A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

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

Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

This work presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness.Este trabalho apresenta um método de otimização prático e eficaz para o projeto de redes de produção submarinas em campos de petróleo offshore, o que compreende o número de coletores, sejam manifolds ou plataformas, sua localização, atribuição de poços a esses coletores e diâmetro de dutos que interligam todos os elementos da rede. Ele traz uma solução rápida que pode ser facilmente implementada como uma ferramenta para otimização de layout e de estudos baseados em simulação. O modelo proposto compreende a dinâmica do reservatório e fluxo multifásico em dutos, baseando-se na linearização multidimensional por partes para formular o problema de otimização de layout como programação inteira linear mista. Além da validação da solução ótima obtida pelo método, a simulação de reservatórios define limites para as variáveis e parâmetros do modelo que caracterizam a perda de carga, a dinâmica do reservatório e a produção de óleo dos poços ao longo do tempo. A perda de carga nas tubulações é modelada por funções lineares por partes que aproximam resultados obtidos pelos simuladores de fluxo multifásicos. O modelo de otimização foi aplicado a um verdadeiro campo de petróleo offshore com o objetivo de avaliar sua efetividade

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved

An optimization framework for the integration of water management and shale gas supply chain design

This study presents the mathematical formulation and implementation of a comprehensive optimization framework for the assessment of shale gas resources. The framework simultaneously integrates water management and the design and planning of the shale gas supply chain, from the shale formation to final product demand centers and from fresh water supply for hydraulic fracturing to water injection and/or disposal. The framework also addresses some issues regarding wastewater quality, i.e., total dissolved solids (TDS) concentration, as well as spatial and temporal variations in gas composition, features that typically arise in exploiting shale formations. In addition, the proposed framework also considers the integration of different modeling, simulation and optimization tools that are commonly used in the energy sector to evaluate the technical and economic viability of new energy sources. Finally, the capabilities of the proposed framework are illustrated through two case studies (A and B) involving 5 well-pads operating with constant and variable gas composition, respectively. The effects of the modeling of variable TDS concentration in the produced wastewater is also addressed in case study B

Exploring flexible strategies in engineering systems using screening models : applications to offshore petroleum projects

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, February 2009."December 2008." Cataloged from PDF version of thesis.Includes bibliographical references (p. 290-297).Engineering Systems, such as offshore petroleum exploration and production systems, generally require a significant amount of capital investment under various technical and market uncertainties. Choosing appropriate designs and field development strategies is a very challenging task for decision makers because they need to integrate information from multiple disciplines to make decisions while the various uncertainties are still evolving. Traditional engineering practice often focuses on finding "the optimal" solution under deterministic assumptions very early in the conceptual study phase, which leaves a large amount of opportunity unexploited, particularly the value of flexible strategies. This thesis proposes a new approach to tackle this issue - exploring flexible strategies using midfidelity screening models. The screening models interconnect and model physical systems, project development, and economics quantitatively at the mid-fidelity level, which allows decision-makers to explore different strategies with significantly less computational effort compared to high fidelity models. The screening models are at a level of detail that gives reliable rank orders of different strategies under realistic assumptions. Flexibilities are identified and classified at strategic, tactical, and operational levels over a system's lifecycle. Intelligent decision rules will then exercise flexible strategies as uncertainties unfold. This approach can be applied as a "front-end" strategic tool to conduct virtual experiments. This helps identify good strategies from a large number of possibilities and then discipline-based tools can be used for detailed engineering design and economics evaluation.(cont.) The present study implemented the use of such screening models for petroleum exploration and production projects. Through two simulation case studies, this thesis illustrates that flexible strategies can significantly improve a project's Expected Net Present Value (ENPV), mitigate downside risks, and capture upside opportunities. As shown in the flexible tieback oilfield development case study, the simulations predicted a 82% improvement of ENPV by enabling architectural and operational flexibility. The distributions of outcomes for different strategies are shown in terms of Value-at-Risk-Gain curves. This thesis develops and demonstrates a generic four-step process and a simulation framework for screening flexible strategies with multi-domain uncertainty for capital-intensive engineering systems.by Jijun Lin.Ph.D