Seismic and sparse data integration through the use of direct sampling

textThe integration of seismic attributes and well data is an important step in the development of reservoir models. These models draw upon large data sets including information from well logs, production history, seismic interpretation, and depositional models. Modern integration techniques use the extensive data sets to develop precise models using complex workflows at increased cost of time and computational power. However, a gap exists in which a geostatistically driven procedure could integrate pattern statistics inferred from seismic images and those integrated from analogous geologic systems in order to develop spatially accurate reservoir models. Direct Sampling Seismic Integration Process, DSSIP, was first proposed by Henke and Srinivasan (2010) as an alternative to traditional seismic integration methods. The process provides a probabilistic mapping tool for fast reservoir analysis based on sparse conditioning data in a target reservoir and fully interpreted data from an analog field. DSSIP combines the structural information present in seismic data and facies patterns present in a training reservoir to create a fully realized output map for the target field. In this work, the basic DSSIP algorithm has been further optimized by performing a detailed parameter sensitivity study. The basic DSSIP algorithm has been demonstrated for a real field data set for a deepwater Gulf of Mexico reservoir. The basic DSSIP algorithm has also been analyzed to understand and model the effects of features such as salt canopy that can blur the seismic image. Finally, a modification to the basic algorithm is also presented that uses only a training model and the seismic data for the target reservoir in order to generate reservoir models for the target reservoir. This procedure eliminates the requirement to have a matching pair of training data sets for both the facies distribution and the corresponding seismic response.Petroleum and Geosystems Engineerin

Methodological approaches to determining the marine radiocarbon reservoir effect

The marine radiocarbon reservoir effect is an offset in 14C age between contemporaneous organisms from the terrestrial environment and organisms that derive their carbon from the marine environment. Quantification of this effect is of crucial importance for correct calibration of the <sup>14</sup>C ages of marine-influenced samples to the calendrical timescale. This is fundamental to the construction of archaeological and palaeoenvironmental chronologies when such samples are employed in <sup>14</sup>C analysis. Quantitative measurements of temporal variations in regional marine reservoir ages also have the potential to be used as a measure of process changes within Earth surface systems, due to their link with climatic and oceanic changes. The various approaches to quantification of the marine radiocarbon reservoir effect are assessed, focusing particularly on the North Atlantic Ocean. Currently, the global average marine reservoir age of surface waters, R(t), is c. 400 radiocarbon years; however, regional values deviate from this as a function of climate and oceanic circulation systems. These local deviations from R(t) are expressed as +R values. Hence, polar waters exhibit greater reservoir ages (δR = c. +400 to +800 <sup>14</sup>C y) than equatorial waters (δR = c. 0 <sup>14</sup>C y). Observed temporal variations in δR appear to reflect climatic and oceanographic changes. We assess three approaches to quantification of marine reservoir effects using known age samples (from museum collections), tephra isochrones (present onshore/offshore) and paired marine/terrestrial samples (from the same context in, for example, archaeological sites). The strengths and limitations of these approaches are evaluated using examples from the North Atlantic region. It is proposed that, with a suitable protocol, accelerator mass spectrometry (AMS) measurements on paired, short-lived, single entity marine and terrestrial samples from archaeological deposits is the most promising approach to constraining changes over at least the last 5 ky BP

Tracer Applications of Noble Gas Radionuclides in the Geosciences

The noble gas radionuclides, including 81Kr (half-life = 229,000 yr), 85Kr (11 yr), and 39Ar (269 yr), possess nearly ideal chemical and physical properties for studies of earth and environmental processes. Recent advances in Atom Trap Trace Analysis (ATTA), a laser-based atom counting method, have enabled routine measurements of the radiokrypton isotopes, as well as the demonstration of the ability to measure 39Ar in environmental samples. Here we provide an overview of the ATTA technique, and a survey of recent progress made in several laboratories worldwide. We review the application of noble gas radionuclides in the geosciences and discuss how ATTA can help advance these fields, specifically determination of groundwater residence times using 81Kr, 85Kr, and 39Ar; dating old glacial ice using 81Kr; and an 39Ar survey of the main water masses of the oceans, to study circulation pathways and estimate mean residence times. Other scientific questions involving deeper circulation of fluids in the Earth's crust and mantle also are within the scope of future applications. We conclude that the geoscience community would greatly benefit from an ATTA facility dedicated to this field, with instrumentation for routine measurements, as well as for research on further development of ATTA methods

Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields

Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an incrementally grown active set of features. In this paper, we address key computational bottlenecks that current incremental techniques still suffer by introducing best-choice edge grafting, an incremental, structured method that activates edges as groups of features in a streaming setting. The method uses a reservoir of edges that satisfy an activation condition, approximating the search for the optimal edge to activate. It also reorganizes the search space using search-history and structure heuristics. Experiments show a significant speedup for structure learning and a controllable trade-off between the speed and quality of learning

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

Chloride waters of Great Britain revisited: from subsea formation waters to onshore geothermal fluids

It has long been known that chloride-dominated saline ground waters occur at depth in the UK, not only beneath the sea but also onshore at depths of a few hundred metres. In a few places in northern England, these saline waters discharge naturally at surface in the form of springs. In recent years, however, these saline ground waters have come to be regarded as resources: as potential geothermal fluids intercepted in deep boreholes. Comparisons of the major ions and stable isotopes (δ2H, δ18O and δ34S) of these saline ground waters with North Sea oilfield formation waters, and with brines encountered in former subsea workings of coastal collieries, reveal that they are quite distinct from those found in North Sea oilfields, in that their as δ2H/δ18O signatures are distinctly “meteoric”. δ34S data preclude a significant input from evaporite dissolution – another contrast with many North Sea brines and some colliery waters. Yet, enigmatically, their total dissolved solids contents are far higher than typical meteoric waters. It is tentatively suggested that these paradoxical hydrogeochemical properties might be explained by recharge during Cenozoic uplift episodes, with high concentrations of solutes being derived by a combination of high-temperature rock–water interaction in the radiothermal granites and/or ‘freeze out’ from overlying permafrost that surely formed in this region during cold periods. Geothermometric calculations suggest these saline waters may well be representative of potentially valuable geothermal reservoirs