Seismic Inversion Model Research Articles

Crustal thickness variations beneath Egypt through gravity inversion and forward modeling: linking surface thermal anomalies and Moho topography

This study aims to quantify the topography of the Moho boundary, the lower crust and uppermost mantle contact of Egypt, in order to estimate the crustal thickness variation and its link to the distribution of thermal anomalies under Egypt. This is accomplished by modeling satellite gravity, supported by the passive seismic constraints throughout Egypt. However, when estimating the thickness of the crust in Egypt using just seismic data, substantial uncertainty and deviation are produced due to the sparsely dispersed stations. Integrating seismic and gravity data minimizes uncertainty and improves estimate accuracy. The investigation is broken down into four stages, the first involving utilizing the Sentinel-3B satellite to create land surface temperature maps. The subsequent steps consist of gravity and seismic data adjustments, inversion and forward modeling. We used seismically restricted nonlinear inversion to look at Goco06s satellite gravity data to model the Moho’s topographic surface. The data gathered from deep seismic refraction and receiver functions adjusted the analyzed data. The inversion process relies on the adapted Bott's approach and Tikhonov regularization, using the assumption of the sphericity of the Earth planet. Reference values for depth of Moho and density contrast were set at 35 km and 500 kg/m3, respectively. The average statistical difference for Moho depth between gravity-based model and seismic data is − 0.10 km. Through forward gravity modeling, five gravity profiles were chosen and interpreted in 2.5D models. The results indicated that the Moho depth in the south varies from 35 to 39 km and decreases in the north and the Mediterranean. In upper Egypt, the highest Moho depth is 39 km. The depth varies beneath the Sinai Peninsula as it is about 35 km in its south, reaches 30 km in the northern portion, and ranges along the Red Sea’s Rift Margin from 29 to 32 km. Moreover, the final model shows the relation between Moho coincides with the surface temperature anomalies approved by satellite images and hot springs. The model reveals a correlation between Moho discontinuity and surface temperature anomalies, revealing the highest geothermal potential in a rectangular area in central Egypt, between latitudes 25°N and 30°N, based on satellite imagery and hot springs distribution.

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%.

Building initial model for seismic inversion based on semi‐supervised learning

AbstractSeismic inversion is an important tool for reservoir characterization. The inversion results are significantly impacted by a reliable initial model. Conventional well interpolation methods are not able to meet the needs of seismic inversion for lateral heterogeneous reservoirs. Inspired by the sequence modelling network and seismic inversion in the Laplace–Fourier domain, we propose an initial model‐building method using semi‐supervised learning strategy. The proposed method considers spatial information to ensure the horizontal continuity of the initial model. Based on the fact that the low‐frequency components of seismic signals in the Laplace–Fourier domain are easier to obtain, we use the forward model in the Laplace–Fourier domain to replace the time‐domain forward model. The proposed workflow was validated using the Marmousi II model. Although the training was carried out on a small number of low‐frequency impedance traces, the proposed workflow was able to build low‐frequency model for the entire Marmousi II model with a correlation of 98%. Field data examples demonstrate the feasibility and effectiveness of the proposed method. For lateral heterogeneous reservoirs, the proposed method performs better than the well interpolation method. By utilizing the model obtained by the proposed method as the initial low‐frequency model of the conventional inversion method, it is possible to estimate better inversion results. The results of different combinations of training sets demonstrate the stability of the proposed method. This method may still be a viable choice if there is lateral heterogeneity underground but not much well‐logging label data.

Evolution Inversion: Co-Evolution of Model and Data for Seismic Reservoir Parameters Inversion

Evolution Inversion: Co-Evolution of Model and Data for Seismic Reservoir Parameters Inversion

ПРИМЕНЕНИЕ МЕТОДА СЕЙСМИЧЕСКОЙ ИНВЕРСИИ ДЛЯ ОЦЕНКИ ФИЗИЧЕСКИХ ПАРАМЕТРОВ НЕФТЕГАЗОНОСНЫХ ПЛАСТОВ

The article is devoted to a modern seismic inversion model. The spatial distribution of impedance is simulated using a synthetic well tie. A low-frequency background model is built as well as an acoustic impedance cube. Density and sonic log data are balanced. The study conducted both an analysis of petroelastic properties and a dynamic analysis. Guidelines for drilling locations are given. The method used takes into account the geological data based on a structural model for seismic horizons.

3D Integrated Model-Driven Work Flow Developed for Shale Hydraulic Fracturing

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 212406, “3D Integrated G&G Model-Driven Mitigation Work Flow on Screenout, Frac Hits, and Casing Deformation in Ultradeep Shale Hydraulic Fracturing,” by Jianfa Wu and Bo Zeng, PetroChina, and Lipeng Wang, SLB, et al. The paper has not been peer reviewed. _ The Longmaxi shale gas play in China is unique because of multiple tectonic deformations in its geological history. While its hydraulic fracturing design has matured after a decade-long evolution, the success of every well cannot be ensured without considering heterogeneity. To address these challenges, a multidisciplinary team was formed to work on a pad; through the team’s efforts, designed proppant volume achieved zero casing deformations, fewer screenouts, and weaker fracture hits. The key to effective mitigation was continuous 3D geological and geomechanical (G&G) modeling through ongoing iterations with field data. Background The Longmaxi shale play in the Sichuan Basin is approximately 20 000 km2 and is buried between 2000 and 4500 m. This play has more than 10 trillion m3 of gas reserves and is made up of shallow and deep reservoirs. Currently, the operator has moved to the deeper Luzhou and Chongqing blocks buried at a depth greater than 3500 m. Nearly 200 horizontal wells per year are planned in the next 5 years. A fracture hit with tens of MPa increase was observed. Four casing failures were observed on 32 of the wells in this block, which made the unstimulated length more than 200 m and cost an extra $300,000 for interventions in each problematic well. While the risk of screenout, fracture hit, and casing deformation is common, risk levels vary for different reservoirs. No work flow is available that could be developed to identify and reduce these hazards. In this study, ultradeep shale gas at Pad X, buried almost 3900 m in the Luzhou Block, was selected to recognize and mitigate these risks (Fig. 1), where four wells were positioned 300 m apart. The lateral length of X-1 is roughly 1000 m, while the rest of the wells are roughly 1900 m in length. 3D G&G Modeling By combining seismic interpretation, seismic inversion, petrophysical analysis, and 1D geomechanical modeling while adhering to the Longmaxi-Wufeng Shale modeling technique used in neighboring blocks, the authors established a G&G model with dimensions of 40×40×0.5 m for the Luzhou Block. The pad model was refined to 8×8×0.5 m by using well logs to better characterize the variations in stage level. Natural Fractures. 3D surface seismic data was the only reliable source available to predict natural fractures in the shale and design the fracturing job, though the uncertainty was unavoidable. To display fault-/fracture-system characteristics, seismic attributes, including variance, ant tracking, and curvature were examined.

The use of machine learning toward an accurate initial model for seismic inversion

Seismic inversion is used routinely in hydrocarbon exploration and development as an effective geophysical tool. Inversion methodologies vary from relative to absolute poststack, prestack, and stochastic seismic inversion. Most of the inversion algorithms suffer from the “nonuniqueness” problem, which means there is more than one possible geologic model that fits the seismic data. Because of this, other information must be provided in the form of a low-frequency initial model. Construction of an accurate initial model requires honoring the subsurface geology of the field, adequate well control, and accurately picked horizons. The lack of all or some of these items makes it hard to build a reliable initial model, and the subsequent inversion will be subject to cumulative errors and accordingly questionable results. To mitigate these limitations, we implement the multiple linear regression algorithm to blend selected seismic attributes with seismic velocities to build a robust 3D initial model. First, we analyze the elastic properties of a given well against the seismic internal attributes and the available processing velocities. Next, we build a set of multilinear equations to convert the chosen attributes into 3D elastic properties. Finally, we smooth the results and produce the elastic initial models. No horizons are needed in this process. To prove its validity, the proposed approach was applied in the offshore Nile Delta, Egypt. The new initial models for the case studies show better consistency with the geology of the area and contain very fine details compared with the conventional initial models. Hence, the proposed approach suits exploration of deep targets as it provides a reliable link between seismic internal attributes, velocities, and elastic logs for the construction of an accurate 3D initial model even though the data may be insufficient.

4D Seismic Inversion and Rock Physic Modeling to Monitor CO2 Injection at Carbon Capture and Storage Project in The Utsira Formation, Sleipner Field, North Sea, Norway

Carbon Capture and Storage (CCS) is used at Sleipner Field due to the implementation of a carbon emission tax off the coast of Norway. This project causes the fluid at the Utsira Formation as a reservoir to be replaced by CO2, so the elastic property of the reservoir rock will change. Because of that, the 3D seismic survey was carried out in 1994 (baseline) and re-acquisition in 2001 (monitor) to observe CO2 distribution and changes in rock properties. This study aims to monitor the distribution of CO2 as well as changes in reservoir rock's acoustic and elastic parameters. This research performed the cross-equalization, 4D Seismic Inversion model-based, and rock physics modeling process. From data processing, obtained information that CO2 spreads laterally, then moves to the northeast and does not penetrate the overburden. Also, we get the NRMS value of 0.443068 and the cross-correlation value of 0.907426. 4D Inversion results reveal a change in the reflector at the reservoir zone, as indicated by the velocity pushdown caused for a decrease in seismic velocity owing to CO2. In addition, rock physics modeling provides that changes occur in bulk modulus, Vp, Vs, density, and AI. From the process, there are differences in AI values where the Inversion results show a decrease in AI values of 2.9%, while rock physics modeling shows a 12% reduction.

Structurally Constrained Initial Impedance Modeling for Poststack Seismic Inversion

The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Bayesian Physics-Informed Neural Networks for the Subsurface Tomography Based on the Eikonal Equation

The high cost of acquiring a sufficient amount of seismic data for training has limited the use of machine learning in seismic tomography. In addition, the inversion uncertainty due to the noisy data and data scarcity is less discussed in conventional seismic tomography literature. To mitigate the uncertainty effects and quantify their impacts in the prediction, the so-called Bayesian Physics-Informed Neural Networks (BPINNs) based on the eikonal equation are adopted to infer the velocity field and reconstruct the travel-time field. In BPINNs, two inference algorithms including Stein Variational Gradient Descent (SVGD) and Gaussian variational inference (VI) are investigated for the inference task. The numerical results of several benchmark problems demonstrate that the velocity field can be estimated accurately and the travel-time can be well approximated with reasonable uncertainty estimates by BPINNs. This suggests that the inferred velocity model provided by BPINNs may serve as a valid initial model for seismic inversion and migration.

Magnetotelluric image of a hyper-extended and serpentinized rift system

Magnetotelluric (MT) data can image the Earth's electrical resistivity down to the mantle but are rarely used for investigation of offshore rifted margins. In such settings, the lower crust and upper mantle are altered by distinct tectono-thermal processes but often display similar seismic velocities and densities. By integrating resistivity models from MT data, we aim at resolving such ambiguity. Here, 3D inversion of long period, marine MT data (1 – 3000 s) is performed on 104 receivers located along two, ∼300 km long transects in the SW Barents Sea. The resolving power of MT data is assessed with synthetic tests in an archetypal rift system where ample crustal thickness variation occurs. The results highlight that our MT data sense the transition from necking to hyper-extended domain where the crust (<10 km) is not recovered by 3D inversion. In the Bjørnøya Basin – the northernmost member of a hyper-extended Cretaceous basin chain in the NE Atlantic – we combine seismic interpretation and MT inversion models to assign resistivity properties at two depth intervals: (1) 0.1-1 Ωm within Lower Cretaceous marine shales buried at 10-15 km depth (2) 1-10 Ωm within the uppermost mantle. Based on a fluid-rock model, we emphasize that seawater as a sole pore fluid phase is not conductive enough to explain such high bulk conductivities at both intervals. A 25% serpentinization of mantle rocks can account for a fivefold rise in salinity of the residual fluid and is compatible with bulk resistivity, density, and seismic velocities in the Bjørnøya Basin. Such high-salinity fluid can ascend and mix with seawater in pore spaces of the sediments, supporting our model of saline fluid circulation in hyper-extended basins. In conclusion, electrical resistivity models can disambiguate interpretation of deep structures in rifted margin by detecting saline fluids from partial serpentinization, intermixing with seawater in overlying marine sediments.

Envelope-based constrained model for multiparameter pre-stack seismic inversion

Pre-stack seismic inversion is an effective method to estimate multiparameter from seismic reflection data in the oil–gas reservoir exploration. In order to get a reliable inverted parameter result, a constrained model is commonly used to reduce the multi-solution in the seismic inversion. However, it is hard to get an appropriate constrained model by conventional well-log data interpolation or velocity field in offshore oil–gas exploration with sparse wells. Envelope inversion has been successfully applied to get a good initial model with low-frequency components in full-waveform inversion. In order to fully exploit the rich information contained in seismic data, we introduce envelope inversion into background modeling based on convolution theory and propose a multiparameter pre-stack seismic inversion with envelope prior constraint. Both inversion results of the model and field data indicate the effectiveness and adaptability of the proposed method in multiparameter estimation.

3D Geomechanical Model Construction for Wellbore Stability Analysis in Algerian Southeastern Petroleum Field

The main objective of this research work was the wellbore stability evaluation of oil and gas wells based on a 3D geomechanical model, which as constructed using seismic inversion in a southeastern Algerian petroleum field. The seismic inversion model was obtained by using an iterative method and Aki and Richards approximation. Since the correlation between the inversion model and the log data was high at the wells, the reservoir was efficiently characterized and its lithology carefully discriminated in order to build a reliable 3D geomechanical model. The latter was further used to suggest the drilling mud weight window for the ongoing wells (well 5) and to examine the stability of four previously drilled wells. The main contribution of this study is providing a 3D geomechanical model that allows the optimization of drilling mud weight parameters so that a wellbore’s stability is guaranteed, on the one hand, and, on the other hand, so that the reservoir damage brought about by excessive surfactant use can be prevented. Indeed, the mud parameters are not just important for the drilling process’s effectiveness but also for logging operations. Since the tools have limited investigation diameters, with excessive use of surfactant, the invaded zone can become larger than the tools’ investigation diameter, which makes their logs unreliable. Hence, the 3D geomechanical model presented here is highly recommendable for the proposition of new wells, entailing less exploration uncertainty and more controllable productivity.

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

Digital rock physics (DRP) has become an important tool to analyze the characteristics of pore structures and minerals and reveal the relationships between microscopic structures and the physical properties of reservoirs. However, it is greatly difficult to upscale the rock physical parameters, such as P-wave velocity, S-wave velocity, and elastic moduli, from DRP to large-scale boreholes and reservoirs. On the other hand, theoretical rock physical modeling can establish the internal relationship between the elastic properties and physical parameters of tight sandstones, which provides a theoretical basis for seismic inversion and seismic forward modeling. Therefore, the combination of digital rock physics and rock physical modeling can guide the identification and evaluation of the gas reservoir’s “sweet spot.” In this study, the CT images are used to analyze the mineral and pore characteristics. After that, the V-R-H model is used to calculate the equivalent elastic moduli of rocks containing only the mineral matrix, and then, the differential equivalent medium (DEM) model is used to obtain the elastic moduli of dry rocks containing minerals and pores. Subsequently, the homogeneous saturation model is used to fill the fluids in the pores and the Gassmann equation is used to calculate the equivalent elastic moduli of the saturated rock of tight sandstones. Rock physical modeling is calibrated, and the reliability of the rock physical model is verified by comparing those with the logging data. Afterward, the empirical relationship of rock porosity established from CT images and rock elastic moduli is obtained, and then, the elastic parameters obtained by seismic data inversion are converted into porosity parameters by using this empirical relationship. Finally, the porosity prediction of large-scale reservoirs in the study area is realized to figure out the distribution of gas reservoirs with high porosity. The results show that the H3b and H3c sections of the study area exhibit higher porosity than H3a. For the H3b reservoir, the northeast and middle areas of the gas field are potential targets since their porosity is larger than that of others, from 10% to 20%. Because of the effects of the provenance from the east direction, the southeast region of the H3c reservoir exhibits higher porosity than others.

A hybrid physics and machine learning approach for velocity prediction

Elastic well logs play an important role in reservoir characterization in the subsurface. However, due to the high expense of drilling, only a few wells are drilled to limited depths, making it difficult to understand the deposition and execute geophysical activities such as building background models for seismic inversion and property models for seismic forward modeling. We begin with P-velocity prediction between the wells and extension along the wellbore to tackle this problem. A hybrid workflow is introduced based on seismic and available P-velocity logs, including well-log decomposition, relative rock physics, seismic forward modeling, feature engineering based on seismic transform, optimal attribute selection, and machine learning network training and prediction. In this hybrid workflow, relative P-velocity instead of absolute P-velocity is used for labeling. The rock-physics study and seismic forward modeling contribute to label augmentation. The machine learning approach assists in discovering the relationship between the relative P-velocity and the optimal seismic attributes. Under physics rules, the predicted relative velocity through the trained network is integrated with the compaction trend to estimate the final absolute velocity. This hybrid workflow is applied to a case study of sand-shale sequences in northern China. The model-based deterministic inversion and data-driven machine learning approaches are also compared. The results of blind well testing indicate that the data-driven approach lacks generalization capability and fails to predict extension in some blind wells. The physics-based inversion performs differently in blind wells in different locations. By contrast, P-velocity prediction with the hybrid workflow improves prediction accuracy in all blind wells, horizontally and vertically. The results indicate that this hybrid workflow promises interpolating and extending elastic well logs when the deposition environment does not vary significantly. Further studies are recommended to discuss the applicability of this workflow.

Wave-equation migration velocity analysis via the optimal-transport-based objective function

Wave-equation migration velocity analysis builds a kinematically accurate macrovelocity model containing the large-scale structures of the model for seismic imaging or full-waveform inversion (FWI). Differential semblance optimization formulates the misfit function in the subsurface domain by applying a penalty to the unfocused subsurface-offset gathers or the nonflattened angle gathers. Such a penalty leads to gradients with strong artifacts and spurious oscillations, which then leads to slow convergence. We formulate the misfit function using the optimal transport (OT) among neighboring traces in angle-domain common-image gathers (CIGs). Specifically, we measure the unflatness of the gathers by comparing the Wasserstein distance of adjacent traces. Because CIGs are ideal to form a probability distribution, which is required by OT, it is natural to use OT to measure the time shifts between two distributions. Our objective function exhibits well-behaved convex and unimodal properties in a simple model with a single interface, and it is expected to be minimum for the correct velocity when the angle gathers are flat. A synthetic data set example on the Marmousi model indicates the ability of our method to provide an initial model for FWI that allows FWI to converge. We also apply the approach on a marine field data set to further demonstrate its effectiveness in estimating the macrovelocity model.

Hierarchical reservoir lithofacies and acoustic impedance simulation: Application to an oil field in SW of Iran

In this study, a hierarchical simulation algorithm was implemented to produce 3D models of lithofacies and acoustic impedance (AI) in an oil field in southwestern Iran. Hierarchical simulation is a powerful method that takes into account the spatial distribution and hard contact relationships of continuous variables within the categorical variables in reservoir characterization. To implement the hierarchical simulation algorithm in this study, pluri-Gaussian simulation was used to construct the layouts of one categorical variable (lithofacies domains) and turning bands simulation was used to produce one continuous variable (AI models) within each simulated domain. The dataset that is available to test the proposed method includes the lithofacies and AI logs of seven wells. For validation of the proposed model, one of wells was excluded from the initial dataset to check the accuracy of results. The algorithm was implemented on a gridded model to produce 50 realizations of 3D lithofacies and AI models. The statistical analyses of the produced models verified the capabilities of hierarchical simulation in reproducing the lithofacies domains and AI values related to each domain. The simulated AI models were compared with the seismic inversion model to corroborate the reliability of the results. In addition, to show the superiority of the hierarchical approach, the results were compared to the results of direct AI modeling in which the AI models were generated without considering any lithofacies data. The comparisons in the blind well indicated a high correlation between the modeled values and the true AI. The correlation coefficient between average AI models of the hierarchical approach and the true AI was 0.85. Whereas, this value was 0.75 for the average of directly simulated AI models and 0.74 for the seismic inversion, respectively. Histogram analysis also verified the capability of the hierarchical modeling in reproducing the statistical properties of the original data. The resulting AI model obtained using the method proposed in this study also showed a high compatibility with the deterministic seismic inversion cube.

Inversion of seismic source parameters from satellite InSAR data based on deep learning

Satellite interferometric synthetic aperture radar (InSAR) has been playing an important role in the earthquake surface deformation observation and source model inversions with its advantage of fine spatial resolution in comparison with seismological data. At present, the two-step inversion approach, including a nonlinear inversion step for determining the fault geometry (e.g., length, width, depth, strike, dip, rake, slip) and a linear inversion step for estimating the slip distribution, is widely used to obtain seismic source parameters from satellite InSAR data. However, the nonlinear inversion step has some weaknesses, such as a prior knowledge requirement, local minima solution, complex and time-consuming. Coseismic differential interferogram is input into back propagation neural network (BPNN) to perform real time inversion of fault geometry in the previous study. But due to the simpleness of network structure, rake and slip parameters is fixed and inversion accuracy is limited. In this paper, we propose a deep learning approach of Earthquake Source Parameters Inversion using ResNet (abbreviated as ESPI-ResNet) from satellite InSAR data. ESPI-ResNet is able to firstly classify the 4 fault types and then invert the 7 fault geometry parameters based on uniform slip model. We train our model by firstly creating a large dataset of simulated interferograms with source parameters ranges confined by seismological and geological data. The accuracy of fault type classification is 99.6% and root mean squared error (RMSE) of inversed fault geometry parameters is low in the simulated test dataset. To find most suitable model for seismic source parameters inversion, we further compare the inversed accuracy of different networks, including BPNN, VGG-16, ResNet-18, ResNet-34, ResNet-50 and DenseNet. We find that moderate increase of network depth and the use of convolution, deep residual learning can improve the model's performance for source parameters inversion, and therefore ResNet-34 is chosen as the backbone network in this study. Finally, real differential interferograms of Yutian earthquake (2020 Mw 6.3), Jiuzhaigou earthquake (2017 Mw 6.5) and Menyuan earthquake (2016 Mw 5.9) are used to validate the proposed method. Real earthquakes validation shows all of them have correct fault type classification, and the inverted results are consistent with the current seismic source parameters data.

Data-driven prestack-waveform inversion using genetic algorithm: Methodology and examples

The choice of an initial model for seismic waveform inversion is important. In matured exploration areas with adequate well control, we can generate a suitable initial model using well information. However, in new areas where well control is sparse or unavailable, such an initial model is compromised and/or biased by the regions with more well controls. Even in matured exploration areas, if we use time-lapse seismic data to predict dynamic reservoir properties, an initial model that we obtain from the existing preproduction wells could be incorrect. In this work, we outline a new methodology and workflow for a nonlinear prestack isotropic elastic waveform inversion. We call this method a data-driven inversion, meaning that we derive the initial model entirely from the seismic data without using any well information. By assuming a locally horizontal stratification for every common midpoint and starting from the interval P-wave velocity, estimated entirely from seismic data, our method generates pseudowells by running a two-pass 1D isotropic elastic prestack-waveform inversion that uses the reflectivity method for forward modeling and the genetic algorithm for optimization. We then use the estimated pseudowells to build the initial model for seismic inversion. By applying this methodology to real seismic data from two different geologic settings, we determine the usefulness of our method. We believe that our new method is potentially applicable for subsurface characterization in areas where well information is sparse or unavailable. Additional research is, however, necessary to improve the computational efficiency of the methodology.

An Investigation of Seismic Velocity Variation through a Tectonic Boundaries-Case Study in Central Iraq

A 3D velocity model was created by using stacking velocity of 9 seismic lines and average velocity of 6 wells drilled in Iraq. The model was achieved by creating a time model to 25 surfaces with an interval time between each two successive surfaces of about 100 msec. The summation time of all surfaces reached about 2400 msec, that was adopted according to West Kifl-1 well, which penetrated to a depth of 6000 m, representing the deepest well in the study area. The seismic lines and well data were converted to build a 3D cube time model and the velocity was spread on the model. The seismic inversion modeling of the elastic properties of the horizon and well data was applied to achieve a corrected velocity cube. Then, the velocity cube was converted to a time model and, finally, a corrected 3D depth model was obtained. This model shows that the western side of the study area, which is a part of the stable shelf, is characterized by relatively low thickness and high velocity layers. While the eastern side of the study area, which is a part of the Mesopotamian, is characterized by high thickness and low velocity of the Cretaceous succession. The Abu Jir fault is considered as a boundary between the stable and unstable shelves in Iraq, situated at the extreme west part of the study area. The area of relatively high velocity gradient is considered as the limit of the western side of the Mesopotamian basin. This area extends from Najaf-Karbala axis in the west to the Euphrates River in the east. It is found that the 3D stacking velocity model can be used to obtain good results concerning the tectonic boundary.