scholarly journals Assessment of updated low frequency model in delineating bypassed hydrocarbon reservoir: A 4D seismic study of ’X’ - field, offshore, Niger Delta, Nigeria

2020 ◽  
Vol 13 (36) ◽  
pp. 3738-3753 ◽  
Author(s):  
Aniefiok Sylvester Akpan ◽  
Keyword(s):  
Acoustic Impedance ◽  
Deterministic Model ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Fluid Replacement ◽  
Inversion Method ◽  
Frequency Model ◽  
Hydrocarbon Reservoir ◽  
Model Based ◽  
4D Seismic

Aim/objectives: The aim of this research is, to use Time lapse (4D) seismic and investigate the influence of low frequency update in deterministic model-based seismic inversion employed in delineating a prospect saturated with bypassed hydrocarbon accumulation. Method: The dataset employed in this study incorporates 4D seismic volumes with fifteen (15) years production, interval between 2001 baseline and 2016 monitor seismic vintages. The inversion was carried out using full bandwidth of the updated low frequency and bandpass filtered low frequency approaches. The seismic vintages (baseline and monitor) were simultaneously inverted into acoustic impedance volumes for the two approaches. The formation fluid and lithology were discriminated through fluid replacement modelling (FRM) based on the colour separation between brine and gas saturation scenarios. Findings: The two inversion methods employed reveal six (6) zones suspected to be saturated with bypassed hydrocarbons. The delineated bypassed zones are masked in the full bandwidth approach,depicting the effect of the updated low frequency model. Meanwhile, the bandpass filtered approach result presents a better delineated bypassed reservoir as the zones are more pronounced when compared with the full bandwidth approach. Porosity estimate reveals that the bandpass filtered approach is characterized with excellent porosity in the suspected bypassed zones. The results equally gave more reliable and full delineated bypassed zones. Originality and novelty: The dataset employed in this study were obtained from a producing hydrocarbon field which, interest is to maximize the production of oil/gas. The study will bridge the inherent gab observed in using model-based seismic inversion approach to analyse and interpret seismic data in order to delineate hydrocarbon prospects. The research reveals that,the model-based seismic inversion method is still very effective in delineating hydrocarbon prospect when the updated low frequency is bandpass filtered to remove the model effect which influences the inverted acoustic impedance results. Keywords: Porosity; frequency; bypassed; reservoir and impedance

Incorporation of geology with rock physics enables subsalt poststack inversion: A case study in the Gulf of Mexico

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB53-WB65 ◽  
Author(s):  
Huyen Bui ◽  
Jennifer Graham ◽  
Shantanu Kumar Singh ◽  
Fred Snyder ◽  
Martiris Smith
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Blind Test ◽  
Frequency Model ◽  
Layer Sequence

One of the main goals of seismic inversion is to obtain high-resolution relative and absolute impedance for reservoir properties prediction. We aim to study whether the results from seismic inversion of subsalt data are sufficiently robust for reliable reservoir characterization. Approximately [Formula: see text] of poststack, wide-azimuth, anisotropic (vertical transverse isotropic) wave-equation migration seismic data from 50 Outer Continental Shelf blocks in the Green Canyon area of the Gulf of Mexico were inverted in this study. A total of four subsalt wells and four subsalt seismic interpreted horizons were used in the inversion process, and one of the wells was used for a blind test. Our poststack inversion method used an iterative discrete spike inversion method, based on the combination of space-adaptive wavelet processing to invert for relative acoustic impedance. Next, the dips were estimated from seismic data and converted to a horizon-like layer sequence field that was used as one of the inputs into the low-frequency model. The background model was generated by incorporating the well velocities, seismic velocity, seismic interpreted horizons, and the previously derived layer sequence field in the low-frequency model. Then, the relative acoustic impedance volume was scaled by adding the low-frequency model to match the calculated acoustic impedance logs from the wells for absolute acoustic impedance. Finally, the geological information and rock physics data were incorporated into the reservoir properties assessment for sand/shale prediction in two main target reservoirs in the Miocene and Wilcox formations. Overall, the poststack inversion results and the sand/shale prediction showed good ties at the well locations. This was clearly demonstrated in the blind test well. Hence, incorporating rock physics and geology enables poststack inversion in subsalt areas.

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.

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>

scholarly journals Application of model-based inversion technique in a field in the coastal swamp depobelt, Niger delta

2018 ◽  
Vol 6 (1) ◽  
pp. 122
Author(s):  
Okoli Austin ◽  
Onyekuru Samuel I. ◽  
Okechukwu Agbasi ◽  
Zaidoon Taha Abdulrazzaq
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Seismic Reflection Data ◽  
Model Based ◽  
Reflection Data ◽  
X 32 ◽  
Niger Delta Basin ◽  
Bypassed Oil

Considering the heterogeneity of the reservoir sands in the Niger Delta basin which are primary causes of low hydrocarbon recovery efficiency, poor sweep, early breakthrough and pockets of bypassed oil there arises a need for in-depth quantitative interpretation and more analysis to be done on seismic data to achieve a reliable reservoir characterization to improve recovery, plan future development wells within field and achieve deeper prospecting for depths not penetrated by the wells and areas far away from well locations. An effective tool towards de-risking prospects is seismic inversion which transforms a seismic reflection data to a quantitative rock-property description of a reservoir. The choice of model-based inversion in this study was due to well control, again considering the heterogeneity of the sands in the field. X-26, X-30, and X-32 were used to generate an initial impedance log which is used to update the estimated reflectivity from which we would obtain our inverted volumes. Acoustic impedance volumes were generated and observations made were consistent with depth trends established for the Niger Delta basin, inverted slices of Poisson impedances validated the expected responses considering the effect of compaction. This justifies the use of inversion method in further characterizing the plays identified in the region.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

Accurate poststack acoustic-impedance inversion by well-log calibration

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. R59-R67 ◽  
Author(s):  
Igor B. Morozov ◽  
Jinfeng Ma
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Joint Inversion ◽  
Low Frequency ◽  
Real Data ◽  
Data Sets ◽  
Frequency Model ◽  
Physical Constraints ◽  
Impedance Inversion ◽  
Reliable Solution

The seismic-impedance inversion problem is underconstrained inherently and does not allow the use of rigorous joint inversion. In the absence of a true inverse, a reliable solution free from subjective parameters can be obtained by defining a set of physical constraints that should be satisfied by the resulting images. A method for constructing synthetic logs is proposed that explicitly and accurately satisfies (1) the convolutional equation, (2) time-depth constraints of the seismic data, (3) a background low-frequency model from logs or seismic/geologic interpretation, and (4) spectral amplitudes and geostatistical information from spatially interpolated well logs. The resulting synthetic log sections or volumes are interpretable in standard ways. Unlike broadly used joint-inversion algorithms, the method contains no subjectively selected user parameters, utilizes the log data more completely, and assesses intermediate results. The procedure is simple and tolerant to noise, and it leads to higher-resolution images. Separating the seismic and subseismic frequency bands also simplifies data processing for acoustic-impedance (AI) inversion. For example, zero-phase deconvolution and true-amplitude processing of seismic data are not required and are included automatically in this method. The approach is applicable to 2D and 3D data sets and to multiple pre- and poststack seismic attributes. It has been tested on inversions for AI and true-amplitude reflectivity using 2D synthetic and real-data examples.

scholarly journals Assessment of Paleocene to lower Oligocene formations and basement to estimate the potential hydrocarbon reservoirs using seismic inversion: a case study in the Upper Assam Shelf, India

2021 ◽  
Author(s):  
Neha Rai ◽  
Dip Kumar Singha ◽  
Rima Chatterjee
Keyword(s):  
Acoustic Impedance ◽  
Seismic Inversion ◽  
High Porosity ◽  
North East India ◽  
Hydrocarbon Reservoir ◽  
North East ◽  
Lower Oligocene ◽  
Potential Reservoir ◽  
Well Data ◽  
Granite Basement

AbstractThe upper Assam shelf is a self-slope basin in north-east India, filled with nearly 7 km of sedimentary rocks of tertiary period with the granite basement and various oil fields along the border of the Naga thrust. The major producing fields are structural and strati-structural. The study area is placed in between the Mikir hills and Naga thrust. The objective of the study is to identify potential hydrocarbon reservoir zones in the geologically complex south upper of the Assam shelf using estimates of acoustic impedance and porosity derived by 3D post-stack seismic inversion. Well data, such as sonic velocity and density logs, from two wells (namely, KA and TE) are used in the inversion and validation of results. Inversion results are used to build a geological model in the form of acoustic impedance from which we derive 3D porosity cube which are used for hydrocarbon potential in the Paleocene to lower Oligocene sands, and the Precambrian basement. Although the amplitude maps provide an indication of potential reservoirs, the extent of these zones are much better identified in the inverted impedance maps and the corresponding estimated high-porosity zones. The analysis predicted the potential reservoir rocks in the Sylhet, Kopili and Barail formations, in which the Sylhet and Kopili appear to have good potential zones. Near the vicinity of the Naga thrust belt, the proximity of potential reservoir is predicted in the Kopili, Sylhet formation and in the fractured basement, respectively.

Improved seismic inversion and reservoir description of the Ichthys gas-condensate field, Browse Basin, Western Australia

2010 ◽  
Vol 50 (2) ◽  
pp. 716
Author(s):  
Masamichi Fujimoto ◽  
Takeshi Yoshida ◽  
Andrew Long
Keyword(s):  
Low Frequency ◽  
Seismic Inversion ◽  
Gas Condensate ◽  
Sensor Technology ◽  
Frequency Model ◽  
Porosity Distribution ◽  
Low Frequencies ◽  
Simultaneous Inversion ◽  
Dual Sensor ◽  
Well Data

Seismic inversion has become a standard geophysical tool to enhance seismic resolution, predict the reservoir porosity distribution, and to discriminate between reservoir and non-reservoir pay zones. Conventional seismic data does not record the low frequencies necessary for inversion. To enable a complete bandwidth, low frequencies are modelled from well data and are typically interpolated throughout the volume using seismic velocities. This often causes the resultant porosity distribution calculated from the inverted P-impedance to be biased by the well data and the geometry of well locations. Dual-sensor GeoStreamer technology was used to acquire a regional multi-client 2D survey by PGS in 2008, including some lines over the Ichthys gas-condensate field in the Browse Basin. Dual-sensor streamer processing recovers a wider frequency bandwidth than conventional seismic. Receiver ghost removal combined with deep streamer towing simultaneously boosts both the low and high frequencies. The improved bandwidth enables a higher quality of velocity analysis, which further improves resolution throughout the section. Simultaneous inversion of the data validated the uplift of the low frequency data, and significantly reduced the bias towards well data for the low frequency model. The resultant P-impedance data demonstrated an excellent tie to well data. The dual-sensor technology promises to improve the description of the porosity distribution within our reservoir model.

Poststack Seismic Inversion Using A Patch-based Gaussian Mixture Model

Geophysics ◽  
2021 ◽  
pp. 1-102
Author(s):  
Lingqian Wang ◽  
Hui Zhou ◽  
Hengchang Dai ◽  
Bo Yu ◽  
Wenling Liu ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Acoustic Impedance ◽  
Structural Information ◽  
Gaussian Mixture ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Borehole Data ◽  
Inversion Result ◽  
Band Limited

Seismic inversion is a severely ill-posed problem, because of noise in the observed record, band-limited seismic wavelets, and the discretization of a continuous medium. Regularization techniques can impose certain characteristics on inversion results based on prior information in order to obtain a stable and unique solution. However, it is difficult to find an appropriate regularization to describe the actual subsurface geology. We propose a new acoustic impedance inversion method via a patch-based Gaussian mixture model (GMM), which is designed using available well logs. In this method, firstly, the non-local means (NLM) method estimates acoustic impedance around wells in terms of the similarity of local seismic records. The extrapolated multichannel impedance are then decomposed into impedance patches. Using patched data rather than a window or single trace for training samples to obtain the GMM parameters, which contain local lateral structural information, can provide more impedance structure details and enhance the stability of the inversion result. Next, the expectation maximization (EM) algorithm is used to obtain the GMM parameters from the patched data. Finally, we apply the alternating direction method of multipliers (ADMM) to solve the conventional Bayesian inference illustrating the role of regularization, and construct the objective function using the GMM parameters. Therefore, the inversion results are compliant with the local structural features extracted from the borehole data. Both synthetic and field data tests validate the performance of our proposed method. Compared with other conventional inversion methods, our method shows promise in providing a more accurate and stable inversion result.

Interpretation of full-azimuth broadband land data from Saudi Arabia and implications for improved inversion, reservoir characterization, and exploration

2013 ◽  
Vol 1 (2) ◽  
pp. T167-T176 ◽  
Author(s):  
Brian P. Wallick ◽  
Luis Giroldi
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Gas Field ◽  
Frequency Model ◽  
Low Frequencies ◽  
Limited Bandwidth

Interpretation of conventional land seismic data over a Permian-age gas field in Eastern Saudi Arabia has proven difficult over time due to low signal-to-noise ratio and limited bandwidth in the seismic volume. In an effort to improve the signal and broaden the bandwidth, newly acquired seismic data over this field have employed point receiver technology, dense wavefield sampling, a full azimuth geometry, and a specially designed sweep with useful frequencies as low as three hertz. The resulting data display enhanced reflection continuity and improved resolution. With the extension of low frequencies and improved interpretability, acoustic impedance inversion results are more robust and allow greater flexibility in reservoir characterization and prediction. In addition, because inversion to acoustic impedance is no longer completely tied to a wells-only low-frequency model, there are positive implications for exploration.

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