We have addressed the geophysical problem of obtaining an elastic model of the subsurface from recorded normal-incidence seismic data using convolutional neural networks (CNNs). We train the network on synthetic full-waveform seismograms generated using Kennett’s reflectivity method on earth models that were created under rock-physics modeling constraints. We use an approximate Bayesian computation method to estimate the posterior distribution corresponding to the CNN prediction and to quantify the uncertainty related to the predictions. In addition, we test the robustness of the network in predicting impedances of previously unobserved earth models when the input to the network consisted of seismograms generated using: (1) earth models with different spatial correlations (i.e. variograms), (2) earth models with different facies proportions, (3) earth models with different underlying rock-physics relations, and (4) source-wavelet phase and frequency different than in the training data. Results indicate that the predictions of the trained network are susceptible to facies proportions, the rock-physics model, and source-wavelet parameters used in the training data set. Finally, we apply CNN inversion on the Volve field data set from offshore Norway. P-wave impedance [Formula: see text] inverted for the Volve data set using CNN showed a strong correlation (82%) with the [Formula: see text] log at a well.

The dielectric constant of fluorinated polymides, their blends, and composites is known to decrease with the increase in free volume due to a decrease in the number of polarizable groups per unit volume. Herein, we report an interesting finding on the origin of dielectric constant in a polymer blend prepared using a fluorine-containing polymer and a polyimide probed in terms of its available free volume, which is distinct from the generally observed behavior in fluorinated polyimides. For this study, a blend of poly(vinylidene fluoride-co-hexafluoro propylene) and poly(ether imide) was chosen and the interaction between them was studied using FTIR, XRD, TGA, and SEM. The blend was investigated by positron annihilation lifetime spectroscopy (PALS), Doppler broadening (DB), and dielectric analysis (DEA). With the increase in the free volume content in the blend, surprisingly, the dielectric constant also increases and is attributed to additional space available for the polarizable groups to orient themselves to the applied electric field. The results obtained would pave the way for more effective design of polymeric electrical charge storage devices.

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave velocity ([Formula: see text]), S-wave velocity ([Formula: see text]), and density ([Formula: see text]) of the earth’s subsurface. Generally, the seismic inversion problem is solved using one of the traditional optimization algorithms. These algorithms start with a given model and update the model at each iteration, following a physics-based rule. The algorithm is applied at each common depth point (CDP) independently to estimate the elastic parameters. Here, we have developed a technique using the convolutional neural network (CNN) to solve the same problem. We perform two critical steps to take advantage of the generalization capability of CNN and the physics to generate synthetic data for a meaningful representation of the subsurface. First, rather than using CNN as in a classification type of problem, which is the standard approach, we modified the CNN to solve a regression problem to estimate the elastic properties. Second, again unlike the conventional CNN, which is trained by supervised learning with predetermined label (elastic parameter) values, we use the physics of our forward problem to train the weights. There are two parts of the network: The first is the convolution network, which takes the input as seismic data to predict the elastic parameters, which is the desired intermediate result. In the second part of the network, we use wave-propagation physics and we use the output of the CNN to generate the predicted seismic data for comparison with the actual data and calculation of the error. This error between the true and predicted seismograms is then used to calculate gradients, and update the weights in the CNN. After the network is trained, only the first part of the network can be used to estimate elastic properties at remaining CDPs directly. We determine the application of physics-guided CNN on prestack and poststack inversion problems. To explain how the algorithm works, we examine it using a conventional CNN workflow without any physics guidance. We first implement the algorithm on a synthetic data set for prestack and poststack data and then apply it to a real data set from the Cana field. In all the training examples, we use a maximum of 20% of data. Our approach offers a distinct advantage over a conventional machine-learning approach in that we circumvent the need for labeled data sets for training.

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We build Convolutional Neural Networks (CNNs) to obtain petrophysical properties in the depth domain from pre-stack seismic data in the time domain. We compare two workflows – i) end-to-end CNN (PetroNet) – to directly predict petrophysical properties from prestack seismic data, and ii) cascaded CNNs with two CNN architectures – the first network (ElasticNet) to predict elastic properties from pre-stack seismic data followed by a second network (ElasticPetroNet) to predict petrophysical properties from elastic properties. Cascaded CNNs with more than twice the number of trainable parameters as compared to end-to-end CNN showed similar prediction performance for a synthetic dataset. The average correlation coefficient for test data between true and predicted clay volume (around 0.7) is higher than the average correlation coefficient between true and predicted porosity (around 0.6) for both the networks. The cascaded workflow depends on the availability of elastic properties and is three times more computationally expensive than the end-to-end workflow for training. Coherence plots between the true and predicted values for both cases show that the maximum coherence occurs for values of inverse wavenumber above 15 m, which is approximately equal to 1/4 the source wavelength or λ/4. The network predictions have some coherence with the true values even at a resolution of 10 m, which is half of the variogram range used in simulating the spatial correlation of the petrophysical properties. The Monte Carlo dropout technique is used for approximate quantification of the uncertainty of the network predictions. An application of the end-to-end network for prediction of petrophysical properties is made with the Stybarrow field located offshore Western Australia. The network made good predictions of petrophysical properties at the well locations. The network is particularly successful in identifying the reservoir facies of interest with high porosity and low clay volume.

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With an aim to enhance low temperature impact strength, blends of PP-cp (Impact Grade PP) and metallocene-catalyzed plastomer (EXACT® ethylene-α-octene copolymer) were prepared using a co-rotating, intermeshing twin-screw extruder in 90 : 10, 80 : 20, 70 : 30, and 60 : 40 weight ratio. Rheological properties studied by Haake’ single-screw extruder with torque rheometer attachment and capillary die showed pseudoplastic melt behavior at 220°C in the shear rate range of 400—4000 s—1. Density and MFI determinations showed minimal change. Morphology studied by low voltage scanning electron microscope (LVSEM) of blend samples showed distinct biphasic blend morphology wherein PP-cp as continuous phase and plastomer as spherical domains (0.5—2 μm size) with stabilized distribution and dispersion. Izod impact strength of the blends at varied temperatures (23, 0, -10, -20, -30, and -40°C) showed substantial enhancement in low temperature impact strength compared to the base polymer (from 44 J/m in case of pure PP-cp to 539 J/m in case of 40% plastomer blend at -40°C).