During the past decade, time-lapse seismic technology has been widely applied in hydrocarbon reservoir management. It is a very powerful method to obtain information on reservoir changes in the inter-well regions. This information helps to identify bypassed hydrocarbons and extend the economic life of a field. In a typical scenario, one baseline survey and subsequent monitoring surveys are acquired over time. The survey geometry is usually exactly repeated and well-sampled to mitigate acquisition effects on the next steps in the process. By processing and comparing all the datasets, some physical changes, e.g. reflection amplitude and travel-time changes, can be estimated. These time-lapse changes are then used to calculate interpretable parameter changes in dynamic reservoir rock and fluid properties, e.g. pore pressure and fluid saturation.
In a conventional time-lapse processing workflow, all the multiples are first removed from the data, then independent imaging process is employed to each dataset, given the same propagation velocity model. Later on, to compensate the ignored velocity variations between different surveys, a time-shift map (travel-time differences) is estimated from the calculated images and then applied back to them, yielding the final reflection amplitude differences. However, this conventional processing strategy is usually sensitive to the success of multiple removal and survey repeatability, and also requires well-sampled surveys providing proper illumination. Moreover, artifacts are often generated in addition to the actual time-lapse changes due to the non-repeatable uncertainties during the independent processing steps. Regarding the time-shift-map tool, the relative velocity changes derived from the time-shift map are not the actual velocity changes due to its local 1D subsurface assumption that is embedded.
In order to relax these rigid requirements and have a better velocity change indicator, we propose Simultaneous Joint Migration Inversion (S-JMI) as an effective time-lapse tool for reservoir monitoring, which combines a simultaneous time-lapse data processing strategy with the Joint Migration Inversion (JMI) method. JMI is a full wavefield inversion method that explains the measured reflection data using a parameterization in terms of reflectivities and propagation velocities. JMI is able to make use of multiples and at the same time take velocity variations between surveys into account. The simultaneous strategy, which means fitting all the datasets simultaneously, allows the baseline and monitor parameters to communicate and compensate with each other dynamically during inversion via L2-norm constraints, thus, reducing the non-repeatable uncertainties during the time-lapse processing workflow. As a result, more accurate time-lapse differences can be achieved by S-JMI, compared to inverting each dataset independently. Moreover, in order to get more localized time-lapse velocity differences, we further extend the regular S-JMI to a robust high-resolution S-JMI (HR-S-JMI) process by making a link between the reflectivity/reflectivity-difference and velocity/velocity-difference during inversion. With a complex synthetic example based on the Marmousi model, we demonstrate the performance of the time-shift-map-based method, sequential JMI, the regular S-JMI and HR-S-JMI is improving in this particular order.
Next, we further demonstrate the effectiveness of the proposed method in more real-life cases with a highly realistic synthetic model based on the Grane field, offshore Norway, and a time-lapse field dataset from the Troll Field. Moreover, in order to investigate the feasibility of HR-S-JMI in practice, several numerical experiments based on the realistic Grane model are conducted, regarding the following aspects: noise, including random noise and coherent noise caused by the acoustic assumption; the quality of time-lapse surveys, including sparse surveys, non-repeated surveys, and Ocean Bottom Node (OBN) vs streamer (different types of monitoring surveys); non-repeated sources, including source positioning errors and non-repeated source wavelets; spatial weighting operators in the L2-norm constraints; and sensitivity to weak time-lapse effects. These experiments show that HR-S-JMI is very robust to random noise, coherent noise, survey sparsity, survey non-repeatability, source positioning errors and source wavelet discrepancies. Furthermore, HR-S-JMI remains effective when the spatial weighting operators in the L2-norm constraints are largely relaxed and HR-S-JMI is capable of detecting weak time-lapse changes (e.g. velocity changes down to +/- 35 m/s). These features make it a suitable time-lapse processing solution for cost-effective (semi-)continuous monitoring, termed i4D survey technology, in which inexpensive localized and sparse surveys are employed between the conventional full-field surveys. The simultaneous strategy of S-JMI allows the full-field survey information to compensate the poor illumination of the in-between sparse surveys during process. Furthermore, calender-time constraints are proposed and applied to the parameter differences between the baseline and monitors along the calender-time axis by taking advantage of the feature that time-lapse effects usually develop gradually over time. With a complex synthetic example based on the Marmousi model, we demonstrate that S-JMI is a promising tool to process datasets acquired from (semi-)continuous monitoring, like an i4D survey.
In conclusion, we propose high-resolution simultaneous JMI (HR-S-JMI) as an effective time-lapse processing tool for the following main reasons:
• HR-S-JMI is able to make use of multiples to extend the illumination of the subsurface, instead of removing them;
• HR-S-JMI is an extended imaging process, including automatic velocity updating. Therefore, it takes velocity variations between surveys directly into account;
• HR-S-JMI is a good indicator of velocity changes, it can invert for high-resolution accurate time-lapse velocity changes;
• HR-S-JMI is robust to the uncertainties existing in the monitoring surveys, e.g. noise, sparsity, non-repeatability, source positioning errors, source wavelet discrepancy, etc;
• HR-S-JMI has the ability to detect weak time-lapse changes (velocity changes down to +/- 35 m/s)