Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

High-resolution reservoir characterization by an acoustic impedance inversion of a Tertiary deltaic clinoform system in the North Sea

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. O57-O67 ◽  
Author(s):  
Daria Tetyukhina ◽  
Lucas J. van Vliet ◽  
Stefan M. Luthi ◽  
Kees Wapenaar
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Sampling Rate ◽  
Seismic Inversion ◽  
Acoustic Properties ◽  
The North ◽  
The North Sea ◽  
Well Data

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface, and potential reservoir units are often close to or below seismic resolution. For seismic inversion, it is therefore important to integrate the seismic data with higher resolution constraints obtained from well logs, whereby not only the acoustic properties are used but also the detailed layering characteristics. We have applied two inversion approaches for poststack, time-migrated seismic data to a clinoform sequence in the North Sea. Both methods are recursive trace-based techniques that use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and uses horizontal and vertical regularization parameters for lateral propagation. The first method has a low level of parameterization embedded in a geologic framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geologic settings of the reservoir; however, there is no explicit geologic significance and the method is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data.

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

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

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

Impact of phase rotation on reservoir characterization and implementation of seismic well tie technique for calibration offshore Nile Delta, Egypt

2020 ◽  
Vol 39 (5) ◽  
pp. 346-352
Author(s):  
Mohamed G. El-Behiry ◽  
Mohamed S. Al Araby ◽  
Ramy Z. Ragab
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Wavelet ◽  
Well Location ◽  
Formation Behavior ◽  
Dynamic Components ◽  
Well Data ◽  
Phase Amplitude ◽  
Seismic Anomalies

Seismic wavelets are dynamic components that result in a seismic trace when convolved with reflectivity series. The seismic wavelet is described by three components: amplitude, frequency, and phase. Amplitude and frequency are considered static because they mainly affect the appearance of a seismic event. Phase can have a large effect on seismic appearance by changing the way it describes the subsurface. Knowing the wavelet properties of certain seismic data facilitates the process of interpretation by providing an understanding of the appearance of regional geologic markers and hydrocarbon-bearing formation behavior. The process through which seismic data wavelets are understood is called seismic well tie. Seismic well tie is the first step in calibrating seismic data in terms of polarity and phase. It ensures that the seismic data are descriptive to regional markers, well markers, and discoveries (if they exist). The step connects well data to seismic data to ensure that the seismic correctly describes well results at the well location. It then extends the understanding of seismic behavior to the rest of the area covered by the seismic data. Good seismic well tie will greatly reduce uncertainties accompanying seismic interpretation. One important outcome of the seismic well tie process is understanding the phase of seismic data, which affects how seismic data will reflect a known geologic marker or hydrocarbon-bearing zone. This understanding can be useful in quantifying discoveries attached to seismic anomalies and extending knowledge from the well location to the rest of the area covered by seismic data.

Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Jie Zhang ◽  
Xuehua Chen ◽  
Wei Jiang ◽  
Yunfei Liu ◽  
He Xu
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Model Parameters ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Hydrocarbon Reservoir ◽  
Convolution Model

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with the medium velocity increase and compresses with the medium velocity decrease. The commonly used convolution model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We develop a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well log data. This method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolution model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. Additionally, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with least-squares-based methods. The results indicate that the proposed method is effective and stable even for data with a low S/N.

Seismic well tie by aligning impedance log with inverted impedance from seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T917-T925
Author(s):  
Bo Zhang ◽  
Yahua Yang ◽  
Yong Pan ◽  
Hao Wu ◽  
Danping Cao
Keyword(s):  
Seismic Data ◽  
Dynamic Time Warping ◽  
Real Data ◽  
Seismic Inversion ◽  
Synthetic Seismogram ◽  
Well Logs ◽  
Time Warping ◽  
Seismic Wavelet ◽  
Dynamic Time ◽  
Depth Relationship

The accuracy of seismic inversion is affected by the seismic wavelet and time-depth relationship generated by the process of the seismic well tie. The seismic well tie is implemented by comparing the synthetic seismogram computed from well logs and the poststack seismogram at or nearby the borehole location. However, precise waveform matching between the synthetic seismogram and the seismic trace does not guarantee an accurate tie between the elastic properties contained represented by the seismic data and well logs. We have performed the seismic well tie using the impedance log and the impedance inverted from poststack seismic data. We use an improved dynamic time warping to align the impedance log and impedance inverted from seismic data. Our workflow is similar to the current procedure of the seismic well tie except that the matching is implemented between the impedance log and the inverted impedance. The current seismic well-tie converges if there is no visible changes for the wavelets and time-depth relationship in the previous and current tying loops. Similarly, our seismic well tie converges if there are no visible changes for the wavelets, inverted impedance, and time-depth relationship in the previous and current tying loops. The real data example illustrates that more accurate inverted impedance is obtained by using the new wavelet and time-depth relationship.

Consistent Downscaling of Seismic Inversion Thicknesses to Cornerpoint Flow Models

SPE Journal ◽  
2008 ◽  
Vol 13 (04) ◽  
pp. 412-422
Author(s):  
Subhash Kalla ◽  
Christopher D. White ◽  
James Gunning ◽  
Michael Glinsky
Keyword(s):  
Covariance Matrix ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Flow Simulation ◽  
Rock Physics ◽  
Bayesian Framework ◽  
Seismic Inversion ◽  
Total Thickness ◽  
Flow Models ◽  
Well Data

Summary Accurate reservoir simulation requires data-rich geomodels. In this paper, geomodels integrate stochastic seismic inversion results (for means and variances of packages of meter-scale beds), geologic modeling (for a framework and priors), rock physics (to relate seismic to flow properties), and geostatistics (for spatially correlated variability). These elements are combined in a Bayesian framework. The proposed workflow produces models with plausible bedding geometries, where each geomodel agrees with seismic data to the level consistent with the signal-to-noise ratio of the inversion. An ensemble of subseismic models estimates the means and variances of properties throughout the flow simulation grid. Grid geometries with possible pinchouts can be simulated using auxiliary variables in a Markov chain Monte Carlo (MCMC) method. Efficient implementations of this method require a posterior covariance matrix for layer thicknesses. Under assumptions that are not too restrictive, the inverse of the posterior covariance matrix can be approximated as a Toeplitz matrix, which makes the MCMC calculations efficient. The proposed method is examined using two-layer examples. Then, convergence is demonstrated for a synthetic 3D, 10,000 trace, 10 layer cornerpoint model. Performance is acceptable. The Bayesian framework introduces plausible subseismic features into flow models, whilst avoiding overconstraining to seismic data, well data, or the conceptual geologic model. The methods outlined in this paper for honoring probabilistic constraints on total thickness are general, and need not be confined to thickness data obtained from seismic inversion: Any spatially dense estimates of total thickness and its variance can be used, or the truncated geostatistical model could be used without any dense constraints. Introduction Reservoir simulation models are constructed from sparse well data and dense seismic data, using geologic concepts to constrain stratigraphy and property variations. Reservoir models should integrate spare, precise well data and dense, imprecise seismic data. Because of the sparseness of well data, stochastically inverted seismic data can improve estimates of reservoir geometry and average properties. Although seismic data are densely distributed compared to well data, they are uninformative about meter-scale features. Beds thinner than about 1/8 to 1/4 the dominant seismic wavelength cannot be resolved in seismic surveys (Dobrin and Savit 1988; Widess 1973). For depths of ˜3000 m, the maximum frequency in the signal is typically about 40 Hz, and for average velocities of ˜2,000 m/s, this translates to best resolutions of about 10 m. Besides the limited resolution, seismic-derived depths and thicknesses are uncertain because of noise in the seismic data and uncertainty in the rock physics models (Gunning and Glinsky 2004, 2006). This resolution limit and uncertainties associated with seismic depth and thickness estimates have commonly limited the use of seismic data to either inferring the external geometry or guiding modeling of plausible stratigraphic architectures of reservoirs (Deutsch et al. 1996). In contrast, well data reveal fine-scale features but cannot specify interwell geometry. To build a consistent model, conceptual stacking and facies models must be constrained by well and seismic data. The resulting geomodels must be gridded for flow simulation using methods that describe stratal architecture flexibly and efficiently.

The discrete prolate spheroidal sequences and a series expansion for seismic wavelets

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. V119-V132 ◽  
Author(s):  
Michael Greene
Keyword(s):  
Series Expansion ◽  
Seismic Data ◽  
Robust Method ◽  
Prolate Spheroidal ◽  
Seismic Wavelet ◽  
Ricker Wavelet ◽  
Band Limited ◽  
Well Data ◽  
Definition Of ◽  
Logical Extension

The discrete prolate spheroidal sequences (DPSSs) are a set of time-limited, band-limited, and mutually orthogonal sequences. I exploit their favorable properties to introduce a series expansion for seismic wavelets. By modifying the definition of the DPSSs, I generate new sequences that are tailored to the character of the seismic wavelet but retain the DPSSs’ favorable properties. I show that a series expansion composed of these new sequences can be thought of as a logical extension of a familiar practice, assuming the wavelet is a linear combination of a Ricker wavelet and its Hilbert transform ([Formula: see text] rotation). Two examples, one synthetic and one real, show how the series expansion can be used to tie well data to seismic data. This leads to a robust method to determine a wavelet. The examples also illustrate the properties of the series expansion, some of which can be related to familiar geophysical concepts such as thin-bed tuning. I conclude that this series expansion is a natural and useful way to describe the seismic wavelet.

Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

2021 ◽  
Author(s):  
Khalid Obaid ◽  
Muhammad Aamir ◽  
Tarek Yehia Nafie ◽  
Omar Aly ◽  
Widad Krissat ◽  
...  
Keyword(s):  
Seismic Data ◽  
Drilling Fluid ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs ◽  
Abu Dhabi ◽  
Residual Noise ◽  
Data Conditioning ◽  
Well Data

Abstract Rock physics/seismic inversion is a powerful tool that deliver information about intra-wells rocks elastic attributes and reservoir properties such as porosity, saturation and rock lithology classification. In principle, inversion is like an engine that should be fueled by proper input quality of both seismic and well data. As for the well data, sonic and density logs measure the rock properties a few inches from the borehole. Reliability of sonic transit-time and bulk density logs can be affected by large and rapid variation in the diameter and shape of the borehole cross-section, as well as the process of drilling fluid invasion. The basic assumption for acoustic well logs editing and conditioning is to use other recorded logs (not affected by bad-hole conditions) in a Multivariate-Regression Algorithm. In addition, Fluid Substitution was implemented to correct for the mud invasion that affects the acoustic and elastic properties based on the PVT data for fluid properties computation. The logs were then quality checked by multiple cross-plotting comparisons to the standard Rock-physics trends templates. As for seismic data, there are several factors affecting the quality of surface seismic data including the presence of residual noise and multiples contamination that caused improper amplitude balancing. Optimizing the seismic data processing for the inversion studies require reviewing and conditioning the seismic gathers and pre-stack volumes, guided by a deterministic seismic-to-well tie analysis after every major stage of the processing sequence. The applied processes are mainly consisting of Curvelet domain noise attenuation to attenuate residual noise. This was followed by high resolution Radon anti-multiple to attenuate residual surface multiples and Extended interbed multiple prediction to attenuate interbed multiples. In addition, Offset dependent amplitude and spectral balancing were applied to maintain the seismic amplitudes fidelity. This paper will illustrate a case from Abu Dhabi where data conditioning results improved the Hydrocarbon saturated carbonates vs brine saturated carbonate and the lithology predictions, leading to optimizing field development plans and drilling operations.

Shaly sand rock physics analysis and seismic inversion implication

2014 ◽  
Vol 54 (2) ◽  
pp. 503
Author(s):  
Adi Widyantoro ◽  
Matthew Saul
Keyword(s):  
Seismic Data ◽  
Oil And Gas ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Elastic Response ◽  
Seismic Amplitude ◽  
Cap Rock ◽  
Shaly Sand ◽  
Well Data ◽  
Stiffening Effect

The analysis of well data from the Enfield field of the Exmouth Sub-basin, WA, indicates that both cementation and pore-filling clay appear to have a stiffening effect on the reservoir sands. The elastic contrast between brine sand and the overlying shale is often small and the large amplitudes observed from seismic data are associated with hydrocarbon content. More detailed rock physics and depth trend analysis of elastic and petrophysical properties, however, indicate significant spatial variability in the cap rock shales across the field with different sand shale mixtures, causing changes in the elastic response of the rock. Areas where shales are softer produce weak seismic amplitude contrasts even with high hydrocarbon saturation; the amplitude response being similar to areas with stiffer shales and brine-filled sands. The variations in reservoir quality are, therefore, masked by the distribution of the brine, oil and gas, as well as the variations in the cap rock. The Enfield rock physics analysis provides an example of reducing amplitude ambiguity over lithology-fluid variation and improves the chance of successful interpretation of the results of seismic inversion.

Time-variant wavelet extraction with a local-attribute-based time-frequency decomposition for seismic inversion

2017 ◽  
Vol 5 (1) ◽  
pp. SC9-SC16 ◽  
Author(s):  
Rui Zhang ◽  
Sergey Fomel
Keyword(s):  
Frequency Spectrum ◽  
Seismic Data ◽  
Field Data ◽  
Seismic Signal ◽  
Seismic Inversion ◽  
Kernel Matrix ◽  
Time Frequency ◽  
Seismic Wavelet ◽  
Frequency Decomposition ◽  
Noise Contamination

Seismic impedance inversion has been widely used to estimate subsurface properties. Conventional inversion assumes that seismic data are the convolution result of seismic wavelet and reflectivity, implying that seismic data are stationary when a constant wavelet is considered. However, seismic data are nonstationary because of noise contamination and attenuation during wave propagation, which means that the frequency spectrum of the seismic signal changes from shallow to deep formations. We have developed a time-variant wavelet extraction method by using a local-attribute-based spectral decomposition technique. Time-variant wavelets are generated according to the local frequency spectrum, which can be used to construct a time-variant wavelet kernel matrix. By using this time-variant kernel matrix, we can obtain a better correlation between synthetic and extracted seismograms than by using constant wavelet on a field data example. Using this example, we have also compared the time-variant and constant wavelets for inverting the field data to estimate subsurface acoustic impedance. Our results showed improved resolution and a better fit to well-log-measured impedance by using the time-variant wavelets.

Sign in / Sign up

Export Citation Format

Share Document