Efficient, concurrent Bayesian analysis of full waveform LaDAR data

Bayesian analysis of full waveform laser detection and ranging (LaDAR) signals using reversible jump Markov chain Monte Carlo (RJMCMC) algorithms have shown higher estimation accuracy, resolution and sensitivity to detect weak signatures for 3D surface profiling, and construct multiple layer images with varying number of surface returns. However, it is computational expensive. Although parallel computing has the potential to reduce both the processing time and the requirement for persistent memory storage, parallelizing the serial sampling procedure in RJMCMC is a significant challenge in both statistical and computing domains. While several strategies have been developed for Markov chain Monte Carlo (MCMC) parallelization, these are usually restricted to fixed dimensional parameter estimates, and not obviously applicable to RJMCMC for varying dimensional signal analysis. In the statistical domain, we propose an effective, concurrent RJMCMC algorithm, state space decomposition RJMCMC (SSD-RJMCMC), which divides the entire state space into groups and assign to each an independent RJMCMC chain with restricted variation of model dimensions. It intrinsically has a parallel structure, a form of model-level parallelization. Applying the convergence diagnostic, we can adaptively assess the convergence of the Markov chain on-the-fly and so dynamically terminate the chain generation. Evaluations on both synthetic and real data demonstrate that the concurrent chains have shorter convergence length and hence improved sampling efficiency. Parallel exploration of the candidate models, in conjunction with an error detection and correction scheme, improves the reliability of surface detection. By adaptively generating a complimentary MCMC sequence for the determined model, it enhances the accuracy for surface profiling. In the computing domain, we develop a data parallel SSD-RJMCMC (DP SSD-RJMCMCU) to achieve efficient parallel implementation on a distributed computer cluster. Adding data-level parallelization on top of the model-level parallelization, it formalizes a task queue and introduces an automatic scheduler for dynamic task allocation. These two strategies successfully diminish the load imbalance that occurred in SSD-RJMCMC. Thanks to the coarse granularity, the processors communicate at a very low frequency. The MPIbased implementation on a Beowulf cluster demonstrates that compared with RJMCMC, DP SSD-RJMCMCU has further reduced problem size and computation complexity. Therefore, it can achieve a super linear speedup if the number of data segments and processors are chosen wisely

Closing the loop by engineering consistent 4D seismic to simulator inversion

The multi-disciplinary nature of closing the loop (CtL) between 4D seismic and reservoir engineering data requires integrated workflows to make sense of these different measurements. According to the published literatures, this integration is subject to significant inconsistency and uncertainty. To resolve this, an engineering consistent (EC) concept is proposed that favours an orderly workflow to modelling and inverting the 4D seismic response. Establishing such consistency facilitates a quantitative comparison between the reservoir model and the acquired 4D seismic data observation. With respect to the sim2seis workflow developed by Amini (2014), a corresponding inverse solution is proposed. The inversion, called seis2sim, utilises the model prediction as a priori information, searching for EC seismic answers in the joint domain between reservoir engineering and geophysics. Driven by a Bayesian algorithm, the inversion delivers more stable and certain elastic parameters upon application of the EC constraints. The seis2sim approach is firstly tested with a synthetic example derived from a real dataset before being applied to the Heidrun and Girassol field datasets. The two real data examples are distinctive from each other in terms of seismic quality, geological nature and production activities. After extracting the 3D and 4D impedance from the seismic data, CtL workflows are designed to update various aspects of the reservoir model according to the comparison between sim2seis and seis2sim. The discrepancy revealed by this cross-domain comparison is informative for robust updating of the reservoir model in terms reservoir geometry, volumetrics and connectivity. After applying tailored CtL workflows to the Heidrun and Girassol datasets, the statistical istributions of petrophysical parameters, such as porosity and NTG, as well as intra- and inter-connectivity for reservoir compartments are revised accordingly. Consequently, the 3D and 4D seismic responses of the reservoir models are assimilated with the observations, while the production match to the historical data is also improved . Overall, the proposed seis2sim and CtL workflows show a progression in the quantitative updating of the reservoir models using time-lapse seismic data