A B S T R A C TWe introduce a method to detect and compensate for inconsistent coupling conditions that arise during onshore seismic data acquisitions. The reflected seismic signals, the surface waves, or the ambient-noise records can be used for the evaluation of the different coupling conditions of closely spaced geophones. We derive frequency-dependent correction operators using a parametric approach based upon a simple model of the interaction between geophone and soil. The redundancy of the measurements available permits verification of the assumptions made on the input signals in order to derive the method and to assess the validity of the model used. The method requires point-receiver data in which the signals recorded by the individual geophones are digitized.We have verified the accuracy of the method by applying it to multicomponent ambient-noise records acquired during a field experiment in which the coupling conditions were controlled and modified during different phases of the experiment. We also applied the method to field data, which were acquired without the coupling conditions being controlled, and found that only a few geophones showed an anomalous behaviour. It was also found that the length of the noise records routinely acquired during commercial surveys is too short to provide enough statistics for the application of our method.

Fluid imaging technologies are used in a wide range of E&P applications. Among geophysical methods, electromagnetics (EM) determines subsurface resistivities and thus responds to fluid changes. On the path to zero carbon footprint, the most significant potential for EM lies in monitoring geothermal, carbon capture, utilization and storage (CCUS), and enhancing oil recovery (EOR). To optimize reservoir fluid monitoring, we calibrate surface measurements to well logs resulting in a 3D anisotropic model consistent with borehole data. This is done before and after depletion or injection to estimate a time-lapse reservoir response. As part of a carbon capture and storage project, we carried out baseline measurements and validated the surface EM data to the 3D anisotropic borehole model. The monitoring workflow for this project can easily be adapted for other applications to support the energy transition. From this, we learned that measurement accuracy requirements higher than 1 % because we are often imaging small anomalies. While there are always limits in acquisition set by industrial noise, we derived two ways of increasing the anomaly. One is by using, similar to a borehole focused logs, focusing methods in the acquisition setup. This is still subject to measurement accuracy limitations and limited to electric fields only. Another way is to add borehole sensors that increase the sensitivity by around a factor of 10. While shallow (around 50 m) is sufficient, they can be extended to deeper borehole sensors, bringing the measurements close to the anomaly and is thus the preferred approach. This, in combination with calibration back to the 3D anisotropic borehole log allows you to certify the data for its information content. This will give you quantifiable ways to derive risk values and significantly reduce acquisition and monitoring operations cost.

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Understanding the behavior of CO2 injected into a reservoir and delineating its spatial distribution are fundamentally important in enhanced oil recovery (EOR) and CO2 capture and sequestration activities. Interdisciplinary geoscience collaboration and well-defined workflows, from data acquisition to reservoir simulation, are needed to effectively handle the challenges of EOR fields and envisioned future commercial-scale sites for planned and incidental geologic CO2 storage. Success of operations depends on decisions that are based on good understanding of geologic formation heterogeneities and fluid and pressure movements in the reservoir over large areas over time. We present a series of workflow steps that optimize the use of available data to improve and integrate the interpretation of facies, injection, and production effects in an EOR application. First, we construct a simulation-to-seismic model supported by rock physics to model the seismic signal and signal quality needed for 4D monitoring of fluid and pressure changes. Then we use Bayesian techniques to invert the baseline and monitor seismic data sets for facies and impedances. To achieve a balance between prior understanding of the reservoir and the recorded time-lapse seismic data, we invert the seismic data sets by using multiple approaches. We first invert the seismic data sets independently, exploring sensible parameter scenarios. With the resulting realizations, we develop a shared prior model to link the reservoir facies geometry between seismic vintages upon inversion. Then we utilize multirealization analysis methods to quantify the uncertainties of our predictions. Next, we show how data may be more deeply interrogated by using the facies inversion method to invert prestack seismic differences directly for production effects. Finally, we show and discuss the feedback loop for updating the static and dynamic reservoir simulation model to highlight the integration of geophysical and engineering data within a single model.

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