Search

Time‐lapse seismic (four‐dimensional seismic) data play a preeminent role in closed‐loop reservoir management by providing a full‐field image of dynamic reservoir behaviour during production. Nonetheless, the multidisciplinary nature of four‐dimensional closed‐loop approaches demands more quantitative and fast methods to integrate rock physics models, reservoir flow simulation models and four‐dimensional seismic analysis. In this work, we tackle this time‐consuming and expensive process and develop a data‐driven quantitative approach to leverage the inherent physics between four‐dimensional seismic and reservoir property changes. We propose an inversion flow method using machine learning strategies to estimate changes in reservoir properties directly from a fast‐derived four‐dimensional seismic attribute. This study was carried out in a Brazilian deep‐water field where production started in 2013 with 3 years of production and injection history. For this reservoir, the estimation of fluid saturation maps is a critical objective to assist engineers with data assimilation procedures. We also generated millions of training data samples using 200 simulation models from the field mentioned above (before history matching) to highlight the benefits of restricting training samples to proper fluid flow consistent combinations. Results demonstrate high prediction accuracy for the targeted reservoir property changes. Additionally, it provides insights for the detection of sweet spots and positioning of infill wells. We significantly simplify the four‐dimensional seismic integration process, allowing initial engagements of reservoir engineers with decision‐making processes and data assimilation applications. [ABSTRACT FROM AUTHOR]

Emry oil field is one of the biggest oil fields in the North Western Desert of Egypt. (Barremian-Hauterivian) Alam El Bueib IIIG is the main targeted sand reservoir interval. A total of 5000 BOPD were produced by the exploratory well Emry Deep 0IX. Despite that, the limit of the field is still not accurately defined, because Matruh Canyon affected the extension of Alam El Bueib IIIG. Therefore, the main uncertainty is to scheme the unconformity surface between the sand of Alam El Bueib IIIG and the infill shale of Matruh Canyon. The seismic data and the well logs data were utilized to distinguish the reservoir extension for Alam El Bueib IIIG. The work was achieved by performing the rock physics study and the post-stack seismic inversion. Rock physics plays an essential role in the feasibility study to discriminate the sand of Alam El Bueib IIIG from the non-reservoir facies (Matruh Shale). Acoustic impedance model has been computed to define the geometry of Matruh Canyon. The results from the current study, the acoustic inverted model helped to differentiate between the reservoir and the non-reservoir, which cannot be detected from the normal seismic. Two-way time map for Base Matruh Canyon was generated with a possible three leads were emphasized. The conclusion reveals that the application of the cross-plots of the rock physics can discriminate between the sand of Alam El Bueib IIIG and Matruh Canyon. The post-stack impedance inversion demonstrated to be a powerful method for detecting the boundary between the sand of Alam El Bueib IIIG and the shale of Matruh Canyon. The present research will be the first of its type for separating Matruh Canyon from the remnant part of Alam El Bueib Formation. [ABSTRACT FROM AUTHOR]

Petrophysical properties are the least explored parameters in the Mahanadi basin in the eastern Indian margin. We have used 2D seismic reflection data along a profile AC with available well logs of the Expedition-01 of the Indian National Gas Hydrates Program (NGHP) for the evaluation of lateral variation of porosity – a petrophysical property. The seismic line passes through the Sites NGHP-01-19B in the AB section, and NGHP-01-09A in the BC section. The porosity is estimated using two different approaches based on acoustic impedance (AI) transformation and direct seismic inversion. The AI transformation approach fails to decipher consistent porosity distributions whereas the direct seismic inversion approach provides satisfactory results along seismic line AC (AB and BC) in the Mahanadi Offshore region. The AI transformed porosity section illustrates erroneous porosity values within the target zone, irrespective of a strong coefficient of determination (R 2 ) of 0.82 and 0.85 between AI and porosity at sites NGHP-01-19B and NGHP-01-9A, respectively. The accuracy of the porosity results obtained from the direct inversion approach depends on the best linear regression fit established between acoustic and porosity reflectivity-based models with a coefficient of determination as 0.91 at Site NGHP-01-19B and 0.94 at Site NGHP-01-9A. The porosity distributions are also corroborated with the density-porosity log values, and it is observed that porosity from direct seismic inversion is consistent with the porosity log information at respective sites. The porosity results at the two sites signify the overall good performance of the direct seismic inversion method and also indicate that the probability of free-gas occurrences in shallow sediments is higher compared to that of gas-hydrates. [ABSTRACT FROM AUTHOR]

This study is a comparative analysis of three types of post-stack inversion techniques, namely, Band-Limited (BLI), Linear Programming Sparse-Spike (LP-SSI), along with Model-Based (MBI), in addition to a neural network in the Sufyan oil field of the Muglad rift basin, Sudan. It investigates whether a combination of multi-attribute analysis and a neural network could bring about a better definition of unresolved thin reservoir layers. Applied inversion techniques produce accurate and reliable results, while the MBI method brings a higher correlation coefficient (0.988) and higher amplitude spectrum correlation (0.999). Hence, it is better for the Sufyan seismic data. The resulting resolution and precision of multi-attribute rock properties prediction is achieved using multi-layer feed forward neural (MLFN), probabilistic neural (PNN), along with radial basis function neural (RBFN) networks to predict porosity. The combination of PNN and LP-SSI has the highest training correlation of 0.9534 and validation correlation of 0.7998. However, MLFN shows that LP-SSI, when used as an external attribute, produces high-resolution images compared to those estimated with other combinations with training correlation of 0.9400 and validation correlation of 0.7111. The resulting resolution and precision in terms of porosities are higher when the combination of LP-SSI and MLFN approach is used compared to that estimated by other methods. Thus, LP-SSI is more accurate than other techniques to predict petrophysical parameters in the Sufyan oil field. [ABSTRACT FROM AUTHOR]

In seismic data analysis, acoustic impedance may be used for predicting the porosity of a sedimentary layer with known lithology, where the influence on impedance from pore fluid is known. This method typically assumes that the stiffness of the rock frame is solely a function of porosity and that there is no variation in stiffness between rocks with the same composition and porosity. However, contact cementation can cause an increase in rock frame stiffness with no significant reduction in porosity. This means that rocks with similar porosities and varying degrees of cementation would have significantly different elastic moduli; thus, attempting to derive porosity from seismic impedance alone would incur errors. The primary goal of this study is to assess how significant this error is for porosity prediction. The secondary goal is to calculate the dry rock frame stiffness across the field and evaluate to what extent the time of hydrocarbon placement affects the lithification of the rock. To fulfill these objectives, we studied petrophysical logs and core data from the Dan field and derived seismic impedance, porosity and stiffness expressed in terms of Biot's coefficient for 14 wells across the field. Trends defined between acoustic impedance, porosity, and Biot's coefficient in the Maastrichtian unit of the Dan field showed that, for a given porosity and water saturation in the oil zone, the chalk located at the crest of the reservoir is softer than the chalk located at the flanks of the field. We also found that chalk is stiffer in the down‐faulted north‐western part of the field for a given porosity and water saturation in the oil zone. In the water zone, no difference was found. We speculate that contact cementation downflank in the northwest block caused an increase in the chalk stiffness without reducing the porosity, while earlier hydrocarbon filling of the south‐eastern block and the crest of the structure delayed contact cementation between the chalk particles. This difference in contact cementation between rocks of the same porosity causes porosity prediction from acoustic impedance to vary by up to six porosity units. [ABSTRACT FROM AUTHOR]

The concept of infinitesimal elastic deformation and the theory of elastic seismic waves was formed in the first part of the 19th century and was based mainly on the Fermat, Huygens and Snell developments in the theory of optics. At the same time, seismic wave propagation (utilized in geophysical prospecting) and optic wave propagation through defined media are based on the same physical-mathematical principles, making it possible to transfer nonconventional procedures developed in the first domain to the second one and back. In this investigation, we propose transferring advanced methodologies established in seismic prospecting to practical optics. We selected two advanced approaches with the following aims: (a) homeomorphic imaging; (b) novel description of boundary conditions. The first approach is established with the utilization of the revealed local theoretical relationship between the geometrical features of two fundamental beams and the geometrical properties of hidden geological targets of the media under study. The employed geometrical characteristics of the fundamental beams are spreading functions and curvatures of the singular wavefronts. The second approach is based on a novel description of the boundary conditions. It enables the determination of a faultless seismic (optical) system with the preassigned focusing and imaging assets when any aberrations are absent. An optimal optical system is usually determined as some arrangement agreeing to some perfect system with acceptable correctness. Employment of the developed procedures in the optical design will permit the application of a description of the optical surface using: (1) parametric functions, (2) differential equations, and (3) mixed (parametric-differential). On this basis, optical systems with a minimal number of optical features with complicated shapes can be promptly computed. Another important application field of the suggested methods is the design of optical systems with diffractive elements. [ABSTRACT FROM AUTHOR]

The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties’ estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics library, which plays a significant role in dataset input for network training, validation, and testing to improve elastic properties in this field. The deep learning network architecture comprising UNET and RESNET-18 with weak supervision networks has proven to be useful to enhance computational work efficiency and prediction accuracy while handling the non-linearity of the data and the non-uniqueness of the solutions. We successfully validated the proposed method on actual field data from a clastic fluvial-dominated field in the Malay basin. Upon comparative analysis with the conventional method, both inversion results are comparable and capable of identifying the reservoir occurrence and distribution. The conventional method exposed the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir. Meanwhile, the proposed method showed more stable, clearer definition and fewer noise inversion results but with a faster turn-around time and a more efficient workflow. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing the proposed method for elastic properties prediction compared to the conventional. The result implies that the proposed method can provide a good elastic properties prediction framework while addressing data limitations and sparsity issues in typical deep learning-based inversions.

Recent years have seen deep learning (DL) architectures being leveraged for learning the nonlinear relationships across the parameters in seismic inversion problems in order to better analyse the subsurface, such as improved velocity model building (VMB). In this study, we focus on deep-learning-based inversion (DLI) for velocity model building, leveraging on a conditional generative adversarial network (PIX2PIX) with ResNet-9 as generator, as well as a comprehensive mathematical methodology for generating samples of multi-stratified heterogeneous velocity models for training the DLI architecture. We demonstrate that the proposed architecture can achieve state-of-the-art performance in reconstructing velocity models using only one seismic shot, thus reducing cost and computational complexity. We also demonstrate that the proposed solution is generalisable across linear multi-layer models, curved or folded structures, structures with salt bodies as well as higher-resolution structures built from geological images through quantitative and qualitative evaluation.

The Kifl field is an important oil producer from Nahr Umar (NU) and Zubair (Zb) formations in the Hilla region, South of Iraq. In the present study, a detailed methodology is proposed to allow mapping important reservoir properties (Φ, Sw, and Vsh) of the producing zones using seismic and well-log data. Log data analysis showed that the calculated porosity of Nahr Umar and Zubair formations is moderate (15–21%) to high (25–30%) and rarely records porosity values less than 10%. Similarly, water saturation falls generally close to an average value of 40–45% and the cutoffs define 1 to 3 pay zones in the studied wells. Seismic attributes and well data are manipulated with a feed-forward artificial neural network (ANN) to predict the distribution of the key pay zones' petrophysical parameters. The results showed that the identified pay zones of Nahr Umar formation in log analysis are depicted accurately with reservoir extension toward the southwest of the study area. This is true especially at the middle and lower parts of the formation where at least two potential pay zones (the lower extends for a larger distance with a thicker interval) are expected. Alternatively in Zubair formation, the results are not as successful as reported in Nahr Umar formation particularly for Sw prediction. This is probably due to the instability of Sw calculations from well-log data, identified by the high standard deviation (0.1804), in the Zb formation and the associating dramatic changes in the calculation of Sw probabilities (P10: 0.2907 versus P90: 0.706). The proposed nonlinear relationship between seismic data and reservoir property in mapping reservoir petrophysics seems promising, but the performance should be tested and verified in other fields. [ABSTRACT FROM AUTHOR]

The current research deals with studying the petrophysical properties represented by the porosity and its distribution on the level of all units of the top and bottom of the Kirkuk Formation Group. The study area is located in Maysan province in the southeastern part of Iraq in the Amara field. The Kirkuk Group was deposited in the Tertiary Age. The post-stack method using seismic inversion and creating a relationship between seismic data was accomplished using Hampson-Russel software at well Am-1 and Seismic lines Ama 20 and 30. The research results indicate high porosity values on top of the formation with a decrease in acoustic impedance (Z) and, therefore, a reduction in the density. At the same time, low porosity values were indicated for the bottom of the formation with an increase in both acoustic impedance and density. The top of the formation contains clastic sediments with oil-sand of high porosity, which is considered hydrocarbon indication and low porosity carbonate materials at the bottom of the formation. [ABSTRACT FROM AUTHOR]

We implement a machine-learning inversion approach to infer petrophysical rock properties from pre-stack data that combines a convolutional neural network and a discrete cosine transform of data and model spaces. This transformation is used for model and data compression. The network learns the inverse mapping between the compressed seismic and the compressed petrophysical domain. A theoretical rockphysics model relates elastic and petrophysical properties, while the exact Zoeppritz equations map the elastic properties onto the seismic domain. Training and validation examples are generated under the assumption of a Gaussian variogram model and a non-parametric prior. A Monte Carlo simulation strategy is employed for uncertainty assessment. We present synthetic inversions on a realistic subsurface model and the outcomes of the proposed approach are compared with those achieved by a standard linearised inversion. The network predictions are assessed in case of errors in the calibrated rock-physic model, in the estimated source wavelet, and in the assumed noise statistics. We also demonstrate that transfer learning avoids retraining the network from scratch when the target and training properties differ. Our experiments confirm that the implemented inversion successfully solves the petrophysical seismic inversion, opening the possibility to get instantaneous predictions of reservoir properties and related uncertainties. [ABSTRACT FROM AUTHOR]

Elastic impedance (EI) inversion for partial angle stack seismic data is a key technology in seismic reservoir prediction within the oil and gas industry. EI inversion provides a consistent framework to invert partial angle stack seismic data, just as the AI inversion does for post-stack data. The commonly used EI inversion process is angle by angle. Hence, the inverted EI for different angles may be nonconforming, especially for the seismic data with a low signal-to-noise ratio. This paper proposes to simultaneously invert multiple partial angle stack seismic data to obtain EI for different angles at once. To obtain conformable EI, we used the joint sparse constraint on the reflection coefficients for different angles. Then, the objective function for simultaneous EI inversion was constructed. Next, synthetic seismic data profiles with three different angles were used to show the superiority of the proposed EI inversion method compared to the conventional method. At last, a real seismic data line was used to test the feasibility of the proposed method in practice. The inversion results of synthetic data and real data showed that it provides an effective new alternative method to estimate EI from partial stack seismic data. [ABSTRACT FROM AUTHOR]

In this paper, the recurrent neural network structure of a bidirectional long short-term memory network (Bi-LSTM) with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data. A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping. The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale, which then stepwise approach the logging curve in the high-frequency band. Finally, a seismic-inversion method of nonlinear mapping multilevel well-seismic matching based on the Bi-LSTM network is developed. The characteristic of this method is that by applying the multilevel well-seismic matching process, the seismic data are stepwise matched to the scale range that is consistent with the logging curve. Further, the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well-seismic matching process, such as the inconsistency in the scale of two types of data, accuracy in extracting the seismic wavelet of the well-side seismic traces, and multiplicity of solutions. Model test and practical application demonstrate that this method improves the vertical resolution of inversion results, and at the same time, the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect. [ABSTRACT FROM AUTHOR]

Seismic impedance inversion is one of the key techniques for quantitative seismic interpretation. Most conventional post-stack seismic impedance inversion approaches are based on the linear theory, whereas the relationship between seismic response and impedance is highly nonlinear. Thus, it is challenging to implement conventional inversion methods to obtain high-resolution impedance for reservoir investigation. Convolutional neural network (CNN), a superior deep neural network, has a strong learning ability, which can learn from data and establish complex nonlinear mapping. However, CNN-based methods are generally heavily dependent on amounts of labeled data. Hence, an alternative seismic inversion approach is proposed that combines the closed-loop CNN and geostatistics. The closed-loop CNN is less dependent on labeled data, characterized by utilizing labeled data and unlabeled data simultaneously to train the neural network. The two subnets represent forward modeling and inversion respectively, constraining each other during the neural network training. Geostatistics can be used to enrich the training data for neural network training, taking into account geological and geophysical prior information. Synthetic data testing reveals that the proposed inversion scheme can obtain more reasonable results benefiting from labeled training data augmentation. The proposed inversion scheme is applied to the field data for identifying thin interbedded reservoir within delta depositional system. The predicted results obtained by the proposed inversion scheme are consistent with well log data and geological settings, offering insights into reservoir characterization and hydrocarbon identification. [ABSTRACT FROM AUTHOR]

Seismic data provide evidence about hydrocarbon deposition, geological and geophysical subsurface information, including geomechanical aspects. Deriving and understanding geomechanical properties is crucial for reservoir management as it can avoid drilling and production-related problems that cause environmental impacts associated with land subsidence and uplift. The Poison's ratio (PR), Young Modulus (YM), and elastic moduli for a reservoir block were estimated using 3D seismic pre-stack data and well data. 3D Mechanical Earth Models (MEM) were also developed using the well logs, seismic horizons, and drilling data. Seismic data-derived geomechanical properties were compared with the mechanical earth models for the first time for this field. Well-tie analysis was used for inversion of 3D seismic data to extract detailed waveform and amplitude information. The brittleness index of the subsurface layers was estimated, which is a critical rock property that provides information about rock hardness and fragility phenomenon. The brittleness index has a diverse range from 5-35%, with significant contrast at shallow zones. PR and YM models generated from 3D MEM and seismic data have average values of 0.2 -0.6 and 5 - 28 GPa with significant contrast from shales and carbonates. The study recommends that the drilling through these problematic zones should be avoided to avoid wellbore problems that cause challenges in maintaining wellbore integrity and reservoir management in the North-Heera field, Mumbai Offshore Basin.

Pore pressure is the pressure associated with fluids in the pore spaces of sedimentary rocks. This pressure varies from hydrostatic to severe overpressure. It is classified as abnormal pressure when it deviates from hydrostatic pressure. Incorrect prediction or the absence of pore pressure evaluation can lead to severe damage of the drilling platform, loss of personnel, waste of resources and environmental pollution. This study was carried out in an offshore field in Niger Delta with an objective of predicting the abnormal pressure regime from the seismic volume using seismic inversion technique. First, we employed the Eaton’s and Bower’s model of predicting pore pressure from Well log. The predicted pore pressure from the well was used to calibrate the P-impedance that was used in the inversion process. A pressure-based model for the seismic inversion process was developed. This was constrained by the relationship between the P-impedance and pore pressure in the study area. The predicted pore pressure from the Well logs aligned with the measured pore pressure in the formation. At about 3600 m to end of the Well, depicts a pressure regime that is a deviation from the hydrostatic pressure trend. The abnormal pressure regime is marked by the region where a change is observed in the normal compaction trend line. The cross plot of acoustic impedance against pore pressure reveals a linear relationship; with a direct relationship indicating a normal pressure interval and the indirect relationship at the abnormal pressure regime. The inverted 3D pressure result from the seismic volume validates the normal compaction trend line and also the pressure values estimated from the Well logs. The abnormal pressure regime in the study area is predicted to be found towards the end of Agbada Formation and through the Akata Formation due to the un-compacted shales.

The Brazilian pre-salt carbonate province is a scientific frontier that has been subject of extensive research and academic discussion. Santos basin carbonates show that reservoir quality is strongly conditioned by the characteristics of the depositional environment and its associated diagenetic history and alterations. Dolomitization and silicification are two of the most common diagenetic processes in Brazilian carbonates, and the combination of sedimentary and diagenetic complexity leads to a much more heterogeneous reservoir, especially complicated for geoscientists trying to mimic these geological features in terms of static and dynamic modelling. This study proposes a methodology for seismic derived diagenetic properties calculation through seismic quantitative interpretation and classification of inverted P- and S-impedance volumes applied to Brazilian Pre-Salt carbonate reservoirs. Our proposition is to establish relations between petroelastic properties and diagenetic features, first in the well-log domain and, later, in the seismic domain through different quantitative probabilistic techniques. Using the available seismic data and probabilistic approaches through stochastic inversion and Bayesian facies classification, we can interpret the occurrence of diagenetic silica and dolomite in 3D volumes. The volumetric analysis generated through the presented methodology can provide much more insights into the sedimentary history of the field, especially for static and dynamic porosity and permeability modelling. • Diagenetic features are a major concern for porosity modelling in pre-salt carbonates. • Most diagenetic studies are thin section limited and simply extrapolate facies away from wells without lateral constraints. • We calculated dolomitization and silicification volumes that are laterally mappable. • 3D diagenetic volumes can provide valuable inputs for petrophysical and geological modelling. [ABSTRACT FROM AUTHOR]

Abstract In an attempt to reduce the errors and uncertainties associated with predicting reservoir properties for static modeling, seismic inversion was integrated with artificial neural network for improved porosity and water saturation prediction in the undrilled prospective area of the study field, where hydrocarbon presence had been confirmed. Two supervised neural network techniques (MLFN and PNN) were adopted in the feasibility study performed to predict reservoir properties, using P-impedance volumes generated from model-based inversion process as the major secondary constraint parameter. Results of the feasibility study for predicted porosity with PNN gave a better result than MLFN, when correlated with well porosity, with a correlation coefficient of 0.96 and 0.69, respectively. Validation of the prediction revealed a cross-validation correlation of 0.88 and 0.26, respectively, for both techniques, when a random transfer function derived from a given well is applied on other well locations. Prediction of water saturation using PNN also gave a better result than MLFN with correlation coefficient of 0.97 and 0.57 and cross-validation correlation coefficient of 0.89 and 0.3, respectively. Hence, PNN technique was adopted to predict both reservoir properties in the field. The porosity and water saturation predicted from seismic in the prospective area were 24–30% and 20–30%, respectively. This indicates the presence of good quality hydrocarbon bearing sand within the prospective region of the studied reservoir. As such, the results from the integrated techniques can be relied upon to predict and populate static models with very good representative subsurface reservoir properties for reserves estimation before and after drilling wells in the prospective zone of reservoirs.

The fluid identification of fracture media is susceptible to factors such as observation data, fluid sensitivity parameters, and inversion algorithms. However, appropriate nonlinear algorithms can be applied to reduce errors generated during the inversion process, thus helping to improve the reliability of fluid identification. In this paper, analysis and comparison are made to various reflection coefficient approximate formulas, of which the applicability and accuracy are demonstrated in fluid-saturated poroelastic media, and the objective function is constructed further from Russell's reflection coefficient formula. In this sense, the genetic algorithm (GA) can achieve the pre-stack inversion of seismic waves. Based on the inversion results such as P-, S-wave velocity, and density, the Russell fluid indicator, Poisson's ratio, and Poisson impedance are combined to identify the water-, gas-, and oil-bearing fracture. Finally, the applicability and reliability of the method are demonstrated through numerical calculations in some regions of Marmousi2 model and the field data. The results show that the seismic data can be preliminarily analyzed by the inversion method, followed by combining the Russell fluid indicator, Poisson's ratio, and Poisson impedance, effectively reducing the multiplicity of the inversion results and improving the reliability of fluid identification in fractured media. • Proper fluid factors can effectively reduce the multiplicity of inversion results. • The objective function is formed with Russell's reflection coefficient. • Combining three parameters can improve the reliability of fluid identification in fractured media. [ABSTRACT FROM AUTHOR]

Seismic inversion is aimed at building a mapping from low-resolution seismic data to high-resolution impedance data. Most of the traditional methods have satisfactory interpretability, and most parameters tend to have specific physical definitions. On the other hand, deep learning-based methods present poor interpretability as their prediction performance is not always clearly explainable. One of the significant challenges of deep learning-based methods is to quantify the uncertainty of the model. The uncertainty includes aleatoric uncertainty and epistemic uncertainty, and epistemic uncertainty can be used to evaluate the predicted accuracy of the trained model. In this article, we propose a new deep learning model called uncertainty backpropagation network (UB-Net) to perform impedance inversion. The proposed UB-Net is based on a closed-loop framework and can predict the impedance and the epistemic uncertainty simultaneously. UB-Net has three closed-loop data flows, whereby the predicted uncertainty is utilized as the weight of loss functions to improve the inversion accuracy. Experimental analyses demonstrate that UB-Net presents advanced inversion accuracy on both synthetic and real examples. In particular, the mean absolute error (MAE) on synthetic examples drops by 40%, and the Pearson correlation coefficient (PCC) on real examples increases by 2%. Besides, compared with existing approaches, UB-Net presents superior spatial continuity and preserves more geological structures such as little faults in real examples. [ABSTRACT FROM AUTHOR]

Gas content and brittleness characteristics, which are mostly quantified using the fluid factor and brittleness index, are the key factors in the evaluation of coal‐measure gas reservoirs, which are similar to traditional unconventional gas reservoirs. However, most previous seismic inversion research and evaluations only focused on one of the parameters. Therefore, in this study, we performed a combined inversion of the fluid factor and brittleness index based on pre‐stack seismic records collected from a typical coal‐measure gas block in the Sichuan Basin. First, a new P–P wave reflection coefficient approximation based on both parameters was derived, and the precision and sensitivity of the inversion parameters were analysed. We then constructed a new inversion equation based on Bayes' theorem. The logging curves obtained for a typical coal‐measure gas well and through pre‐stack seismic profile were used to evaluate the inversion method. The results of the model test and profile inversion at the well location were in good agreement with the original logging values. Finally, we analysed and discussed the results of the inversion of both parameters for the evaluation of the gas content and brittleness characteristics of the target reservoirs. [ABSTRACT FROM AUTHOR]

Seismic 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. [ABSTRACT FROM AUTHOR]

The conventional approach in reservoir characterization uses an extrapolation of petrophysical properties calculated from well logs in determining the entire field volume. This technique fails to acknowledge the heterogeneity of a reservoir and consequently leads to errors (under or overestimation) in reservoir capacity quantification. This research employs a deterministic seismic inversion approach in determining the petrophysical properties of each reservoir sand. First, the reservoir sand was identified from intervals of low gamma-ray value with corresponding high resistivity log value. Then, a synthetic seismogram was generated to tie the well to the seismic data and identify events corresponding to the reservoir sands. This was done by convolving the developed reflectivity series, derived from approximated Zeoppritz reflectivity with a zero-phase wavelet generated using the seismic. Acoustic impedance from the well and seismic were analyzed and strongly correlated. The impedance was then constrained with determined petrophysical parameters from the well, and it was used in generating the petrophysics base model used in the inversion. Finally, deterministic seismic inversion was carried out to determine the petrophysical properties such as porosity, the volume of shale, and water saturation associated with the fluids from the seismic volume of Jay-field shallow offshore of Niger Delta. [ABSTRACT FROM AUTHOR]

The identification of gas content is very important for exploration and development of coalbed methane (CBM) reservoirs. As a kind of gas-bearing reservoirs in coal seam, CBM reservoirs usually show strong heterogeneity, which makes the gas content varies greatly in the strata. What's more, the thin interlayer that is common in coal-bearing formation makes it difficult to predict the favorable gas-bearing distribution based on conventional methods. In this study, a seismic waveform indication inversion method was applied to reveal the gas content of No. 8 coal seam reservoirs in the Upper Carboniferous Benxi Formation of the DJ area in the eastern of the Ordos Basin, China. The first step of this method is to calculate the p-wave impedance inversion volume of No. 8 coal seam. The second step is to build the correlation between elastic parameters and gas content in No. 8 coal seam. Through the statistical analysis based on velocity, density, p-wave impedance and measured gas content data of 16 wells, the fitting formula between p-wave impedance and gas content is obtained, with a highest correlation coefficient up to 96%. The third step is to calculate the gas content data volume from the p-wave impedance inversion volume by the above fitting formula, and then the quantitative plane distribution of gas content in No. 8 coal seam can be predicted. The prediction results indicate that the gas content of No. 8 coal seam can be divided into two Classes. To verify the reliability of the inversion results, the production data of well X11 was applied to verify the gas content which located in the Class I area. The application of seismic waveform indication inversion has provided a precise prediction for the spatial distribution of gas content in CBM reservoirs, serving as a basis for locating and designing wells for CBM development. [ABSTRACT FROM AUTHOR]

Geostatistical seismic rock physics amplitude-versus-angle (AVA) inversion allows the joint prediction of rock and fluid properties from seismic reflection data. In these seismic inversion methods, the model perturbation and update occur iteratively in the petrophysical domain. A facies-dependent precalibrated rock physics model is applied to the simulated rock properties to calculate elastic properties. Synthetic seismic reflection data are computed from these elastic models. The rock physics models are calibrated at the well locations and act as the link between the rock and the elastic domains, remaining unchanged during the inversion procedure: the convergence of the inversion and the geological plausibility of the inverted rock property models depend on the quality of the calibration and available well-log data. To overcome this limitation, account for calibration errors and geological scenarios not sampled at the well locations, we introduce an iterative geostatistical seismic AVA inversion where the elastic predictions from a facies-dependent precalibrated rock physics model are updated iteratively, based on the mismatch between observed and synthetic data. We assume an initial $a$ priori uncertainty in the rock physics model and update it iteratively based on the data mismatch. The proposed method is applied in a 1-D synthetic example to illustrate the concept and validate the method. Then, we apply the inversion method to a 3-D real data set. In this application, we use a blind well to assess locally the performance of the technique. The results show that the parameters of space exploration is not compromised by the rock physics model’s uncertainty. [ABSTRACT FROM AUTHOR]

One of the main goals of pre‐stack seismic inversion is the estimation of elastic properties (i.e. P‐, S‐ wave velocities and density) and litho‐fluid classes in the investigated area. To this end, many inversion strategies have been proposed, but the most popular is based on a two‐step inversion approach: First, elastic properties are inferred from pre‐stack data, and then a classification algorithm is used to convert the outcomes of the first stage into litho‐fluid facies. In this work, we propose an alternative approach based on recurrent neural networks. We train two bidirectional long short‐term memory networks to predict the inverse mappings from pre‐stack seismic data to elastic properties, and litho‐fluid classes. In the elastic inversion, we also use a Monte Carlo simulation approach to properly propagate onto the model space both the uncertainties related to noise contamination in the data and to the modelling error introduced by the network approximation. One crucial aspect of any machine‐learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are drawn according to a previously defined elastic and facies prior models derived from actual well log recordings. In particular, we assume a Gaussian‐mixture elastic prior, and we also take into account the uncertainties affecting the estimation of the transition probabilities of facies. We invert each seismic gather independently, and in this context, the generation of the training set, and the learning process can be accomplished with a very limited computational effort on a common notebook. Once trained, the networks estimate the elastic properties, the litho‐fluid facies and the related uncertainties from the pre‐stack data in near real‐time. Synthetic and field data inversions are used to validate the proposed method. The network predictions are also benchmarked against the outcomes of a more standard two‐step approach that combines a linear elastic inversion and a subsequent point‐wise Bayesian classification. Our results demonstrate that the implemented algorithm guarantees more accurate elastic property estimations and facies predictions than the standard inversion strategy. In particular, the predictions provided by the long short‐term memory network are less affected by erroneous assumptions on the noise statistics and prior model and by errors in the estimated source wavelet. [ABSTRACT FROM AUTHOR]

Based on interpretation and inversion of high‐resolution seismic data, a previously undocumented occurrence of pore‐filled gas hydrates that are associated with a submarine canyon system was discovered in the shelf break of the north‐east Qiongdongnan Basin (QDNB) in the northern South China Sea (SCS). Continuous bottom simulating reflections (BSRs) were recognized in the submarine striking NW‐SE ridges along the shelf break. The two‐way travel times (TWTs) of the BSRs are about 200–250 ms below the sea floor and the burial depths are mapped at ~180–225 mbsf after time‐depth conversion. The distribution of the enhanced reflections (ERs) and acoustic blanking zone overlying the BSRs indicate possible pore‐filled gas hydrate enrichment. ERs have been commonly observed immediately below the BSRs, indicating the accumulation of free gas, which exhibits distinct amplitude versus offset (AVO) anomalies. The acoustic impedance inversion results show that the gas hydrate saturation is 20–50%, and the thickness of the gas hydrate‐bearing layers is usually ~10–40 m, with a maximum thickness of up to ~90 m in local regions. Large‐scale listric faults originating from the deep sag connect the hydrocarbon source region and the shallow gas hydrate stability zone (GHSZ) and act as migration pathways for biogenic gas and potentially thermogenic gas. In addition, hydrocarbons may also be transported into the GHSZ along the sidewalls of the canyon. Canyon incision is believed to cause the increase in the burial depth of the BSRs on the flank of the ridge toward the base of the channel and thereby to trap the hydrocarbons migrating along the sidewall in permeable beds due to the decrease in permeability caused by the gas hydrate accumulation, resulting in the occurrence of ERs immediately below the BSRs. It is speculated that in the channel‐levee system, the coarse‐grained sediments should be deposited within the GHSZ, and the reservoir's physical properties are conducive to the wide distribution and enrichment of pore‐filled gas hydrates in the shelf break of the northern QDNB. [ABSTRACT FROM AUTHOR]

Seismic blind high-resolution inversion (BHRI) aims at retrieving the high-resolution data to characterize the stratigraphic structures in the case of an unknown seismic wavelet. However, the unknown wavelet and ill-posedness pose a great challenge to the high-resolution inversion. Regularization-based BHRI is an effective approach. However, it is sensitive to the sets of initial values, regularization terms, and regularization parameters and suffers from computational burden problems. To address these issues, we propose a sequential iterative deep learning method (SIDLM) to implement a BHRI in a fast computational speed, which incorporates three learned components to sequentially invert initial high-resolution data, seismic wavelet, and final high-resolution data in an end-to-end fashion. Specifically, to mitigate the influence of initial values, a data-driven network U-Net is adopted to learn an initial high-resolution data. Furthermore, the architecture makes use of prior information encoded in the forward operator to build a new general alternating direction method of multipliers (ADMM)-like iterative deep neural network, instead of the traditional alternating iterative inversion. The proposed ADMM-like network utilizes the convolutional neural networks to learn the proximal operators to solve each subproblems in alternating iterative inversion. Therefore, all parameters of BHRI, such as the regularization parameters and transform operator, can be implicitly learned from the training datasets in an end-to-end fashion, not limited to the form of the penalty function. Finally, the synthetic and field data examples are conducted to demonstrate the effectiveness of the proposed SIDLM.

Seismic inversion is an effective tool to estimate the properties of subsurface strata from seismograms. However, the intrinsic ill-posedness of the inversion problem causes the inverted subsurface properties to be easily polluted by inversion errors due to the random noise in the observed data. The inversion errors make it difficult to interpret the geological features of subsurface strata, especially their boundaries and textures. To recover high-fidelity inversion results from noisy observed data, we developed a hybrid total-variation (HTV) regularization operator in this research. Compared with the conventional total-variation (TV) regularization, the HTV regularization has two advantages when applied in seismic inversion. One advantage is that HTV regularization overcomes the typical staircase effect of conventional TV regularization while maintaining the advantage of TV regularization in edge-preserving properties. The determination of regularization parameters is also a difficult problem in seismic inversion with conventional TV regularization. A large regularization parameter may lead to an oversmoothed inversion result, while a small value leads to a noisy inversion result. Another advantage of HTV regularization is that it can generate proper inversion results even if a regularization parameter that is too large is adopted, which makes it easy to set the value of the regularization parameter. Numerical examples demonstrate the performance of the HTV regularization method proposed in this research.

The Kuqa Depression in Tarim Basin develops fan deltaic, braided river deltaic, and lacustrine sedimentation, especially extensive braided river deltaic sedimentation, in the Cretaceous-Paleogene Systems. However, fine sedimentary pattern of the Kuqa Depression and North Tarim Uplift belt since the Middle Cenozoic still needs to be studied. As a combination zone of the Kuqa Depression and North Tarim Uplift, the sedimentary characteristics of the DB area in the southern margin of the east of the Kuqa depression have been paid more attention. To better understand the sedimentary framework in the Kuqa Depression and North Tarim Uplift in the Mesozoic and Cenozoic, through joint geologic-geophysical study, sedimentation of sandstone at the bottom of the Paleogene Suweiyi Formation in the DB area and its peripheral area is investigated. Sedimentary facies and sandstone distribution in the area are identified through core observation, component analysis, logging interpretation and seismic inversion. Based on seismic facies analysis, sedimentary facies distribution in the area is delineated. Results show that sandstone at the bottom of the Paleogene Suweiyi Formation in the DB area and its peripheral area is developed in shore-shallow lacustrine beach-bar facies. The beach-bar arenaceous sediments are mainly distributed in the southern DB area and the shallow lacustrine mudstone is developed in northern DB area, showing obvious north-south differentiation characteristics.

Under the condition of thin interbeds with great lateral changes in terrestrial basins, a seismic meme inversion method is established based on the analysis of seismic sedimentology technology. The relationship between seismic waveform and high-frequency well logs is established through dynamic clustering of seismic waveform to improve the vertical and horizontal resolution of inversion results; meanwhile, by constructing the Bayesian inversion framework of different seismic facies, the real facies controlled inversion is realized. The forward model verification results show that the seismic meme inversion can realize precise prediction of 3 m thick thin interbeds, proving the rationality and high precision of the method. The application in the Daqing placanticline shows that the seismic meme inversion could identify 2 m thin interbeds, and the coincidence rates of inversion results and drilling data were more than 80%. The seismic meme inversion method can improve the accuracy of reservoir prediction and provides a useful mean for thin interbeds prediction in terrestrial basins.

A seismic traveltime inversion method is proposed for building smooth velocity models using traveltime observed on irregular surface. Model parameterization in this study is described by a piecewise constant velocity field on a rectangular grid parameterized by sigmodal functions, which is beneficial for the description of irregular surface with high degree of approximation. The velocity field is defined in the rectangular grid which is used for the description of velocity distribution everywhere in the model sigmoidal interpolation. In addition, we use the simple Genetic Algorithm for the inversion procedure. Through this global scope inversion method, we provide high-resolution estimates of the model parameter and ensure that the results obtained are in accordance with the actual data. Our method is validated with synthetic examples of heterogeneous isotropic media and compared to Simulating Annealing. The inverted velocity models and approximate ray paths obtained coincide well with the trajectories simulated using the seismic ray tracing in synthetic heterogeneous isotropic media.

Traditional optimization algorithms are usually applied to estimate the elastic parameters of the subsurface by using field seismic data. However, these optimization algorithms highly depend on prior knowledge (e.g., the initial model setup and sparsity), leading to serious inversion uncertainties. Nowadays, with the rapid development of neural networks, convolutional neural networks (CNNs) have been widely imposed on estimating elastic parameters from field data. However, the deficiency of labeled seismic data impedes the CNNs application in seismic inversion. Moreover, both the size and diversity of labeled datasets are also critical factors influencing the accuracy and resolution of predicted parameters when using the CNNs-based inversion techniques. In this work, taking the unconventional tight sandstone formation as an example, we develop a geological and geophysical model driven CNNs (GGCNNs), named as GGCNNs. The proposed GGCNNs allow us to take advantage of both the prior geological information and basic geophysical model from the generated synthetic labeled prestack seismic datasets, representing essential characteristics of the subsurface. Moreover, under the consideration of data diversity, the GGCNNs model enables us to make a tradeoff between the inversion accuracy and labeled data size. Applications on both synthetic and field data clearly demonstrate the effectiveness of the proposed GGCNNs model for predicting elastic parameters by using prestack seismic data, i.e., its predicted results are with high accuracy in the vertical profile and continuity and smooth in the horizon slice. [ABSTRACT FROM AUTHOR]

In the early 1970's, oil and gas exploration was started in the west of Shetlands, United Kingdom, which is a part of the Atlantic Margin. One of the main exploration targets in the west of Shetlands is the Paleocene deepwater sandstones, some discoveries of which proceeded to development or production stages. The Laggan field discovered in 1986 is under development together with a neighboring discovery, Tormore field. The Foinaven field has been producing oil since 1997. These Paleocene fields are characterised by seismic attributes including amplitude and AVO anomalies, and understanding of these attributes enables reduced geological risks and estimation of reserves, not only in exploration but also in development and production stages. On the other hand, there are many failures of exploration wells in areas with seismic anomalies. Loizou (2005) examined all the exploration wells in the west of Shetlands drilled until 2005, and concluded that only 9 out of 39 wells, which have targeted Paleocene seismic anomalies, resulted in geological success. To improve the success-to-failure ratio in exploration associated with seismic anomalies,辻 is important to construct a plausible geological model including trapping mechanism, seal, reservoir, source rock and hydrocarbon migration that is consistent with hydrocarbon accumulations implied by seismic anomalies. The Tornado discovery was a prospect, which targeted the Paleocene deepmarine sandstone associated with a seismic amplitude anomaly; The first exploration well confirmed a gas column with oil leg. Prior to the drilling, seismic data analysis was carried out together with a CSEM data acquisition. Those geophysical information was integrated with a geological model built on the information from nearby wells and other geological studies to reduce geological risks (Pickering et al.t 2011). This paper presents successful examples of integrated methods of exploration activities which do not rely only on seismic anomalies. [ABSTRACT FROM AUTHOR]

We investigate regional variations in the Lg-wave quality factor (Q) in the Indian Shield with a tomographic method using the LSMR (an iterative solver of least squares algorithm) iterative solver. Vertical broadband seismograms of 223 crustal earthquakes recorded by 113 stations deployed on the Indian Shield are processed to extract the Lg amplitude spectra. The relatively large dataset with good ray coverage allows us to image the lateral variation of the crustal attenuation over the region. The tomographic inversion reveals that the Lg Q0 in the Indian Shield varies from 50 to 650, while the frequency-dependent parameter (η) varies between 0.4 and 1.1 with an average value of 0.7. The attenuation image at 1 Hz suggests the heterogeneities in the Indian crust and clearly demarcates the diverse geological regimes that were hitherto not known. Structural features such as rift, suture zones, sedimentary, and active regions are characterized by high attenuation (Q0 < 200), also associated with higher heat flow values. The cratons of the Indian Shield exhibit an average Q0 of 450, slightly lower than the global average. Broadly speaking, low Q0 (< 200) is found in the southern parts of Dharwar cratons till southern granulitic terrain, attributed to both the rock composition and higher temperature. To a large extent, the Lg-Q is anticorrelated with bulk crustal thickness, bulk Vp/Vs, and surface heat flow, implying that the thicker and intermediate-to-mafic crust has higher attenuation. Our results also suggest that the attenuation scenario of the Indian Shield is more sensitive to temperature than composition. [ABSTRACT FROM AUTHOR]

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L 1 − 2 regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L 1 − 2 regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L 1 − 2 regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs. [ABSTRACT FROM AUTHOR]

Increasing mining depths and intensities have resulted in frequent accidents during roadway excavation, thus increasing the necessity of preemptively determining the geological conditions of rock formations along roadways. Identifying the spatial distributions of lithotypes and the mechanical properties of non-marker bed intervals in coal measure strata are crucial for roadway horizon design, roof management, and efficient excavation. Therefore, based on petrophysical tests, well logging, and seismic data, a comprehensive method is proposed, which uses post-stack wave impedance inversion, petrophysical statistical analyses, and pre-stack simultaneous inversion to predict the lithology and elastic parameters of rocks surrounding roadways. The lithology of the target layer is first divided according to the wave impedances of the inversion body, and the sand lenses within the mudstone interval are described. Based on petrophysical test data, the shear-wave conversion equations of the sand lenses in this target interval and background mudstone are then obtained via marginal limit extrapolation. Finally, pre-stack simultaneous inversion, based on the seismic data and shear-wave conversion curves of different lithologies, is performed to obtain the elastic parameter profile of the target interval in the cross-section of the roadway, laying the foundation for further prediction of the mechanical properties of the surrounding rocks. The results agree with roadway drilling data by > 90%. [ABSTRACT FROM AUTHOR]

Objective: this work presents the results obtained in the development of a seismic velocity inversion mo­del. The reference times recorded on the surface are taken and using the inversion model to obtain the initial reference model (hypocenters and velocities), starting from an unknown model. Methodology: A hypothetical reference model is pro­posed containing 64 blocks with interval velocity, 16 recording stations on the surface, and 64 earth­quakes in the center of each block. With this model, the reference arrival times are generated for each ear­thquake registered in each station. The inversion mo­del is made up of two parts: the direct model, which allows calculating the arrival times of the signal regis­tered on the surface according to the hypo-central lo­cation of the earthquake and the velocity of the P and S wave of the medium; and the inverse model, which estimates a model of the velocity of the environment and hypo-central locations of the earthquakes that are the input variables of the direct model. The direct model was developed with the wave equation, while the inverse model was developed by modifying the generalized inverse matrix by introducing a factor called “damping.” Results: The discretization model is based on the finite difference method.. When estimating the values of velocity and hypo-central location with the inverse algorithm, the propagation of the wave is simulated with the direct model, and they are compared with the data of reference times measured on the surface. Depending on the value of the mean square error, we proceed to modify the mean velocities and hypo­centers of the earthquakes. This process is repeated iteratively until the calculated error is less than a to­lerance of 2x10-3s2. Conclusions: It was found that the estimated values of velocity and hypo central locations coincide well for regions closer to the surface, while for deep regions the error is greater compared to the hypothetical re­ference model.

Abstract Direct hydrocarbon indicator (DHI) expressions observed on seismic could arise due to various geological conditions. Such expression could lead to misinterpretation as hydrocarbon presence if not properly analyzed. This study employs rock physics attributes analysis to evaluate an identified prospect in the undrilled area of the studied reservoir. Prospect identification was actualized by analyzing structural and amplitude maps of the reservoir, which revealed a possible roll over anticline at both the exploited and prospective zone, with a very good amplitude support that conforms to structure. Well-based cross-plot analysis adopted four cross-plot techniques for feasibility study to test the applicability of rock physics for prospect evaluation in the field; Lambda-Rho versus Lambda-Rho/Mu-Rho ratio; Mu-Rho versus Lambda-Rho; and Poisson Ratio versus P-impedance. The result presented Poisson ratio, Lambda-Rho and Lambda/Mu-Rho ratio as good fluid indicator and Mu-Rho as a viable lithology indicator. As such, they were selected for seismic-based attribute and cross-plot analysis to validate the identified prospect. The results from seismic-based analysis showed consistency in the expression of the analyzed attribute at both the exploited and prospective zone. The seismic-based cross-plot analysis result was similar to the well-based analysis and was able to confirm that the observed amplitude expression in the exploited zone is an indication of hydrocarbon-bearing sand.

Quantum computing, along with quantum metrology and quantum communication, are disruptive technologies that promise, in the near future, to impact different sectors of academic research and industry. Among the computational challenges with great interest in science and industry are the inversion problems. These kinds of numerical procedures can be described as the process of determining the cause of an event from measurements of its effects. In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. We compare the obtained results from the quantum computer to those derived from a classical algorithm. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.

Although a mixed carbonate–siliciclastic system of the Strawn Group on the Eastern Shelf in King County, Texas, USA provides excellent hydrocarbon reservoirs, facies variability and reservoir properties within such systems are not well understood. We conducted prestack, simultaneous seismic inversion, and high-level petrophysical analysis to derive elastic properties of rocks to facilitate lithology identification and determination and distribution of the different carbonate facies. Our results show that (1) the Strawn Group in King County is dominated mostly by carbonates and (2) given the ratio of P- and S-wave velocity (Vp/Vs ratio), the carbonates can be separated into three facies: (a) high-Vp/Vs-ratio shelf-edge reef carbonates, in which the Vp/Vs ratio decreases linearly as porosity increases and the Vp/Vs ratio varies from ~2.1 to ≤2.6; (b) moderately low-Vp/Vs-ratio shelf (platform) carbonates, in which the Vp/Vs ratio also decreases as porosity increases and in which the Vp/Vs ratio ranges from ~1.75 to ≤2.15; (c) extremely low-Vp/Vs-ratio slope and basin carbonates, in which the Vp/Vs ratio, although appearing to be almost constant for a wide range of porosity, increases as porosity increases, and in which most Vp/Vs-ratio values appear to range from ~1.5 to ≤2. Results of a through c can be summarized thusly: the Vp/Vs ratio of reef carbonates >the Vp/Vs ratio of platform carbonates and >the Vp/Vs ratio of slope and basin carbonates in the study area.

In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance.

As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on the initial models. This suggests that reasonable initial models act as a supplement to reliable variation trends in formation and can reduce the non-uniqueness of inversion results. In this article, we introduce a well-log interpolation method with a feature map-guided non-local means algorithm, which is for establishing high-fidelity initial models used for prestack seismic inversion. First, we briefly review the basic theory of general model-driven prestack seismic inversion. Then, we use dictionary learning to split the poststack seismic record into patches, and represent them with sparse vectors, instead of directly using seismic record. The advantage of dictionary learning is that it can adaptively extract useful signals from noisy observed data and provide fine structures by sparse reconstruction. Therefore, the proposed feature extraction method can improve the noise immunity and reliability of the well-log interpolation. More accurate initial models are pre-constructed efficiently by our feature extraction method, which improves the reliability of prestack seismic inversion results. Two kinds of observed seismic data are used, including the poststack seismic record for well-log interpolation and prestack seismic data used for inversion. Synthetic and field data tests both demonstrate the favorable performance of the proposed well-log interpolation method. In summary, a novel and convenient initial model building approach is provided, which contributes to seismic exploration and geologic modeling.

Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5 km × 4.5 km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder–decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.9143 (out of 1.0) in the presence of field noise at 10 dB signal-to-noise ratio.

In this study, we develop a 3D geostatistical seismic inversion method based on turning bands simulation and co-simulation to estimate acoustic impedance (AI) from seismic and well data. The proposed method uses an iterative approach based on cross-over concept in genetic algorithm optimization to perform global stochastic inversion. The objective function of the optimization algorithm is the measure of correlation coefficients between the modeled and observed seismic data. In the first iteration of the proposed algorithm, the seismic cubes corresponding to the AI realizations generated by the turning bands simulation are compared to the observed seismic data. Subsequently, the local areas of AI models producing the highest correlation coefficients to the observed seismic data are merged to construct a new supplementary cube that is further used as the secondary variable in the turning bands co-simulation for the next iteration. Generation of the seismic cubes and picking the best AI cube via cross-over genetic algorithm approach is performed iteratively until convergence to a constant correlation coefficient between the modeled and observed seismic data. The proposed method is applied to a synthetic dataset that includes 20 wells with known AI logs and 3D stacked seismic data. According to the results, the algorithm converges to a constant correlation coefficient after a few iterations. In addition, it is observed that employing multi-attribute analysis outputs (meta-attributes) during turning bands co-simulation in the initialization step would improve the final global correlation coefficient from 0.774 to 0.815. [ABSTRACT FROM AUTHOR]