formula: see text] ratio because we relied on this parameter for hydrocarbon identification. Even after performing crosscorrelation between the angle stacks to correct for two-way traveltime shifts to align the stacks, these stacks did not indicate a coherent amplitude variation with angle (AVA) dependence. To deal with this common problem, we corrected the mid and far stacks by using the near and ultrafar stacks as anchoring points for fitting a [Formula: see text] AVA curve. This choice allowed us to match the seismically derived [Formula: see text] ratio with that predicted by the rock-physics model in the reservoir. Finally, the rock-physics model was used to interpret these [Formula: see text] and [Formula: see text] for the fluid, porosity, and mineralogy. The new paradigm in our inversion/interpretation workflow is that the ultimate quality control of the inversion is in an accurate deterministic match between the seismically derived petrophysical variables and the corresponding upscaled depth curves at the wells. Our interpretation is very sensitive to the inversion results, especially the [Formula: see text] ratio. Despite this fact, we were able to obtain accurate estimates of porosity and clay content in the reservoir and around it.

Lithology and fluid prediction from simultaneous seismic inversion over Sandfish field, Niger Delta, Nigeria

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.

Multiecho pseudo-golden angle stack of stars thermometry with high spatial and temporal resolution using k-space weighted image contrast.

Implement and evaluate a 3D MRI method to measure temperature changes with high spatial and temporal resolution and large field of view. A multiecho pseudo-golden angle stack-of-stars (SOS) sequence with k-space weighted image contrast (KWIC) reconstruction was implemented to simultaneously measure multiple quantities, including temperature, initial signal magnitude M(0), transverse relaxation time ( T2*), and water/fat images. Respiration artifacts were corrected using self-navigation. KWIC artifacts were removed using a multi-baseline library. The phases of the multiple echo images were combined to improve proton resonance frequency precision. Temperature precision was tested through in vivo breast imaging (N = 5 healthy volunteers) using both coronal and sagittal orientations and with focused ultrasound (FUS) heating in a pork phantom using a breast specific MR-guided FUS system. Temperature measurement precision was significantly improved after echo combination when compared with the no echo combination case (spatial average of the standard deviation through time of 0.3-1.0 and 0.7-1.9°C, respectively). Temperature measurement accuracy during heating was comparable to a 3D seg-EPI sequence. M(0) and T2* values showed temperature dependence during heating in pork adipose tissue. A self-navigated 3D multiecho SOS sequence with dynamic KWIC reconstruction is a promising thermometry method that provides multiple temperature sensitive quantitative values. Magn Reson Med 79:1407-1419, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Constraints on melt content of off‐axis magma lenses at the East Pacific Rise from analysis of 3‐D seismic amplitude variation with angle of incidence

AbstractWe use 3‐D multichannel seismic data to form partial angle P wave stacks and apply amplitude variation with angle (AVA) crossplotting to assess melt content and melt distribution within two large midcrustal off‐axis magma lenses (OAMLs) found along the East Pacific Rise from 9°37.5′N to 9°57′N. The signal envelope of the partial angle stacks suggests that both OAMLs are partially molten with higher average melt content and more uniform melt distribution in the southern OAML than in the northern OAML. For AVA crossplotting, the OAMLs are subdivided into seven ~1 km2 analysis windows. The AVA crossplotting results indicate that the OAMLs contain a smaller amount of melt than the axial magma lens (AML). For both OAMLs, a higher melt fraction is detected within analysis windows located close to the ridge axis than within the most distant windows. The highest average melt concentration is interpreted for the central sections of the OAMLs. The overall low OAML melt content could be indicative of melt lost due to recent off‐axis eruptions, drainage to the AML, or limited mantle melt supply. Based on the results of this and earlier bathymetric, morphological, geochemical, and geophysical investigations, we propose that the melt‐poor OAML state is largely the result of limited melt supply from the underlying mantle source reservoir with smaller contribution attributed to melt leakage to the AML. We hypothesize that the investigated OAMLs have a longer period of melt replenishment, lower eruption recurrence rates, and lower eruption volumes than the AML, though some could be single intrusion events.

Rock-physics diagnostics of an offshore gas field

Seismic reflections depend on the contrasts of the elastic properties of the subsurface and their 3D geometry. As a result, interpreting seismic data for petrophysical rock properties requires a theoretical rock-physics model that links the seismic response to a rock’s velocity and density. Such a model is based on controlled experiments in which the petrophysical and elastic rock properties are measured on the same samples, such as in the wellbore. Using data from three wells drilled through a clastic offshore gas reservoir, we establish a theoretical rock-physics model that quantitatively explains these data. The modeling is based on the assumption that only three minerals are present: quartz, clay, and feldspar. To have a single rock-physics transform to quantify the well data in the entire intervals under examination in all three wells, we introduced field-specific elastic moduli for the clay. We then used the model to correct the measured shear-wave velocity because it appeared to be unreasonably low. The resulting model-derived Poisson’s ratio is much smaller than the measured ratio, especially in the reservoir. The associated synthetic amplitude variation with offset response appears to be consistent with the recorded seismic angle stacks. We have shown how rock-physics modeling not only helps us to correct the well data, but also allows us to go beyond the settings represented in the wells and quantify the seismic signatures of rock properties and conditions varying in a wider range using forward seismic modeling.

Transdimensional seismic inversion using the reversible jump Hamiltonian Monte Carlo algorithm

Prestack or angle stack gathers are inverted to estimate pseudologs at every surface location for building reservoir models. Recently, several methods have been proposed to increase the resolution of the inverted models. All of these methods, however, require that the total number of model parameters be fixed a priori. We have investigated an alternate approach in which we allow the data themselves to choose model parameterization. In other words, in addition to the layer properties, the number of layers is also treated as a variable in our formulation. Such transdimensional inverse problems are generally solved by using the reversible jump Markov chain Monte Carlo (RJMCMC) approach, which is a tool for model exploration and uncertainty quantification. This method, however, has very low acceptance. We have developed a two-step method by combining RJMCMC with a fixed-dimensional MCMC called Hamiltonian Monte Carlo, which makes use of gradient information to take large steps. Acceptance probability for such a transition is also derived. We call this new method “reversible jump Hamiltonian Monte Carlo (RJHMC).” We have applied this technique to poststack acoustic impedance inversion and to prestack (angle stack) AVA inversion for estimating acoustic and shear impedance profiles. We have determined that the marginal posteriors estimated by RJMCMC and RJHMC are in good agreement. Our results demonstrate that RJHMC converges faster than RJMCMC, and it therefore can be a practical tool for inverting seismic data when the gradient can be computed efficiently.

Fluid identification method and application of pre-stack and post-stack integration based on seismic low-frequency

Seismic low-frequency component included more fluid information, which was still a focus in geophysics. Seismic pre-stack data contained more fluid information, but signal to noise ratio (SNR) was low. By comparison, seismic post-stack data had a high SNR, but some fluid information was lost during data processing. Different angel stacks reflected different fluid seismic amplitude, compared with near-angle stacks. Far angle stacks had more low frequency components, which were more benefit for fluid identification. Based on this principle, combined with the pre-stacks and post-stacks seismic data, more fluid information would be included. In this study, a new fluid identification method was proposed. This method combined the post-stack seismic and pre-stack seismic data, it took into consideration high SNR of the post-stack seismic data and the large fluid amplitude differences of the pre-stack different angles. Field testing indicated this method was quite effective in detecting fluids.

Azimuthal anisotropy analysis of P-wave seismic data and estimation of the orientation of the in situ stress fields: An example from the Rock-Springs uplift, Wyoming, USA

Estimating the orientation and magnitude of maximum and minimum horizontal in situ stress is important for characterizing naturally fractured, unconventional, and carbon-sequestered reservoirs. For naturally fractured reservoirs, they are needed to guide directional drilling; for unconventional reservoirs, they are used for optimal placements of hydraulic fractures; and for carbon-sequestered reservoirs, they are used to avoid fracturing of overlying seal rocks. In addition, a knowledge of stress fields can be used to induce fractures within the target reservoirs and enhance additional storage for carbon-sequestration experiments. The orientation and magnitude of in situ stress can be calculated at the well locations. For locations, away from the wells, analysis of the azimuthal dependence of the amplitude-variation-with-angle gradient or azimuthal angle stacks are used to quantify anisotropy, which are then related with well data and other geologic information for stress estimation. Such azimuthal analysis requires accurate conversion of offset-domain seismic data into angles. We use isotropic prestack waveform inversion for an accurate offset-to-angle transformation along different source-to-receiver azimuths followed by azimuthal analysis. Applying our method to the real seismic data from the Rock-Springs uplift, Wyoming, USA, and relating the results to the well data, we find that our results are favorably related to the orientation of the maximum in situ horizontal stress field measured at the well location.

GAS SAND DETECTION USING ROCK PHYSICS AND PRE-STACK SEISMIC INVERSION, EXAMPLE FROM OFFSHORE NILE DELTA, EGYPT

Well logs and three-dimensional (3-D) partial angle stacks and full angle stack seismic volumes are used inthis study with the purpose of detecting gas sands using rock physics and pre-stack inversion workflows.Integration of pre-stack inversion and rock physics analysis can improve the characterization of the latePliocene gas sandstone reservoir, offshore Nile Delta. The inversion was performed using a deterministicwavelet set. Rock physics was used to enhance the VP, VS, and density volumes from the inversion. The presentstudy performed in three phases: AVO analysis, pre-stack inversion, and lambda-mu-rho (LMR) analysis. Theresults from the different crossplots, such as P-Impedance vs. Vp/Vs, show that the gas sands are clearlyseparated from brine sands and shale. By maximizing the potential offered from the elastic properties such asλρ, μρ and Vp/Vs ratio we were able to define the limits and cutoffs which sufficiently separate the gas sandbodies. The resulted volumes were used to better define the late Pliocene reservoir and optimize a new welllocation. The pre-stack inversion and AVO/rock physics studies resulted in a new Gas Initial In Place (GIIP)calculation that was doubled in the P50 case from the original estimation based only on the seismic amplitudedata. The chance of success was increased and a new well is proposed to drain the gas in the eastern flank ofChannel 1.

Analisis AVO untuk Mengetahui Penyebaran Hidrokarbon Berdasarkan Faktor Fluida (Studi Kasus Lapangan “H” Formasi Talang Akar Cekungan Jawa Barat Utara)

<span>Amplitude versus offset (AVO) inversion analysis can be used to determine the spread of <span>hydrocarbons on seismic data. In this study we conducted AVO on reservoir layer Talang <span>Akar’s formation (TAF). AVO inversion results are angle stack, normal incident reflectivity <span>(intercept), gradient and fluid factor. Angle stack attribute analysis showed an AVO anomaly <span>in the reservoir TAF layer, amplitude has increased negative value from near angle stack to far <span>angle stack. The result of crossplot normal incident reflectivity (intercept) with gradient <span>indicates reservoir TAF layer including Class III AVO anomaly. While the analysis of fluid <span>factor attribute has a negative value thus reservoir TAF layer indicates a potential <span>hydrocarbon.</span></span></span></span></span></span></span></span><br /></span>

Target-oriented data conditioning for prestack inversion in an unconventional reservoir: A Canadian case study

Target-oriented data conditioning is a key part of any reservoir characterization workflow. Data conditioning is used to optimize the match between the synthetic data, used in the prestack inversion, and the real data. When this is done correctly, the accuracy and confidence of inversion results may be greatly improved. This is proved on prestack seismic inversion results from a resource play in Canada. The flow is broken down into prestack gather conditioning, which improves the signal-to-noise and gather flatness, and poststack conditioning, which further improves the coherency prior to applying spectral balancing. As a final key step, spatially variant amplitude balancing is used to calibrate the angle stacks to the expected background trend from the well synthetics. The combination of all steps is demonstrated via [Formula: see text] and mu-rho versus lambda-rho crossplots, between the inverted results and the well measurements, to provide a significant improvement on the resolution and accuracy of the final prestack inversion.

Stochastic inversion by matching to large numbers of pseudo-wells

We have developed a 1D stochastic algorithm for estimating reservoir properties, based on matching large numbers of pseudo-wells to seismic angle stacks. The pseudo-wells are part deterministic and part stochastic 1D stratigraphic profiles with consistent elastic and reservoir properties. Pseudo-wells are sampled from a prior distribution defined by the geological interpretation, a rock physics model and a model for the vertical statistics that provides close control of the lithofacies proportions. A new set of pseudo-wells, typically [Formula: see text] tied to the local stratigraphy, is constructed for each seismic trace. Synthetics, derived from the pseudo-wells using extended elastic impedance, are matched to either one or two seismic angle stacks, and the best matches are selected and averaged to provide a joint estimate of reservoir properties and impedances and the associated uncertainties. The algorithm has been tested on a number of data sets and validated by blind well ties. The algorithm is 1D with no additional constraints on spatial correlation beyond that provided by the seismic data. This restricts the maximum frequency to that of the seismic; however, it makes the algorithm highly parallelizable, allowing for large data sets to be inverted in a few hours given adequate computing resources. We envisage that this inversion algorithm could form the first part of a two-step process with the output used to constrain subsequent geostatistical modeling.

Prediction of elastic properties using seismic prestack inversion and neural network analysis

The use of artificial intelligence algorithms to solve geophysical problems is a recent development. Neural network analysis is one of these algorithms. It uses the information from multiple wells and seismic data to train a neural network to predict properties away from the well control. Neural network analysis can significantly improve the seismic inversion result when the outputs of the inversion are used as external attributes in addition to regular seismic attributes for training the network. We found that integration of prestack inversion and neural network analysis can improve the characterization of a late Pliocene gas sandstone reservoir. For inversion, the input angle stacks was conditioned to match the theoretical amplitude-variation-with-offset response. The inversion was performed using a deterministic wavelet set. Neural network analysis was then used to enhance the [Formula: see text], [Formula: see text], and density volumes from the inversion. The improvement was confirmed by comparisons with logs from a blind well.

Joint Bayesian wavelet and well-path estimation in the impedance domain

We addressed the problem of the well-to-seismic tie as a Bayesian inversion for the wavelet and well path in the impedance domain. The result of the joint inversion is a set of wavelets for multiple angle stacks, and a corresponding well path. The wavelet optimally links the impedance data along the well to the seismic data along the optimized well path in the seismic time domain. Starting with prior distribution for wavelet and well path, the method calculates the posterior distribution of conditioning the prior distributions with the seismic and well-log data. This is done by iteratively inverting the seismic data with the current wavelet, to obtain an impedance cube around the well. In a second step, the seismic impedances are projected onto the well path. By minimizing the misfit between the inverted seismic impedances and the impedances derived from the well log, the wavelet and well path are optimized. Comparing the well and seismic data in the impedance domain enables the method to work on short and noisy well logs. Another advantage of this method is its ability to derive wavelets for multiple angle stacks and multiple well locations simultaneously. We tested the method on synthetic and real data examples. The algorithm performed well in the synthetic examples, in which we had control over the modeling wavelet, and the wavelets derived for a real data example showed consistently good seismic-to-well ties for six angle stacks and seven wells. The main algorithm we developed was aimed to linearize the problem. We compared the posterior distribution of the linearized result with a sampling-based result in a real data example and found good agreement.

Visualisation and delineation techniques adopted from other industries to increase productivity of the G&G work in the oil and gas industry

Given the recent increase of seismic data quality owing to improvements in seismic acquisition and processing, it is surprising to realise that the oil and gas industry is still using standard desktop screens with 256 colour resolution software displays, and for most of the seismic representations, using only three types of colour bars (peak-trough, grey scale or rainbow) for human interpretation, comprehension and decision making processes. Knowing that these displays show 0.000006% of the details captured in 32 bit resolution data, it is a wonder: is the oil and gas industry using the available data to its maximum potential to decrease the risk of drilling dry wells? Astronomy and medical imaging tackled these issues long ago and inspired by them, the oil and gas industry is able to use a 24 bit colour space for representing seismic data in a more appealing way. These innovative seismic data representations are called colour blends and are created using sources such as frequency decomposition products, angle stacks, edge attributes, 4D vintages or any other seismic attributes colour-coded with primary colours. Colour blends have not yet become mainstream due to availability of the tools. The cognitive cybernetics approach allows a more balanced input between data driven processes, interpreter skills and guidance, and has recently been made available for use with colour blends—a breakthrough in interpretation. This extended abstract shows recent advances in these two techniques and how they benefit to the geological and geophysical work based on a case study from the Australian and New Zealand sector.

Joint Impedance and Facies Inversion – Seismic inversion redefined

In this paper we will first review the industry-standard simultaneous inversion method (which derives continuous impedances) and subsequently identify some pitfalls. We will then introduce our new Joint Impedance and Facies Inversion technology (which we call Ji-Fi for short in this paper), which overcomes these pitfalls by recasting the seismic inverse problem as mixed discrete/continuous. Having so captured the correct physics, we apply this first on a wedge model, followed by a case study, before drawing some conclusions. Note that in this paper, it is assumed that the seismic to be inverted is an ensemble of true amplitude partial angle stacks with corresponding wavelets derived from well ties.

Applying a new synchronous inversion of seismograms using maximum likelihood method and stochastic refinements to study ultra-thin oil-saturated reservoirs

Seismic inversion makes use of single or multi-dimensional seismic trace data to derive elastic properties from seismic amplitude data. These elastic properties are subsequently used to make inferences about seismic lithology, lithologic properties, and the presence of fluids. Most implementations of seismic inversions operate on limited stacked subsets of the seismic data. Common inputs to inversion methods include angle stacks, offset stacks, or multiple stacks of time-lapsed data. The implementation in this paper makes use of a synchronous pre-stack inversion based on the maximum likelihood algorithm (Anat Canning and Alex Malkin, 2009). The PMLI inversion is applicable to time and depth angle or offset seismograms. It is a full pre-stack inversion, which inverts each and every trace of an offset or angle gather and returns elastic properties. The PMLI inversion can also invert partial angular or offset sums, which can be created in any processing system. As a complete pre-stack inversion, PMLI provides higher accuracy and noise immunity as observed in the output results of Pw and Sw impedances, density, Vp/Vs, Poisson’s ratio, Lambda Rho (LR) and Mu Rho (MR). PMLI also incorporates a necessary and effective preprocessing step, which is performed on-the-fly as part of the inversion process. Because the PMLI method operates on gathers, it is easily and efficiently parallelized for multi-core and multi-node clusters and for shared memory architectures.