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In current industry practice, angle-averaged (stacked or migrated) P-wave reflection seismic images, approximating Pwave impedance changes, are used in time-lapse (4D) seismic analysis. Introduction of angle dependency in P-wave reflection data allows one to estimate both P-wave and S-wave impedance changes over the reservoir. Utilizing time-lapse Pwave and S-wave impedance, we show how to invert for multiple reservoir properties such as changes in fluid saturation and pressure. Estimates of saturation and pressure change are useful for integration of time-lapse seismic data with reservoir engineering models to improve reservoir fluidflow prediction and enhance reservoir management decisions. Introduction Time-lapse seismic is a procedure where a reservoir is imaged with reflected seismic energy at several time steps while being depleted1,2. This technology is successful when changes in dynamic reservoir properties, such as pressure, fluid saturation and temperature produce an observable change in the seismic impedance contrast of the medium in 3-D space3. A careful analysis combining rock physics measurements, reservoir simulation and forward seismic modeling is required to identify if changes in reservoir properties can produce seismically observable impedance changes, and if so, how they are related to dynamic fluid flow in the reservoir4. Here, we extend conventional post-stack time-lapse seismic analysis to pre-stack seismic data. With this extension we are able to estimate regions of separate saturation and pressure changes in an oil reservoir. Inversion for Pressure and Saturation The inverse problem we address here is to invert for changes in dynamic reservoir properties such as pressure and fluid saturation, given reflection angle dependent time-lapse seismic, or 4D AVO (amplitude variation with offset), data. Here, we consider the isothermal case. The first step in this procedure is to invert the AVO data for relative changes in P-wave and S-wave impedance5. A second optional step would be to use log data to obtain absolute P-wave and S-wave impedances from the relative changes. Finally, time-lapse changes in impedances are related to time-lapse changes in dynamic reservoir properties using impedance crossplotting6,7. We illustrate this method and discuss the principles in an example. Waterflood Example A synthetic data example consisting of a 3D spatially heterogeneous reservoir model based on actual field data from a Chevron field in the Gulf of Mexico is considered. Flow simulations were performed in this reservoir model to simulate water injection recovery. Fig. 1 shows the simulated water saturation difference and pore pressure difference between the start of injection time in Jan/1992, and four years later in Feb/1996. In this portion of the model, a single water injector is located in the upper left corner, and a single oil producer is located in the lower right corner. Note that reservoir heterogeneity in porosity and permeability has caused the injected water to preferentially channel along the left side of the model, on its path from the injector to the producer. The pressure is higher at the injector location, and lower at the producer where fluid is being withdrawn from the reservoir.

We show on synthetic and field data examples that joint pre-stack AVA inversion of PP- and PS-wave data can significantly improve estimation of P-impedance, S-impedance and density. For reservoir characterization, improvements in these parameters can better identify reservoir rock and fluid properties. For reservoir monitoring time-lapse (4D) changes in P-impedance, S-impedance and density can lead to inversion of saturation and pressure changes. We see that, in the joint inversion, 4D S-impedance is better estimated and not coupled to 4D P-impedance. These claims are first demonstrated on synthetic data, and then shown on an onshore unconventional play from Colorado and on offshore 4-D -4-C dataset from the North Sea. Joint inversion of PP- and PS-wave data requires a higher level of care compared to PP-waves since the two wave-modes need to be acquired, processed and merged properly. This has diminished the use of converted waves in the past. However, modern acquisition and processing on land and offshore data make this technology quantitatively more accurate and realizable. As such, we also provide best practices for a successful project. We shown that joint inversion can lead to a larger chance of success in placing exploration and development wells.

In 2017, distributed acoustic sensing (DAS) technology was deployed in a horizontal well to conduct a vertical seismic profiling survey before and after each of 78 hydraulic fracturing stages. From two vibroseis source locations at the surface, time shifts of P- and S-waves were observed but decayed over days. Some stages also showed waves scattered off the stimulated rock volume. We have used 2D finite difference elastic wavefield modeling to understand these observations and connect them to underlying properties of the stimulated rock. We have developed an effective medium model of vertical fractures that close exponentially with time as fluid leaks off into the formation can match the distribution of P- and S-wave time shifts along the well. This has enabled estimates of the height, normal and tangential fracture compliance values, and decay time of the stimulated rock volume. Additionally, the kinematics of scattered waves observed in the data have been found to be consistent with PS conversion across the stimulated rock volume from an individual stage. With higher quality DAS data, stage-by-stage inversion for height, fracture compliance, and decay time attributes may be possible for characterizing variations in the effectiveness of hydraulic fracturing.

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Distributed acoustic sensing is a growing technology that enables affordable downhole recording of strain wavefields from microseismic events with spatial sampling down to ∼1 m. Exploiting this high spatial information density motivates different detection approaches than typically used for downhole geophones. A new machine learning method using convolutional neural networks is described that operates on the full strain wavefield. The method is tested using data recorded in a horizontal observation well during hydraulic fracturing in the Eagle Ford Shale, Texas, and the results are compared to a surface geophone array that simultaneously recorded microseismic activity. The neural network was trained using synthetic microseismic events injected into real ambient noise, and it was applied to detect events in the remaining data. There were 535 detections found and no false positives. In general, the signalto-noise ratio of events recorded by distributed acoustic sensing was lower than the surface array and 368 of 933 surface array events were found. Despite this, 167 new events were found in distributed acoustic sensing data that had no detected counterpart in the surface array. These differences can be attributed to the different detection threshold that depends on both magnitude and distance to the optical fibre. As distributed acoustic sensing data quality continues to improve, neural networks offer many advantages for automated, real-time microseismic event detection, including low computational cost, minimal data pre-processing, low false trigger rates and continuous performance improvement as more training data are acquired.

Optimization of well spacings and completions are key topics in research related to the development of unconventional reservoirs. In 2017, a vertical seismic profiling (VSP) survey using fiber-optic-based distributed acoustic sensing (DAS) technology was acquired. The data include a series of VSP surveys taken before and immediately following the hydraulic fracturing of each of 78 stages. Scattered seismic waves associated with hydraulic fractures are observed in the seismic waveforms. Kinematic traveltime analysis and full-wavefield modeling results indicate these scattered events are converted PS-waves. We tested three different models of fracture-induced velocity inhomogeneities that can cause scattering of seismic waves: single hydraulic fracture, low-velocity zone, and tip diffractors. We compare the results with the field observations and conclude that the low-velocity zone model has the best fit for the data. In this model, the low-velocity zone represents a stimulated rock volume (SRV). We propose a new approach that uses PS-waves converted by SRV to estimate the half-height of the SRV and the closure time of hydraulic fractures. This active seismic source approach has the potential for cost-effective real-time monitoring of hydraulic fracturing operations and can provide critical constraints on the optimization of unconventional field development.