First Break - Volume 41, Issue 3, 2023

A major underlying assumption of migration is that the input data are adequately sampled in terms of surface coverage. In addition, we hope that the subsurface is adequately illuminated, and that the migration algorithm itself is based on an acceptable numerical approximation of the wave equation. However, in general these assumptions and aspirations are never fully met, leading to amplitude imbalance and blurring of the output image. To some extent, this blurring and amplitude imbalance can be removed from the migrated data via application of some form of localised deconvolution, generally referred to as least-squares migration. This image modification can be performed in either the data or the image domain and can be achieved via an iterative or a single pass process, under the assumption that the velocity model is acceptable and that no coherent noise such as multiples, contaminates the input data. In this tutorial, we outline some of the various possible approaches to least-squares migration, and comment on the emerging ideas of adapting full waveform inversion to supplant some of the more expensive forms of least-squares image modification.

Should we try to improve surface monitoring array performance by deploying the sensors at the shallow borehole? We show a case where the surface sensor recorded higher signal-to-noise than the shallow borehole sensor. This improvement is limited to a frequency band between 20 Hz and 50 Hz and it is not caused by lack of coupling of the sensor in the shallow borehole. Our explanation is the resonance effect in the near-surface layers which improves detection of microseismic events.

We present 4D seismic inversion to reservoir pressure and saturation changes applied to data from the Catcher fields. The inversion workflow integrates data from reservoir simulation, well logs and production volumes, time-lapse time shifts and angle-stacked 4D seismic amplitudes as well as machine learning and Bayesian inversion methods. It begins with a petro-elastic model and reservoir pressure sensitivity calibration step, using well log data and time-lapse time-shifts. A machine learning inversion is then used to create an initial estimate of the reservoir property changes. This estimate is then used as prior information, in conjunction with reservoir simulation pressure data, for a stochastic Bayesian inversion workflow. We show that the Bayesian inversion benefits from the use of machine learning prior, leading to improvements in the match to the observed 4D seismic signal as well as the injected and produced water volumes. In addition to a most probable solution, stochastic sampling of the Bayesian posterior distribution also produces uncertainty estimates which are valuable in such inversion problems. With this result, we extract multiple equiprobable realisations and define conditional bounds for the pressure and saturation changes, which we recognise as helpful for reservoir management and 4D seismic data assimilation into reservoir simulation models.

The complexity of carbonate reservoirs is, in part, caused by the multiple rock-types and porosity types that may occur in any carbonate reservoir. Consequently, a persistent concern has been how to derive accurate quantitative estimates of carbonate reservoir properties from borehole and seismic data. Recent developments in this pursuit involve combined use of S-wave and P-wave data, together with petrophysical definitions of their impedances in a manner that lends them geologic character and significance. S-wave impedance and P-wave impedance are linearly related with their respective velocities. Both relationships are rock-typing criteria since their slope and intercept terms consist of geology-dependent coefficients. Linear-linear crossplots of S-wave impedance versus S-wave velocity and P-wave impedance versus P-wave velocity, using core scale measurements in four wells, support the linearity, pattern of data points, and character of the lines predicted for brine and air/gas pore fluids. The slopes and intercepts obtained by the method of linear regression analysis also show values that are location-specific, as well as exhibit variabilities that are indicative of reservoir heterogeneities from one well to another. These results show strong potential in using impedance-velocity relationships for quantitative interpretations of well logs and reflection seismic data.

4D seismic surveys are now routinely used to monitor hydrocarbon reservoirs. Because seismic reflections are sensitive to formation stress and fluid content, repeated seismic surveys are able to detect pressure changes and fluid exchanges that occur during field production. These measurements can help to optimise recovery strategy and identify areas where hydrocarbons have been bypassed. In practice, the seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. However, by applying a 4D seismic co-processing workflow, we were able to detect and quantify a 4D signal even with suboptimal repeated acquisition geometry. Seismic inversion helped to overcome noise, multiple contamination, and differences in dynamic amplitude range between baseline and monitor seismic surveys. Matching the 4D seismic signal to changes in reservoir production characteristics aided in the investigation of the mechanism underlying the observed 4D signal. Detectability of 4D signals was found to be primarily related to changes in reservoir pressure and fluid saturation, which increased from 2010 to 2020 the time-lapse between the base and monitor survey.

Monitoring of time-lapse changes in gravity has been carried out for decades, and measurement repeatability with relative spring gravimeters has in recent years improved to 0.6–2 µGal (standard deviation) for area surveys. This is better than often cited vendor specification. Gravimeters based on other physical principles offer high absolute accuracy or improved time-lapse sensitivity. All these may open possibilities for monitoring smaller and deeper subsurface changes and over shorter time intervals, with applications in hydrocarbon and geothermal production, gas and CO2 storage, hydrology, volcanology and more. Survey design and data processing are integral parts of a program. Sensor redundancy and repeat measurements provide opportunities for in-situ calibrations. An example from a gas accumulation shows a reduction in gravity of about 3 µGal and subsidence of 5–6 mm over 8 years. Both signals are clearly above the noise level and are likely to have been caused by production from a neighbouring structure. The information can quantify the amount of reservoir pore pressure drop, size of gas cap expansion and vertical movements of the gas-liquid contact to a precision well below 1 m.