Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

TIME LAPSE SEISMIC OBSERVATIONS AND EFFECTS OF RESERVOIR COMPRESSIBILITY AT TEAL SOUTH OIL FIELD

One of the original ocean-bottom time-lapse seismic studies was performed at the Teal South oil field in the Gulf of Mexico during the late 1990’s. This work reexamines some aspects of previous work using modern analysis techniques to provide improved quantitative interpretations. Using three-dimensional volume visualization of legacy data and the two phases of post-production time-lapse data, I provide additional insight into the fluid migration pathways and the pressure communication between different reservoirs, separated by faults. This work supports a conclusion from previous studies that production from one reservoir caused regional pressure decline that in turn resulted in liberation of gas from multiple surrounding unproduced reservoirs. I also provide an explanation for unusual time-lapse changes in amplitude-versus-offset (AVO) data related to the compaction of the producing reservoir which, in turn, changed an isotropic medium to an anisotropic medium. In the first part of this work, I examine regional changes in seismic response due to the production of oil and gas from one reservoir. The previous studies primarily used two post-production ocean-bottom surveys (Phase I and Phase II), and not the legacy streamer data, due to the unavailability of legacy prestack data and very different acquisition parameters. In order to incorporate the legacy data in the present study, all three poststack data sets were cross-equalized and examined using instantaneous amplitude and energy volumes. This approach appears quite effective and helps to suppress changes unrelated to production while emphasizing those large-amplitude changes that are related to production in this noisy (by current standards) suite of data. I examine the multiple data sets first by using the instantaneous amplitude and energy attributes, and then also examine specific apparent time-lapse changes through direct comparisons of seismic traces. In so doing, I identify time-delays that, when corrected for, indicate water encroachment at the base of the producing reservoir. I also identify specific sites of leakage from various unproduced reservoirs, the result of regional pressure blowdown as explained in previous studies; those earlier studies, however, were unable to identify direct evidence of fluid movement. Of particular interest is the identification of one site where oil apparently leaked from one reservoir into a “new” reservoir that did not originally contain oil, but was ideally suited as a trap for fluids leaking from the neighboring spill-point. With continued pressure drop, oil in the new reservoir increased as more oil entered into the reservoir and expanded, liberating gas from solution. Because of the limited volume available for oil and gas in that temporary trap, oil and gas also escaped from it into the surrounding formation. I also note that some of the reservoirs demonstrate time-lapse changes only in the “gas cap” and not in the oil zone, even though gas must be coming out of solution everywhere in the reservoir. This is explained by interplay between pore-fluid modulus reduction by gas saturation decrease and dry-frame modulus increase by frame stiffening. In the second part of this work, I examine various rock-physics models in an attempt to quantitatively account for frame-stiffening that results from reduced pore-fluid pressure in the producing reservoir, searching for a model that would predict the unusual AVO features observed in the time-lapse prestack and stacked data at Teal South. While several rock-physics models are successful at predicting the time-lapse response for initial production, most fail to match the observations for continued production between Phase I and Phase II. Because the reservoir was initially overpressured and unconsolidated, reservoir compaction was likely significant, and is probably accomplished largely by uniaxial strain in the vertical direction; this implies that an anisotropic model may be required. Using Walton’s model for anisotropic unconsolidated sand, I successfully model the time-lapse changes for all phases of production. This observation may be of interest for application to other unconsolidated overpressured reservoirs under production.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.