Data assimilation using bayesian filters and B-spline geological models

This paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir

Full Wave Form Inversion for Seismic Data

In seismic wave inversion, seismic waves are sent into the ground and then observed at many receiving points with the aim of producing high-resolution images of the geological underground details. The challenge presented by Saudi Aramco is to solve the inverse problem for multiple point sources on the full elastic wave equation, taking into account all frequencies for the best resolution. The state-of-the-art methods use optimisation to find the seismic properties of the rocks, such that when used as the coefficients of the equations of a model, the measurements are reproduced as closely as possible. This process requires regularisation if one is to avoid instability. The approach can produce a realistic image but does not account for uncertainty arising, in general, from the existence of many different patterns of properties that also reproduce the measurements. In the Study Group a formulation of the problem was developed, based upon the principles of Bayesian statistics. First the state-of-the-art optimisation method was shown to be a special case of the Bayesian formulation. This result immediately provides insight into the most appropriate regularisation methods. Then a practical implementation of a sequential sampling algorithm, using forms of the Ensemble Kalman Filter, was devised and explored

Comparison of extended and ensemble based Kalman filters with low and high resolution primitive equation ocean models

International audienceKalman filters are widely used for data assimilation into ocean models. The aim of this study is to discuss the relevance of these filters with high resolution ocean models. This was investigated through the comparison of two advanced Kalman filters: the singular evolutive extended Kalman (SEEK) filter and its ensemble-based variant, called SEIK filter. The two filters were implemented with the Princeton Ocean model (POM) considering a low spatial resolution configuration (Mediterranean sea model) and a very high one (Pagasitikos Gulf coastal model). It is shown that the two filters perform reasonably well when applied with the low resolution model. However, when the high resolution model is considered, the behavior of the SEEK filter seriously degrades because of strong model nonlinearities while the SEIK filter remains remarkably more stable. Based on the assumption of prior Gaussian distributions, the linear analysis step of the latter can still be improved though

Impacts of warming on phytoplankton abundance and phenology in a typical tropical marine ecosystem

In the tropics, thermal stratification (during warm conditions) may contribute to a shallowing of the mixed layer above the nutricline and a reduction in the transfer of nutrients to the surface lit-layer, ultimately limiting phytoplankton growth. Using remotely sensed observations and modelled datasets, we study such linkages in the northern Red Sea (NRS) - a typical tropical marine ecosystem. We assess the interannual variability (1998–2015) of both phytoplankton biomass and phenological indices (timing of bloom initiation, duration and termination) in relation to regional warming. We demonstrate that warmer conditions in the NRS are associated with substantially weaker winter phytoplankton blooms, which initiate later, terminate earlier and are shorter in their overall duration (~ 4 weeks). These alterations are directly linked with the strength of atmospheric forcing (air-sea heat fluxes) and vertical stratification (mixed layer depth [MLD]). The interannual variability of sea surface temperature (SST) is found to be a good indicator of phytoplankton abundance, but appears to be less important for predicting bloom timing. These findings suggest that future climate warming scenarios may have a two-fold impact on phytoplankton growth in tropical marine ecosystems: 1) a reduction in phytoplankton abundance and 2) alterations in the timing of seasonal phytoplankton blooms

Eastern Mediterranean biogeochemical flux model: simulations of the pelagic ecosystem

International audienceDuring the second phase (2003?2006) of the Mediterranean ocean Forecasting System Project (MFS) named Toward Environmental Predictions (MFSTEP) one of the three major aims was the development of numerical forecasting systems. In this context a generic Biochemical Flux Model (BFM) was developed and coupled with hydrodynamic models already operating at basin scale as well as at regional areas. In the Eastern Mediterranean basin the BFM was coupled with the Aegean Levantine Eddy Resolving MOdel (ALERMO). The BFM is a generic highly complex model based on ERSEM and although a detailed description of the model and its sub models is beyond the scope of this work a short presentation of the main processes, paying emphasis on the parameter values used is presented. Additionally the performance of the model is evaluated with some preliminary results being qualitatively compared against field observations. The model at its present form is rather promising reproducing all major important features even though there are inefficiencies mostly related to primary and bacterial productivity rates

Evaluating tropical phytoplankton phenology metrics using contemporary tools

The timing of phytoplankton growth (phenology) in tropical oceans is a crucial factor influencing the survival rates of higher trophic levels, food web structure and the functioning of coral reef ecosystems. Phytoplankton phenology is thus categorised as an ‘ecosystem indicator’, which can be utilised to assess ecosystem health in response to environmental and climatic perturbations. Ocean-colour remote sensing is currently the only technique providing global, long-term, synoptic estimates of phenology. However, due to limited available in situ datasets, studies dedicated to the validation of satellite-derived phenology metrics are sparse. The recent development of autonomous oceanographic observation platforms provides an opportunity to bridge this gap. Here, we use satellite-derived surface chlorophyll-a (Chl-a) observations, in conjunction with a Biogeochemical-Argo dataset, to assess the capability of remote sensing to estimate phytoplankton phenology metrics in the northern Red Sea – a typical tropical marine ecosystem. We find that phenology metrics derived from both contemporary platforms match with a high degree of precision (within the same 5-day period). The remotely-sensed surface signatures reflect the overall water column dynamics and successfully capture Chl-a variability related to convective mixing. Our findings offer important insights into the capability of remote sensing for monitoring food availability in tropical marine ecosystems, and support the use of satellite-derived phenology as an ecosystem indicator for marine management strategies in regions with limited data availability

Assessment of Red Sea temperatures in CMIP5 models for present and future climate

The increase of the temperature in the Red Sea basin due to global warming could have a large negative effect on its marine ecosystem. Consequently, there is a growing interest, from the scientific community and public organizations, in obtaining reliable projections of the Red Sea temperatures throughout the 21st century. However, the main tool used to do climate projections, the global climate models (GCM), may not be well suited for that relatively small region. In this work we assess the skills of the CMIP5 ensemble of GCMs in reproducing different aspects of the Red Sea 3D temperature variability. The results suggest that some of the GCMs are able to reproduce the present variability at large spatial scales with accuracy comparable to medium and high-resolution hindcasts. In general, the skills of the GCMs are better inside the Red Sea than outside, in the Gulf of Aden. Based on their performance, 8 of the original ensemble of 43 GCMs have been selected to project the temperature evolution of the basin. Bearing in mind the GCM limitations, this can be an useful benchmark once the high resolution projections are available. Those models project an averaged warming at the end of the century (2080–2100) of 3.3 ±> 0.6°C and 1.6 ±> 0.4°C at the surface under the scenarios RCP8.5 and RCP4.5, respectively. In the deeper layers the warming is projected to be smaller, reaching 2.2 ±> 0.5°C and 1.5 ±> 0.3°C at 300 m. The projected warming will largely overcome the natural multidecadal variability, which could induce temporary and moderate decrease of the temperatures but not enough to fully counteract it. We have also estimated how the rise of the mean temperature could modify the characteristics of the marine heatwaves in the region. The results show that the average length of the heatwaves would increase ~15 times and the intensity of the heatwaves ~4 times with respect to the present conditions under the scenario RCP8.5 (10 time and 3.6 times, respectively, under scenario RCP4.5).En prensa4,41

A singular value decomposition approach for detecting and delineating harmful algal blooms in the Red Sea

This is the final version. Available from Frontiers Media via the DOI in this record. Data availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Harmful algal blooms (HABs) have adverse effects on marine ecosystems. An effective approach for detecting, monitoring, and eventually predicting the occurrences of such events is required. By combining a singular value decomposition (SVD) approach and satellite remote sensing observations, we propose a remote sensing algorithm to detect and delineate species-specific HABs. We implemented and tested the proposed SVD algorithm to detect HABs associated with the mixed assemblages of different phytoplankton functional type (PFT) groupings in the Red Sea. The results were validated with concurrent in-situ data from surface samples, demonstrating that the SVD-model performs remarkably well at detecting and distinguishing HAB species in the Red Sea basin. The proposed SVD-model offers a cost-effective tool for implementing an automated remote-sensing monitoring system for detecting HAB species in the basin. Such a monitoring system could be used for predicting HAB outbreaks based on near real-time measurements, essential to support aquaculture industries, desalination plants, tourism, and public health.UK Research and InnovationPlymouth Marine Laboratory (PML)King Abdullah University of Science and Technolog