Model-based Seismic Inversion Research Articles

Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

Understanding the distribution of shear wave velocity (VS) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available VS data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the VS data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of VS in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of VS in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of VS throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated VS. Also, the calculated VS section has a high correlation with the actual logs at the location of the wells.

Stochastic Inversion in Determining the Distribution of Petroleum Carrying Sandstones in the "JS" Field of the South Sumatra Basin

The "JS" field is a field located in the South Sumatra Basin, where the field has good prospects for the distribution of petroleum-bearing sandstone. The target of this research is the Air Benakat formation. This research uses the stochastic seismic inversion method to determine the probability of finding petroleum in sandstone. Stochastic seismic inversion has the advantage that it can overcome thin layers and can reduce existing data misalignments. So stochastic inversion can overcome the shortcomings of other seismic inversions, especially model-based seismic inversion which is the initial model for stochastic seismic inversion. Stochastic seismic inversion produces several realizations by showing uncertainty so as to get results that are close to the actual situation. Probability map of oil-bearing sandstones located in the north and east of the study area. with the slice results obtained for the acoustic impedance range of 8517-9051(m/s)*(g/cc) and oil sand probability with a value range of 0.61-0.78%.

Application of Post Stack Acoustic Impedance Inversion to Lateral Rock Property Prediction: A Case Study Eti-field Offshore Niger Delta

This study presents the result of a Model-based seismic inversion technique which was used to invert an acoustic impedance structure within a reservoir interval by intergrating well logs and 3D post stack seismic data obtained from Eti-field offshore Niger Delta. The purpose was to delineate lateral and vertical alternations in subsurface rock properties which is caused by difference in lithofacies within the reservoir interval. This would help to define hydrocarbon fairways better and constrain the range of hydrocarbon zones for field development. The inversion workflow used in this study includes forward modelling of reflection coefficients from a low frequency impedance model driven from well logs and convolution of the reflection coeffiecients with a source wavelet derived from the seismic data. P-impedance inversion analysis at the control well location gave a near perfect correlation of 0.993686 (correlation coefficient of ≈ 99.4 %) between the original P-impedance log, initial guess P-impedance model log and inverted P-impedance log. The estimated error observed was 5533.1 which corresponds to (0.112308) about 11.23 %. Seismic inversion analysis realized an acoustic impedance striucture with P-impedance values ranging from 7000 to about 50000 ft/s*g/cc and having a general increase with depth trend. Impedance slice extracted from the impedance volume at top of the reservoir predicted lateral variations in P-impedance at well control and away from well control. This information can be invaluable in delineating more prospective reservoir zones in the field and thereby enhancing optimum field development which aids in reservoir management decisions.

Improved lithospheric seismic velocity and density model of the Korean Peninsula from ambient seismic noise data using machine learning

To accurately estimate physical properties and tectonic behavior in the near-surface, a reliable seismic property model is needed for realistic seismic modeling and earthquake location estimation. Recording ambient seismic noise (ASN) data and producing interferometric reflection images traditionally provides subsurface structure observation without an active source. However, due to low signal-to-noise ratio (SNR) and vertical resolution, interpreting upper mantle structures and inverting seismic models from noise data is difficult. To address this, machine learning (ML) techniques are applied to enhance vertical resolution and interpret geologically meaningful boundaries. Spectral enhancement with convolutional U-net generates higher resolution data by preserving temporal continuity. Unsupervised ML interprets lithospheric boundaries more robustly and objectively than manual horizon picking, and model-based seismic inversion integrates improved seismic data with prior full-waveform inversion (FWI) models. ML-based results improve inverted models, displaying more detailed geological structures and seismic property changes, surpassing seismic data limitations.

Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field, Scotian Basin

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field, Scotian Basin. An integrated study of instantaneous frequency, P-impedance, volume of clay and neutron-porosity attributes, and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area. Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region. P-impedance was estimated using model-based seismic inversion. Petrophysical properties such as the neutron porosity (NPHI) and volume of clay (VCL) were estimated using the multilayer perceptron neural network with high accuracy. Comparatively, a combination of low instantaneous frequency (15–30 Hz), moderate to high impedance (7000–9500 gm/cc∗m/s), low neutron porosity (27%–40%) and low volume of clay (40%–60%), suggests fair-to-good sandstone development in the Dawson Canyon Formation. After calibration with the well-log data, it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons. The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition (Dawson Canyon Formation) in the Penobscot field, Scotian Basin. Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs. The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

Analysis of seismic inversion after depth migration for recovery of the low-frequency component of the model

Seismic inversion is used in practice as a tool for predicting reservoir properties. It allows one to extract a model with a high level of detail from seismic data, i.e. high-frequency component of the model. In this case, the input data are the time processing results, and the issues related to the low-frequency component of the model are not considered usually. In the presented work, a model-based seismic inversion algorithm is implemented. The input data for the inversion are the depth image results in true amplitudes and the depth migration velocity model. The possibilities of seismic inversion are numerically investigated to refine the low-frequency component of the model. Experiments were carried out using synthetic seismic data got for realistic Sigsbee model.

Mapping of Reservoir Properties using Model-based Seismic Inversion and Neural Network Architecture in Raniganj Basin, India

Abstract Reservoir characterization is necessary to compute reservoir parameters for hydrocarbon potential and production optimization. The limitation of robust data and the presence of cultural noise is a constraint for reservoir characterization in the Raniganj basin located in India. Based on available well logs and two-dimensional post-stack seismic data, a model-based seismic inversion is executed to generate acoustic impedance by converting acoustic reflectivity into rock elastic parameters. Moreover, the seismic attributes obtained from the inversion are implemented in neural network architectures to map shale volume, Young’s modulus, and Poisson’s ratio. Error analysis between predicted and actual results demonstrate multi-layered feed-forward or probabilistic neural network display a better result in obtaining reservoir parameters. The mapped reservoir section shows the acoustic impedance varying from 5000 to 16,000 (g/cc)*(m/s), shale volume ranging from 15% to 55%, Young’s modulus, and Poisson’s ratio vary from 0.5-9.5 GPa and 0.23-0.27 respectively. Cross-plot between Young’s modulus versus Poisson’s ratio classifies lithology from brittleness and it increases with depth. Neural network architectures help to identify the best model in delineating shale barriers for designing hydraulic fracturing treatments. Results from this study have added significant values in engineering application and will help in ongoing coalbed methane exploration and future geomechanical studies. However, limitations exist in resolving thin coal seams as the seismic resolution depends on the wavelength, velocity, and frequency of waves in the formation.

Seismic inversion of depth migration results

Seismic inversion is used in practice as a tool for predicting reservoir properties. It allows one to extract a model with a high level of detail from seismic data, i.e., high-frequency component of the model. In this case, the input data are the time processing results, and the issues related to the low-frequency component of the model are not considered usually. In the presented work, a model-based seismic inversion algorithm is implemented. The input data for the inversion are the depth image results in true amplitudes and the depth migration velocity model. The possibilities of seismic inversion are numerically investigated to refine the low-frequency component of the model. Experiments were carried out using synthetic seismic data got for realistic Sigsbee model.

Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India

AbstractSeismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation.

Facies probability application for gas reservoir characterization

Reservoir characterization through seismic inversion is the principle method in hydrocarbon exploration. Facies probability is an advance method of reservoir characterization which integrates model-based seismic inversion and facies classification to construct a better reservoir modelling. The reservoir modelling is produced from four main steps. First is crossplot analysis to define the relation between acoustic parameter and petrophysics parameter. Second, model-based seismic inversion to produce acoustic impedance volume from seismic data. Third, facies classification which is defined from porosity effective, shale volume and water saturation. Final step is Bayesian Inference Framework to model probability density function (pdf) of each facies. The result of crossplot analysis shows that there is a linear relationship between acoustic impedance and porosity hence the porosity volume can be derived from the model-based inversion result. Facies classification is divided into two categorical zone, pay and non-pay. Pay zone is categorized as a high porosity layer filled with gas. Its parameters fit with Vshale > 0.6, Sw < 0.7 and porosity effective > 0.22. Then estimation is run using Bayesian probability into acoustic impedance volume and porosity volume resulting probability volume of each facies.

Application of model based post-stack inversion in the characterization of reservoir sands containing porous, tight and mixed facies: A case study from the Central Indus Basin, Pakistan

Porosity is a key parameter for reservoir evaluation. Inferring the porosity from seismic data is often challenging and prone to uncertainties due to number of factors. The main aim of this paper is to show the applicability of seismic inversion on old vintage seismic data to map spatial porosity at reservoir level. 3D-seismic and wireline log data are used to map the reservoir properties of the Lower Goru productive sands in the Gambat Latif block, Central Indus Basin, Pakistan. The Lower Goru formation was interpreted with the help of seismic and well data. Interpreted horizons are thus further used in model-based seismic inversion techniques to map the spatial distribution of porosity. Well-log data are used in the construction of low acoustic impedance models. Calibration of reservoir porosity with inverted acoustic impedance is achieved through well-log data. The results from model-based inversion reasonably estimate the porosity distribution within the C-sand interval of the Lower Goru Member. After post-stack inversion, the porosity values at wells Tajjal-01, Tajjal-02 and Tajjal-03 are 10%, 8% and 12%, respectively. Porosity values calculated from post-stack inversion at the corresponding well locations are in good agreement with the borehole-derived porosity.

Porosity prediction from model-based seismic inversion by using probabilistic neural network (PNN) in Mehar Block, Pakistan

Porosity prediction from model-based seismic inversion by using probabilistic neural network (PNN) in Mehar Block, Pakistan

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

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

Model Based Inversion of Acoustic Impedance from Seismic Trace for Lithofacies Differentiation: an Application in Xy Field Offshore Niger Delta

This study presents the result of a Model-based seismic inversion technique which was used to invert an acoustic impedance structure within a reservoir interval by intergrating well logs and 3D post stack seismic data obtained from XY field offshore Niger Delta. The purpose was to delineate lateral and vertical alternations in subsurface rock properties which is caused by difference in lithofacies within the reservoir interval. This would help to define hydrocarbon fairways better and constrain the range of hydrocarbon zones for field development. The inversion workflow used in this study includes forward modelling of reflection coefficients from a low frequency impedance model driven from well logs and convolution of the reflection coeffiecients with a source wavelet derived from the seismic data. Acoustic impedance cross section obtained from the inversioin algorithim showed impedance values increasing from 4112 to 7539 (m/sec*g/cm 3 ) from top to bottom of the reservoir with gas filled sand facies observed at the top of the reservoir within time window 1900-2100msec. Below time window 2100msec, there is variation in impedance values observed within the anticlinal structures seen at this interval which suggests porous sand facies containing little shale intercalations. This is characteristic of sandstone reservoirs within the Agbada formation in the Niger Delta. These sands were most likely deposited through distributaries channel deposits, distributaries mouth bars, barrier bars, alluvial fans and crevasse which characterize the reservoir rocks (sandstones) in the Niger Delta. At time window 2100-2200msec, anticlinal structures containing porous sand facies with little shale intercalations was observed again. At time window 2200msec, water bearing sand facies (clean sandstone) was observed and at the bottom of the reservoir within time window 2300- 2500msec, the impedance was dominantly high which suggests the presence of shale facies at the bottom of the reservoir. Gas-oil contact (GOC) was observed between time window 2100-2200msec of the acoustic impedance section. These variations in acoustic impedance amplitude is due to lateral changes in lithofacies within the reservoir. The results obtained gave enhanced structural disposition of the reservoir and are important for accurate stratigraphic imaging interpretation to lower the risk in drilling of exploratory and development wells. Keywords : Acoustic impedance, Seismic inversion, Model-based, Lithofacies

Characterization of Gas Hydrates Reservoirs in Krishna-Godavari Basin, Eastern Indian Margin

ABSTRACT Seismic inversion and attribute analysis techniques are frequently used in oil and gas industries to characterize a hydrocarbon reservoir. The gas hydrate reservoir in the Krishna-Godavari basin, eastern Indian margin, by analysis of high-resolution multi-channel seismic (MCS) data has been characterised. The model-based seismic inversion was performed to derive acoustic impedance (3220 m/s*g/cm3) followed by computation of seismic attributes for delineating the bottom simulating reflector or BSR, main marker for gas hydrates, and characterizing the hydrate-bearing sediments. The study shows the characteristics of BSR at a depth of ~125 m below sea floor (mbsf), and hence the presence of gas hydrates. The high velocity, low amplitude, high instantaneous frequency and low sweetness indicate presence of gas hydrates above the BSR, whereas low velocity, high reflection strength, low frequencies and high sweetness imply free gas underlying the BSR.

An approach for three-dimensional quantitative carbonate reservoir characterization in the Pampo field, Campos Basin, offshore Brazil

In carbonate rock reservoirs, spatial distribution models and elastic properties are complex because of diagenetic processes and mineralogical composition, which together directly interfere with variations in pore shape and interconnectivity. The main objective of this paper is to propose a workflow to aid in three-dimensional quantitative carbonate reservoir characterization of the Quissama Formation (Macae Group) in the Pampo field of the Campos Basin, offshore Brazil. Model-based seismic inversion, sequential Gaussian simulation with cokriging for porosity modeling, and truncated Gaussian simulation with trend for facies modeling were used to characterize the carbonate reservoirs. Our results show that the carbonate platform is located between the upper Aptian and lower Albian seismic surfaces. Interpretation of a new surface, called the intra-Albian, was possible via acoustic-impedance (AI) analysis. Our workflow facilitated identification of low AI, high porosity, and best facies areas in structural highs where the most productive wells have been drilled. Facies modeling suggests that intercalation of facies with high and low porosities is connected to shallowing-upward cycles. Finally, several debris facies with low AI and high porosities were identified in an area that could be targeted for new exploration.

3D architecture of the Aquistore reservoir: Implications for CO2 flow and storage capacity

Quantitative assessment of the Aquistore CO2 storage reservoir has been conducted using a 30 km2 3D seismic volume and a suite of well logs. 3D porosity was calculated using acoustic impedance from model-based seismic inversion and a log-based porosity-impedance relation. The reservoir has a mean thickness of 219 m ±ơ = 3% comprising 51% of pay. Strata dip at ∼1.75% SSE and include a prominent SSE-NNW structural fabric dominated by a ridge that corresponds to a Precambrian basement fault and overlying flexure. Porosity maps for Black Island and Deadwood reservoir zones show mean interval porosities of 0.071 ± ơ = 18% and 0.075 ± ơ = 9%, respectively with a weak degree of directionality that is sub-parallel to the strong NNW-SSE structural trends. Lateral spread of injected CO2 will be strongly affected by the NNW-trending structural relief and bulk porosity/permeability trends. Local topographic channels may control CO2 flow particularly when injection rates are low and local closed topographic structures may constitute traps for CO2. CO2 static capacity estimates from well-based mean values are less than comparable seismic-based estimates by <15% due to porosity differences and <5% due to thickness differences. Variations in the Deadwood thickness of up to 25 m from the mean value would have resulted in capacity estimate differences of up to 25% if an alternate well location had been chosen.

Evaluation of optimal reservoir prospectivity using acoustic-impedance model inversion: A case study of an offshore field, western Niger Delta, Nigeria

The evaluation of economic potential of any hydrocarbon field involves the understanding of the reservoir lithofacies and porosity variations. This in turns contributes immensely towards subsequent reservoir management and field development. In this study, integrated 3D seismic data and well log data were employed to assess the quality and prospectivity of the delineated reservoirs (H1–H5) within the OPO field, western Niger Delta using a model-based seismic inversion technique. The model inversion results revealed four distinct sedimentary packages based on the subsurface acoustic impedance properties and shale contents. Low acoustic impedance model values were associated with the delineated hydrocarbon bearing units, denoting their high porosity and good quality. Application of model-based inverted velocity, density and acoustic impedance properties on the generated time slices of reservoirs also revealed a regional fault and prospects within the field.

Estimation of acoustic-impedance model by using model-based seismic inversion on the Ghar Member of Asmari Formation in an oil field in southwestern Iran

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

Improve impedance inversion by adopting seismic sedimentary-guided a priori model

We have developed an innovative procedure for model-based seismic inversion in areas with sparse or clustered and biased well control, where lithofacies and geobodies cannot be adequately sampled in wells and correctly represented in a priori acoustic impedance (AI) models constructed with conventional kriging methods. We have applied seismic sedimentology for facies mapping before the model building process. Lithology-calibrated seismic stratal slices contain rich information for analyzing sedimentary geomorphology and dispersal patterns of depositional systems, providing independent geologic knowledge to constrain a priori models. The new information can be incorporated by directional kriging that properly addresses facies types, orientations, and facies boundary conditions. Finally, the improved a priori model can be applied in Bayesian inversion for an updated inverted AI volume. This procedure was applied in a 3D project in Saidong Depression, Erlian Basin, China, with promising results, achieving inverted AI maps with a more complete facies representation, a more reasonable sediment dispersal pattern (orientation), and clearer facies boundaries.