The applications of neural network in mapping, modeling and change detection using remotely sensed data

Thesis (Ph.D.)--Boston UniversityAdvances in remote sensing and associated capabilities are expected to proceed in a number of ways in the era of the Earth Observing System (EOS). More complex multitemporal, multi-source data sets will become available, requiring more sophisticated analysis methods. This research explores the applications of artificial neural networks in land-cover mapping, forward and inverse canopy modeling and change detection. For land-cover mapping a multi-layer feed-forward neural network produced 89% classification accuracy using a single band of multi-angle data from the Advanced Solidstate Array Spectroradiometer (ASAS). The principal results include the following: directional radiance measurements contain much useful information for discrimination among land-cover classes; the combination of multi-angle and multi-spectral data improves the overall classification accuracy compared with a single multi-angle band; and neural networks can successfully learn class discrimination from directional data or multi-domain data. Forward canopy modeling shows that a multi-layer feed-forward neural network is able to predict the bidirectional reflectance distribution function (BRDF) of different canopy sites with 90% accuracy. Analysis of the signal captured by the network indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model shows that the R2 between the network-predicted canopy parameters and the actual canopy parameters is 0.85 for canopy density and 0.75 for both the crown shape and the height parameters. [TRUNCATED

Excess water production diagnosis in oil fields using ensemble classifiers

In hydrocarbon production, more often than not, oil is produced commingled with water. As long as the water production rate is below the economic level of water/oil ratio (WOR), no water shutoff treatment is needed. Problems arise when water production rate exceeds the WOR economic level, producing no or little oil with it. Oil and gas companies set aside a lot of resources for implementing strategies to effectively manage the production of the excessive water to minimize the environmental and economic impact of the produced water.However, due to lack of proper diagnostic techniques, the water shutoff technologies are not always proficiently applied. Most of the conventional techniques used for water diagnosis are only capable of identifying the existence of excess water and cannot pinpoint the exact type and cause of the water production. A common industrial practice is to monitor the trend of changes in WOR against time to identify two types of WPMs, namely coning and channelling. Although, in specific scenarios this approach may give reasonable results, it has been demonstrated that the WOR plots are not general and there are deficiencies in the current usage of these plots.Stepping away from traditional approach, we extracted predictive data points from plots of WOR against the oil recovery factor. We considered three different scenarios of pre-water production, post-water production with static reservoir characteristics and postwater without static reservoir characteristics for investigation. Next, we used tree-based ensemble classifiers to integrate the extracted data points with a range of basic reservoir characteristics and to unleash the predictive information hidden in the integrated data. Interpretability of the generated ensemble classifiers were improved by constructing a new dataset smeared from the original dataset, and generating a depictive tree for each ensemble using a combination of the new and original datasets. To generate the depictive tree we used a new class of tree classifiers called logistic model tree (LMT). LMT combines the linear logistic regression with the classification algorithm to overcome the disadvantages associated with either method.Our results show high prediction accuracy rates of at least 90%, 93% and 82% for the three considered scenarios and easy to implement workflow. Adoption of this methodology would lead to accurate and timely management of water production saving oil and gas companies considerable time and money

Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

Integrated optimization of scale inhibitor squeeze treatment

Scale inhibitor (SI) squeeze treatment is one of the most widely adopted techniques to control scale deposition. In this technique, chemical scale inhibitor is injected into the near-wellbore area where it retains in the formation and then slowly releases in the produced water when the well is back in production, preventing scale formation at a concentration of few ppm. The injection process normally starts with a preflush to condition the rock, and then the main slug (containing the SI) is injected followed by an overflush to push the chemical further deep into the reservoir. Before production, a shut-in period is also considered for more SI retention in the formation. The aim of chemical inhibition is to delay the deposition kinetics so that scaling issues are deferred from subsurface to surface, where a much easier access allows easier handling of the deposition risk. The main goal of this thesis is to present an integrated study of how to optimize the squeeze treatment design based on the well conditions and by considering the operational constraints. SI concentration, main treatment volume and the overflush volume are considered for squeeze design optimization. Using the sensitivity study, the optimum inhibitor concentration in the main slug is identified. The sensitivity results show that the most efficient squeeze treatment is achieved when the SI is deployed with the highest possible concentration, given the formation damage issues are avoided. In most cases, the well is normally planned to be protected for a target lifetime, this will result in protecting the well until the next treatment becomes available. The squeeze lifetime function was shown to be differentiable against the squeeze parameters, hence a gradient-based optimization algorithm, specifically Gradient Descent (GD) algorithm was applied to optimize the main treatment and the overflush volume for a given target squeeze lifetime. This will result in identifying the squeeze “Iso-Lifetime” curve, which presents all the possible squeeze designs that provide the target lifetime, using the optimum SI concentration. Based on the iso-lifetime designs, a cost analysis was carried out to find the optimum treatment, where the CPB (total cost of squeeze per barrel of water protected during the production period) was minimized, and the design with the lowest CPB was selected as the optimum one. For the cases with some flexibility in treatment lifetime, the same approach as described above was employed for a range of target lifetimes to identify the optimum target. The target lifetime that demonstrates the minimum CPB was identified as the optimum target lifetime which can be considered to optimize the treatment in a single well for long-term. Using this procedure, the optimum long-term strategy for squeeze treatment in a case study was provided. Multi-well squeeze design optimization was also studied in this thesis. Multi-well cases may include scenarios such as treating two or more wells connected to a subsea manifold or treating several single wells in the field where several wells of the same field are treated simultaneously in a squeeze campaign. A supply vessel is normally used to deliver the SI to the wells in a single trip. Due to the limitation of storage capacity on the vessel, the amount of inhibitor which can be used is limited, hence the available amount of inhibitor onboard should be optimally distributed among the wells. The squeeze campaign design was optimized for two field cases, minimizing the total inhibitor volume and the total downtime/pumping time, using the Multi-Objective Particle Swarm Optimization (MOPSO) method. Once the wells are squeezed, they should all reach the target lifetime of the campaign. This is essential such that all wells are protected until the next campaign. Finally, the Pareto Front was identified for the field, including the optimum squeeze campaign designs with the minimum cost, leading to the optimum inhibitor allocation strategy. The associated uncertainties with squeeze optimization were also considered in optimization. These uncertainties are mainly related to the retention isotherm which is normally derived by history matching. There might be several isotherms resulting in a reasonable history match. This causes uncertainty in squeeze lifetime prediction. Uncertainty quantification is considered in this research by evaluating the P10/P50/P90 percentiles using the likelihood function. Finally, scale treatment optimization in geothermal reservoirs was investigated and the most efficient scale treatment strategy in a geothermal doublet system was identified by considering three different techniques of SI deployment: continuous injection downhole, squeeze treatment and batch injection in the injector well. Optimum design for each of the methods was studied considering different reservoir conditions, and the optimization results were compared, providing the best scale treatment strategy in the reservoir

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality