Looking ahead with vertical seismic profiles

Geophysics ◽  
1994 ◽  
Vol 59 (8) ◽  
pp. 1182-1191 ◽  
Author(s):  
Michael A. Payne
Keyword(s):  
Low Frequency ◽  
Well Log ◽  
Seismic Profiles ◽  
Seismic Section ◽  
Data Set ◽  
Log Data ◽  
Frequency Information ◽  
Vertical Seismic Profiles ◽  
Look Ahead ◽  
Vsp Data

Several operations enhance our ability to predict the subsurface below the bottom total depth (TD) of the well when applied to zero‐offset vertical seismic profiling (VSP) data. Other key issues regarding the use of VSP data in this fashion are resolution and look‐ahead distance. An impedance log is the most useful form for presenting VSP data to look ahead of the drill bit. The VSP composite trace must first tie reliably to the surface seismic section and to the well log synthetic seismogram. The impedance log is obtained by inverting this VSP composite trace. However, before performing the inversion, we need to (1) correct the composite trace for attenuation effects below TD and (2) input velocities to provide low‐frequency information. An exponential gain function applied to the VSP data below TD adequately compensates for the loss of amplitude caused by attenuation. A calibration of the seismically derived velocities with VSP velocities yields the necessary low‐frequency information. These concepts are illustrated using a field data set and its subset truncated above TD. The output of these operations on the VSP data are compared to well log data. The question of resolution with these data was determined with a model VSP data set based on the well log data. The investigations indicate that the resolution attainable from look‐ahead data is on the order of 50–75 ft (15–23 m). This is one‐quarter seismic wavelength for the frequencies present in these data. In addition, the maximum look‐ahead distance for these data is shown to be easily 2000 ft (600) m and, perhaps, 4000 ft (1200 m5). By way of illustration, the techniques described and investigated 6were applied to an offshore VSP data set to yield an impedance log. After calibrating this curve with the well log data, the base of the target sand was correctly identified below TD. This prediction successfully yielded the thickness of the sand. Individual zones within the sand unit were identified with less confidence.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Twelve years of vertical birefringence in nine‐component VSP data

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 582-597 ◽  
Author(s):  
Donald F. Winterstein ◽  
Gopa S. De ◽  
Mark A. Meadows
Keyword(s):  
Seismic Data ◽  
Azimuthal Anisotropy ◽  
Vertical Shear ◽  
S Wave ◽  
Seismic Profiles ◽  
Surface Reflection ◽  
Vertical Seismic Profiles ◽  
Reflection Seismic ◽  
San Andreas ◽  
Vsp Data

Since 1986, when industry scientists first publicly showed data supporting the presence of azimuthal anisotropy in sedimentary rock, we have studied vertical shear‐wave (S-wave) birefringence in 23 different wells in western North America. The data were from nine‐component vertical seismic profiles (VSPs) supplemented in recent years with data from wireline crossed‐dipole logs. This paper summarizes our results, including birefringence results in tabular form for 54 depth intervals in 19 of those 23 wells. In the Appendix we present our conclusions about how to record VSP data optimally for study of vertical birefringence. We arrived at four principal conclusions about vertical S-wave birefringence. First, birefringence was common but not universal. Second, birefringence ranged from 0–21%, but values larger than 4% occurred only in shallow formations (<1200 m) within 40 km of California’s San Andreas fault. Third, at large scales birefringence tended to be blocky. That is, both the birefringence magnitude and the S-wave polarization azimuth were often consistent over depth intervals of several tens to hundreds of meters but then changed abruptly, sometimes by large amounts. Birefringence in some instances diminished with depth and in others increased with depth, but in almost every case a layer near the surface was more birefringent than the layer immediately below it. Fourth, observed birefringence patterns generally do not encourage use of multicomponent surface reflection seismic data for finding fractured hydrocarbon reservoirs, but they do encourage use of crossed‐dipole logs to examine them. That is, most reservoirs were birefringent, but none we studied showed increased birefringence confined to the reservoir.

Measurements of attenuation from vertical seismic profiles by iterative modeling

Geophysics ◽  
1985 ◽  
Vol 50 (6) ◽  
pp. 931-949 ◽  
Author(s):  
Michel Dietrich ◽  
Michel Bouchon
Keyword(s):  
Velocity Dispersion ◽  
Synthetic Data ◽  
Layered Media ◽  
Spectral Ratio ◽  
Ratio Method ◽  
Seismic Profiles ◽  
Vertical Seismic Profiles ◽  
Anelastic Attenuation ◽  
Vsp Data ◽  
Intrinsic Dissipation

We present a numerical simulation of vertical seismic profiles (VSP) using the discrete horizontal wavenumber representation of seismic wave fields. The theoretical seismograms are computed in the acoustic case for flat layered media, and they include the effects of absorption and velocity dispersion. A study using the synthetic seismograms was conducted to investigate the accuracy and resolution of attenuation measurements from VSP data. It is shown that in finely layered media estimates of the anelastic attenuation obtained by use of the reduced spectral ratio method are usually inaccurate when the attenuation is measured over a small vertical extent. An iterative method is presented which improves the resolution of the measurements of intrinsic dissipation. This method allows determination for synthetic data of the quality factor over depth intervals of about one wavelength of the dominant seismic frequency.

Facies — The drivers for modern inversions

2020 ◽  
Vol 39 (2) ◽  
pp. 102-109
Author(s):  
John Pendrel ◽  
Henk Schouten
Keyword(s):  
Bayesian Analysis ◽  
Prior Information ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Prior Probabilities ◽  
Physics Model ◽  
Rock Physics Model

It is common practice to make facies estimations from the outcomes of seismic inversions and their derivatives. Bayesian analysis methods are a popular approach to this. Facies are important indicators of hydrocarbon deposition and geologic processes. They are critical to geoscientists and engineers. The application of Bayes’ rule maps prior probabilities to posterior probabilities when given new evidence from observations. Per-facies elastic probability density functions (ePDFs) are constructed from elastic-log and rock-physics model crossplots, over which inversion results are superimposed. The ePDFs are templates for Bayesian analysis. In the context of reservoir characterization, the new information comes from seismic inversions. The results are volumes of the probabilities of occurrences of each of the facies at all points in 3D space. The concepts of Bayesian inference have been applied to the task of building low-frequency models for seismic inversions without well-log interpolation. Both a constant structurally compliant elastic trend approach and a facies-driven method, where models are constructed from per-facies trends and initial facies estimates, have been tested. The workflows make use of complete 3D prior information and measure and account for biases and uncertainties in the inversions and prior information. Proper accounting for these types of effects ensures that rock-physics models and inversion data prepared for reservoir property analysis are consistent. The effectiveness of these workflows has been demonstrated by using a Gulf of Mexico data set. We have shown how facies estimates can be effectively used to build reasonable low-frequency models for inversion, which obviate the need for well-log interpolation and provide full 3D variability. The results are more accurate probability-based net-pay estimates that correspond better to geology. We evaluate the workflows by using several measures including precision, confidence, and probabilistic net pay.

A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA147-WA158
Author(s):  
Kaibo Zhou ◽  
Jianyu Zhang ◽  
Yusong Ren ◽  
Zhen Huang ◽  
Luanxiao Zhao
Keyword(s):  
Decision Tree ◽  
Classification Performance ◽  
Training Data ◽  
Gradient Boosting ◽  
Well Log ◽  
Learning Approaches ◽  
Data Set ◽  
Log Data ◽  
Lithology Identification ◽  
Exploration And Production

Lithology identification based on conventional well-logging data is of great importance for geologic features characterization and reservoir quality evaluation in the exploration and production development of petroleum reservoirs. However, there are some limitations in the traditional lithology identification process: (1) It is very time consuming to build a model so that it cannot realize real-time lithology identification during well drilling, (2) it must be modeled by experienced geologists, which consumes a lot of manpower and material resources, and (3) the imbalance of labeled data in well-log data may reduce the classification performance of the model. We have developed a gradient boosting decision tree (GBDT) algorithm combining synthetic minority oversampling technique (SMOTE) to realize fast and automatic lithology identification. First, the raw well-log data are normalized by maximum and minimum normalization algorithm. Then, SMOTE is adopted to balance the number of samples in each class in training process. Next, a lithology identification model is built by GBDT to fit the preprocessed training data set. Finally, the built model is verified with the testing data set. The experimental results indicate that the proposed approach improves the lithology identification performance compared with other machine-learning approaches.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Multichannel Wiener deconvolution of vertical seismic profiles

Geophysics ◽  
1994 ◽  
Vol 59 (10) ◽  
pp. 1500-1511 ◽  
Author(s):  
Jakob B. U. Haldorsen ◽  
Douglas E. Miller ◽  
John J. Walsh
Keyword(s):  
Least Squares ◽  
Amplitude Spectrum ◽  
Seismic Source ◽  
Seismic Profile ◽  
Signal Spectrum ◽  
Seismic Profiles ◽  
Vertical Seismic Profiles ◽  
Vsp Data ◽  
Zero Offset ◽  
Noise Signature

We describe a technique for performing optimal, least‐squares deconvolution of vertical seismic profile (VSP) data. The method is a two‐step process that involves (1) estimating the source signature and (2) applying a least‐squares optimum deconvolution operator that minimizes the noise not coherent with the source signature estimate. The optimum inverse problem, formulated in the frequency domain, gives as a solution an operator that can be interpreted as a simple inverse to the estimated aligned signature multiplied by semblance across the array. An application to a zero‐offset VSP acquired with a dynamite source shows the effectiveness of the operator in attaining the two conflicting goals of adaptively spiking the effective source signature and minimizing the noise. Signature design for seismic surveys could benefit from observing that the optimum deconvolution operator gives a flat signal spectrum if and only if the seismic source has the same amplitude spectrum as the noise.

Tomographic determination of three‐dimensional seismic velocity structure using well logs, vertical seismic profiles, and surface seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (8) ◽  
pp. 1085-1098 ◽  
Author(s):  
Stephen K. L. Chiu ◽  
Robert R. Stewart
Keyword(s):  
Velocity Structure ◽  
Seismic Velocity ◽  
Random Noise ◽  
Three Dimensional ◽  
Real Data ◽  
Well Logs ◽  
Seismic Profiles ◽  
Vertical Seismic Profiles ◽  
The Earth ◽  
Vsp Data

A tomographic technique (traveltime inversion) has been developed to obtain a two‐ or three‐dimensional velocity structure of the subsurface from well logs, vertical seismic profiles (VSP), and surface seismic measurements. The earth was modeled by continuous curved interfaces (polynomial or sinusoidal series), separating regions of constant velocity or transversely isotropic velocity. Ray tracing for each seismic source‐receiver pair was performed by solving a system of nonlinear equations which satisfy the generalized Snell’s law. Surface‐to‐borehole and surface‐to‐surface rays were included. A damped least‐squares formulation provided the updating of the earth model by minimizing the difference between the traveltimes picked from the real data and calculated traveltimes. Synthetic results indicated the following conclusions. For noise‐free cases, the inversion converged closely from the initial guess to the true model for either surface or VSP data. Adding random noise to the observations and performing the inversion indicated that (1) using surface data alone allows reconstruction of the broad velocity structure but with some inaccuracy; (2) using VSP data alone gives a very accurate but laterally limited velocity structure; and (3) the integration of both data sets produces a more laterally extensive, accurate image of the subsurface. Finally, a field example illustrates the viability of the method to construct a velocity structure from real data.

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