ALBERT

Description: Tight-gas sandstone reservoirs of the Ordos Basin in China are characterized by high rock-fragment content, dissimilar pore types, and a random distribution of fluids, leading to strong local heterogeneity. We model the seismic properties of these sandstones with the double-double porosity theory, which considers water saturation, porosity, and the frame characteristics. A generalized seismic wavelet is used to fit the real wavelet, and the peak frequency-shift method is combined with the generalized S-transform to estimate attenuation. Then, we establish rock-physics templates (RPTs) based on P-wave attenuation and impedance. We use the log data and related seismic traces to calibrate the RPTs and generate a 3D volume of rock-physics attributes for the quantitative prediction of saturation and porosity. The predicted values are in good agreement with the actual gas production reports, indicating that the method can be effectively applied to heterogeneous tight-gas sandstone reservoirs.

Description: The complete characterization of a reservoir requires accurate determination of properties such as the porosity, gamma ray, and density, among others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. In general, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray, suggesting great uncertainty in the determined relationship. Although for many rocks there is a well-established petrophysical model correlating the P-impedance to porosity, there is not a comparable model correlating the P-impedance to gamma ray. To address this issue, interpreters can use crossplots to graphically correlate two seismically derived variables to well measurements plotted in color. When there are more than two seismically derived variables, the interpreter can use multilinear regression or artificial neural network analysis that uses a percentage of the upscaled well data for training to establish an empirical relation with the input seismic data and then uses the remaining well data to validate the relationship. Once validated at the wells, this relationship can then be used to predict the desired reservoir property volumetrically. We have described the application of deep neural network (DNN) analysis for the determination of porosity and gamma ray over the Volve field in the southern Norwegian North Sea. After using several quality-control steps in the DNN workflow and observing encouraging results, we validate the final prediction of the porosity and gamma-ray properties using blind well correlation. The application of this workflow promises significant improvement to the reservoir property determination for fields that have good well control and exhibit lateral variations in the sought properties.

Description: Nowadays, there are many unsupervised and supervised machine learning techniques available for performing seismic facies classification. However, those classification methods either demand high computational costs or do not provide an accurate measure of confidence. Probabilistic neural networks (PNNs) overcome these limitations and have demonstrated their superiority among other algorithms. PNNs have been extensively applied for some prediction tasks, but they have not been well studied regarding the prediction of seismic facies volumes using seismic attributes. We have explored the capability of the PNN algorithm when classifying large- and small-scale seismic facies. In addition, we evaluate the impact of user-chosen parameters on the final classification volumes. After performing seven tests, each with a parameter variation, we assess the impact of the parameter change on the resultant classification volumes. We find that the processing task can have a significant impact on the classification volumes, but we also find how the most geologically complex areas are the most challenging for the algorithm. Moreover, we determine that even if the PNN technique is performing and producing considerably accurate results, it is possible to overcome those limitations and significantly improve the final classification volumes by including the geologic insight provided by the geoscientist. We conclude by proposing a new workflow that can guide future geoscientists interested in applying PNNs, to obtain better seismic facies classification volumes by considering some initial steps and advice.

Description: Compaction effects can obscure the impedance separation between hydrocarbon-bearing and fully brine-saturated sandstones. We have improved their discrimination by removing depth-related trends from inverted seismic impedance. Although the ratio of compressional- to shear-wave velocity versus seismic compressional-wave impedance crossplots shows differences among pay, brine sand, and shale trends, using absolute inverted impedances only imperfectly distinguishes hydrocarbon sands from brine sands due to outliers. In a given locality, statistical comparison of well log and seismic-derived impedances enables us to obtain a shale impedance model for a lithology baseline to detrend the impedance from the effects of burial and overburden. This has the effect of unmasking anomalies associated with hydrocarbon-bearing sands and serves as a reliable fluid discriminator. For an offshore Gulf of Mexico data set on the flank of a salt dome, with pay occurring over a wide range of depths, we identify hydrocarbon-bearing sands with a greater success rate after detrending the absolute seismic impedance.

Description: Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.

Description: Seismic amplitude-variation-with-offset (AVO) inversion from prestack seismic data plays a significant role in estimating elastic parameters and characterizing reservoir properties. In general, sparse regularization is widely used to solve ill-posed inverse problems by reducing the solution space of subsurface parameters, which makes seismic AVO inversion more stable. However, the traditional sparse constraint inversion only focuses on the vector sparsity of reflectivity, instead of the structural sparse characteristics of the estimated parameters. Consequently, various elastic parameters demonstrate different formation structural features in the same location of stratum. In this study, we have developed a novel approach that combines the structural sparsity and the vector sparsity of the model reflectivity to establish the posterior probability density distribution and solve the objective function of the model parameters. Based on the relationship among multiple elastic parameters, we divide the model parameters to be inverted into several groups according to intrinsic structural sparse characteristics of elastic parameters. In this case, all of the model parameters at the same sampling point are classified into the identical group, which ensures that different estimated parameters indicate the same characteristic in terms of stratigraphic structure. From the perspective of Bayesian inference, we use the modified Cauchy probability density function (PDF) to characterize the group sparsity and describe the relationship among model parameters in the same group by Gaussian PDF. Furthermore, we estimate the optimum solution corresponding to the maximum a posteriori probability under Bayesian inference. Synthetic experiments on a Marmousi model prove that the estimated P-velocity, S-velocity, and density are consistent with those of the real models, and the application of field data confirms the availability and feasibility of group sparse inversion.

Description: The connectivity of complex carbonate reservoirs has an essential impact on the exploration and development of these reservoirs. From geologic genesis, the connectivity of complex carbonate reservoirs is mainly controlled by faults and dissolution. Therefore, accurate identification of faults and karst caves is the key to studying reservoir connectivity. The Ordovician carbonate reservoir in the Hudson Oilfield of the Tarim Basin is used for our reservoir connectivity analysis study. First, we calculate the coherence and curvature attributes, respectively, and then merge the two attributes using a neural network algorithm. Finally, we use the ant-tracking method to track the faults for the merged data. The results show that the approach substantially enhances deterministic faults that can be seen directly on the seismic data, and subtle faults can also be identified. For reservoir identification, we use the diffraction imaging method to describe the karst reservoir in this study area. The results show that diffraction imaging can identify small-scale caves that cannot be well recognized on the seismic reflection data. Furthermore, the caves connected on the diffraction seismic data are isolated from each other on the seismic reflection data, making the connection between caves clearer. Based on the results of the fault and cave identification, we analyze the reservoir connectivity of the study area using the oil pressure and daily production data, which indicates that the north–northwest and near-north–south faults probably play a role in the connection of the reservoirs, whereas the northeast–east faults tend to block the connection of the reservoirs.

Description: Salt is one of the most effective agents for trapping oil and gas. As a ductile material it can move and deform surrounding sediments and create traps. However, effective sealing of reservoirs for movement of hydrocarbons along breaching faults or fracture swarms (i.e. macroseepage) is a completely different mechanism than the molecular movement of hydrocarbons through grain boundaries and microfractures as found in microseepage. Forum Exploration chose to evaluate the applicability of passive surface geochemistry for mapping hydrocarbons in their onshore West Gebel El Zeit lease due to difficulties in seismic imaging through salt and anhydrites sequences. Two economic producing wells had been drilled in the lease, but due to compartmentalization and complexity in the area, three dry wells had also been drilled. Target formations included the Kareem Formation at ∼2,700 m and the Rudeis Formation at ∼3,000 m.The geochemical survey encompassed 100 passive geochemical modules. Passive samplers were also deployed around two producing wells and one dry well. Calibration data generated positive thermogenic signatures around the two producing wells in contrast to the background or baseline signature developed around the dry well. The Rudeis Formation calibration signature ranged from ∼nC5 - ∼nC9 while the Kareem Formation calibration signature ranged from ∼nC6 nC12. This suggested the Rudeis calibration signature was lighter than the Kareem. This correlated with independent API gravity testing on produced oil samples (41o API gravity oil for the Rudeis, 35o API gravity oil for the Kareem).A post-survey well, Fh85-8, was drilled based on combined geochemical and seismic data results. The well was an oil discovery, with initial production of 800 BOPD. The evidence presented in this Gulf of Suez example shows that microseepage can occur through salt sequences. As such, ultrasensitive passive surface geochemical surveys provide a powerful tool for derisking salt plays.

Description: Distributed acoustic sensing (DAS) technologies are now becoming widespread, particularly in vertical seismic profiling (VSP). Being a spatially densely sampled recording of the seismic wavefield, DAS data provide an extended measurement compared with point geophone VSP. We have developed a basic theory that enables an intuitive geophysical understanding of DAS data amplitudes using the concepts of kinetic and potential energy and their fluxes. We start by relating DAS and geophone measurements to potential energy and kinetic energy, respectively. We use this relationship and energy balancing along the well to construct a scheme for inverting DAS and geophone wavefields for density and velocity simultaneously. Then, recognizing that it may be impractical to have geophones and DAS, we adopt a second inversion scheme that eliminates the need for geophones and uses up- and downgoing DAS wavefields instead. There is no need for first-break picking or windowing the data, and the full-length DAS records can be used in both inversion schemes. We test these inversion schemes on 2D elastic synthetics.