An unsupervised deep-learning method for porosity estimation based on poststack seismic data

We propose to invert reservoir porosity from poststack seismic data using an innovative approach based on deep-learning methods. We develop an unsupervised approach to circumvent the requirement of large volumes of labeled data sets for a conventional learning process. We apply convolutional neural networks (CNN) on seismic data to predict the relative porosity that is to be added to a low-frequency prior component. We then apply a forward model to synthesize seismic data based on a source wavelet and an acoustic impedance converted from the network-determined porosity. The parameters in the CNN are iteratively updated to minimize the error between recorded and simulated seismic data. We test the capability of our deep-learning approach to estimate reservoir porosity using a synthetic rock-physics model with two different signal-to-noise ratios. We also apply the proposed method to a real case study of seismic data acquired for hydrocarbon exploration of clastic reservoirs in the Vienna Basin. Instead of randomly assigning neural parameters, we use pretrained weights and biases at a previous location as initialization values for the next location, to preserve the geologically lateral continuity of the layers’ physical properties. As shown by these analyses, the unsupervised CNN-based scheme provides more or equally accurate results than standard methods for porosity estimation from seismically inverted acoustic impedance, which makes it a promising tool in seismic reservoir characterization with less user intervention.

Acid-based cement slurry with controllable properties

Intensive occurrence of sand related problems in wells diminishes oil flow rate and leads to heavy expenses on production and equipment maintenance. Hard geological factors on field bedding, heterogeneity of reservoir porosity and permeability, strict constraints on physicochemical property of oil and produced water restrict the efficient application of several available methods and technologies for sand control. The increasing densification of sand related problems at late stages of development gives rise to implementation of different type of workover. There exist several backfilling compositions with a number of draw-backs to control sand influx from the reservoir into the well. With the purpose to work out more effective technology to ensure the consolidation of reservoir there was developed a new grouting mortar. This slurry contains cement, hydrated aluminum silicate and 7-8% hydrochloric acid solution. Barrier of grouting mortar has high resistance and adhesive characteristics and penetrates much deeper into pores increasing consolidation efficiency.