Determination of lower and upper bounds of predicted production from history-matched models

We present a method to determine lower and upper bounds to the predicted production or any other economic objective from history-matched reservoir models. The method consists of two steps: 1) performing a traditional computer-assisted history match of a reservoir model with the objective to minimize the mismatch between predicted and observed production data through adjusting the grid block permeability values of the model. 2) performing two optimization exercises to minimize and maximize an economic objective over the remaining field life, for a fixed production strategy, by manipulating the same grid block permeabilities, however without significantly changing the mismatch obtained under step 1. This is accomplished through a hierarchical optimization procedure that limits the solution space of a secondary optimization problem to the (approximate) null space of the primary optimization problem. We applied this procedure to two different reservoir models. We performed a history match based on synthetic data, starting from a uniform prior and using a gradient-based minimization procedure. After history matching, minimization and maximization of the net present value (NPV), using a fixed control strategy, were executed as secondary optimization problems by changing the model parameters while staying close to the null space of the primary optimization problem. In other words, we optimized the secondary objective functions, while requiring that optimality of the primary objective (a good history match) was preserved. This method therefore provides a way to quantify the economic consequences of the well-known problem that history matching is a strongly ill-posed problem. We also investigated how this method can be used as a means to assess the cost-effectiveness of acquiring different data types to reduce the uncertainty in the expected NPV.     

Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogotá, Colombia

Bogotá is located in the central Andean region of Colombia, which is frequently affected by landslide processes. These processes are mostly triggered during the rainy season in the city. This fact remarks the importance of determining what rain-derived parameters (e.g. intensity, antecedent rain, daily rain) are better related with the occurrence of landslides. For this purpose, the linear discriminant analysis (LDA), a technique derived from multivariate statistics, was used. The application of this type of analysis led to obtain simple mathematical functions that represent the probability of occurrence of landslides in Bogotá. The functions also allow to identify the most relevant variables derived from records of rainfall linked to the generation of landslides. A proof of concept using the proposed methodology was done using historic rainfall data from a 9-km² area of homogenous climatology and geomorphology in the south part of Bogotá. Landslides needed to be grouped for the LDA. Each one of these grouping categories represents landslides that occurred in similar geomorphologic conditions. Another set of events with no landslides was generated synthetically. Results of the proof of concept show that rainfall parameters such as normalized rainfall intensity I _MAP, normalized daily rainfall R _MAP and rainy-days normal RDN have the best statistical correlation with the landslides observed in the zone of analysis.     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

Mid and late Holocene forest fires and deforestation in the subalpine belt of the Iberian range,northern Spain

The conversion of subalpine forests into grasslands for pastoral use is a well-knownphenomenon, although for most mountain areas the timing of deforestation has not been determined. The presence of charcoal fragments in soil profiles affected by shallow landsliding enabled us to date the occurrence of fires and the periods of conversion ofsubalpine forest into grasslands in the Urbión Mountains, Iberian Range, Spain. We found that the treeline in the highest parts of the northwestern massifs of the Iberian Range(the Urbión, Demanda, Neila, and Cebollera massifs) is currently between 1500 and 1600 m a.s.l., probably because of pastoral use of the subalpine belt, whereas in the past it would have reached almost the highest divides(at approximately 2100–2200 m a.s.l.). The radiocarbon dates obtained indicate that the transformation of the subalpine belt occurred during the Late Neolithic, Chalcolithic, Bronze Age, Iron Age, and Middle Ages. Forest clearing was probably moderate during fires prior to the Middle Ages, as the small size of the sheep herds and the local character of the markets only required small clearings, and therefore more limited fires. Thus, it is likely that the forest recovered burnt areas in a few decades; this suggests the management of the forest and grasslands following a slash-andburn system. During the Middle and Modern Ages deforestation and grassland expansion affected most of the subalpine belt and coincided with the increasing prevalence of transhumance, as occurred in other mountains in the Iberian Peninsula(particularly the Pyrenees). Although the occurrence of shallow landslides following deforestation between the Neolithic and the Roman Period cannot be ruled out, the most extensive shallow landsliding processes would have occurred from the Middle Ages until recent times.     

Characterization of fresh and weathered petroleum for potential impacts to soil fertility

In many petroleum-producing regions, there are not adequate controls to prevent pipeline breaks and spills, and thus soil is frequently contaminated with petroleum hydrocarbons. Different petroleum oil compounds may produce negative impacts on soil fertility. In this study, four fresh crudes, a weathered petroleum, and oils from bioremediated and burned sites were investigated (specific gravities 0.83–1.27). Fourier transform infrared spectroscopy revealed three predominant polar functional groups to be more plentiful in the heavier crudes. The relative abundance of these groups was used to calculate an index that was directly correlated with specific gravity (R ² = 0.9960) and the percent of asphaltene plus (polars + resins) fractions in the oil (R ² = 0.9643). This index correlated exponentially to the water repellency caused by petroleum in an alluvial soil (R ² = 0.9928). Furthermore, extra-heavy oil at a concentration of 10,000 ppm, the maximum allowable oil concentration in the soil that is within regulatory norms in many US states and other countries, and with a specific gravity >1.002, showed severe water repellency. This study presents an alternative for determining soil remediation criteria based on the API gravity of the oil rather than the C-range of the hydrocarbon mixtures, simplifying analytical methods and systematically studying the interaction between the kinds of petroleum mixtures and potential impacts to soil fertility.