Author: EPIC Energy

Improving pseudo-optimal Kalman-gain localization using the random shuflle method

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In present days, Iterative ensemble smoothers (IES) are among the main methods to perform ensemble-based history matching in petroleum reservoirs. Generally, some localization technique is applied to the IES to pre- vent ensemble collapse, which is the consequence of an excessive reduction of the posterior ensemble variance. When the standard distance-based localization is applied, the assimilation of non-local parameters is difficult, and besides that, this kind of methodology has also several intrinsic parameters that need to be defined before the assimilation. In contrast, adaptive localization methods aim to overcome the noticed problems of distance-based localization, by using some statistical method to define the localization. This article proposes a novel adaptive localization scheme, on top of two preexisting techniques: pseudo-optimal and correlation-based localizations. The motivation here is to further improve the adaptive localization scheme, by combining the strengths of these two preexisting techniques. The efficacy of the proposed localization scheme is tested in one 2D and one 3D case studies, whereas the latter case study involves a field-scale reservoir model with both local and non-local pa- rameters, which often impose challenges on the conventional localization schemes. In comparison to other evaluated localization schemes, our results indicate that the proposed adaptive localization scheme achieves improved history matching performance.

 

4th EPIC Conference

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Update (Oct. 21, 2022): given the limited number of seats, registrations for the 4th EPIC Conference are now closed.

EPIC would like to invite graduate students, researchers and reservoir engineering professionals to the 4th. EPIC Conference, annual event to disseminate the center's ongoing research. The 4th. EPIC Conference will be on November 07th. to 09th 2022* at the main campus of the University of Campinas (UNICAMP), in Campinas – SP – Brazil.

The activities of the 4th EPIC Conference will occur in two venues:

For further information, check the Conference Program below or

Download the Conference Program (in PDF)

If you have any doubts, please contact us at epic@unicamp.br

* The short-course entitled “Electric Submersible Pumps (ESPs): Fundamentals, Failure Analysis & Reliability, and Emulsion as a Flow Assurance Issue” will have an extra sessin on November 10th. 2022, at 8:30 a.m.

Conference Program

Day 1: General Program
Day 1: Poster Session
Day 2: General Program
Day 2: Poster Session
Day 3: General Program

Short Course: Ensemble based Decision Making in Reservoir Management and Field Development Planning

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Short Course

Update (June 21st): instructions for the participants were sent to the e-mail address provided at registration. If you have not received this message, check your spam folder. For further inquiries, please reach us at epic@unicamp.br

EPIC invites graduate students, researchers, and reservoir engineering professionals to the short course entitled “Ensemble based Decision Making in Reservoir Management and Field Development Planning“, to be taught by Prof. Remus Gabriel Hanea, leading advisor on Reservoir Technology at Equinor (Norway), and Prof. Bogdan Sebacher, associated professor at the Military Technical Academy  of Bucharest (Romania).

The course will be offered in English, between June 27th and 29th, 2022, at the School of Mechanical Engineering (FEM) of the University of Campinas (UNICAMP), in Campinas, Brazil.

Prof. Remus Gabriel Hanea is the leading advisor on Reservoir Technology, Assisted History Matching and Optimization/Decision Making in the Subsurface Discipline Excellence department in Equinor. His background is in Applied Mathematics, and he has 15 years of experience in Uncertainty Quantification, Data Assimilation and Optimization, and Decision Making with applications in Atmospheric sciences, Hydrology and in various Energy domains (mainly O&G). He also has a part time professorship position at theUniversity of Stavanger (Norway), in the Department of Petroleum Engineering, in the group of Petroleum Geoscience Engineering. Remus’s main research topics are: Assisted History Matching and Robust Optimization for Reservoir Management, Value of Information, Decision and Risk Analysis and Geostatistics. Remus teaches a specialized course for PhD and MSc students on Inverse Modeling, Data Assimilation and Optimization with applications in Reservoir Management, and supervises MSc and PhD students.

Prof. Bogdan Sebacher is Associate Professor at the Military Technical Academy of Bucharest (Romania).

Course Syllabus

Day 1

(June 27th. 2022)

Introduction to Reservoir Management and Data Assimilation

Morning Session (9:00am to 12:00pm)

  • Introduction to Reservoir Management and Field Development Planning – basic notions
  • Introduction to Data Assimilation and Decision Making/Optimization – basic notions
  • Models, Data, Uncertainties
  • Bayes Theorem
  • Kalman Filter

Afternoon Session (2:00pm to 5:00pm)

  • From Ensemble Kalman Filter (EnKF) to Ensemble Smoother Multiple Data Assimilation (ES-MDA) – the journey
  • Pros and Cons and practical implementations (case studies)
  • Ensemble validation and analysis, Quality assurance and the definition of success

Day 2

(June 28th. 2022)

Facies modeling and estimation

Morning Session (9:00am to 12:00pm)

  • Pluri-Gaussian approach
  • Adaptive Pluri-Gaussian Simulation (APS)
  • Probability cubes for facies distributions – Seismic and log data information

Afternoon Session (2:00pm to 5:00pm)

  • Multipoint Geostatistics (MPS) approach
  • Parameterization and estimation
  • Machine learning approaches

Day 3

(June 29th. 2022)

Decision Making/Optimization under geological uncertainties

Morning Session (9:00am to 12:00pm)

  • Robust/Ensemble Optimization
  • Basic concepts
  • Ensemble and stochastic gradient optimization approaches
  • Practical Implementations
  • Way forward – Structured decision making approach

CCUS Course (2022)

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O Programa de Pós-graduação em Ciências e Engenharia de Petróleo da UNICAMP e o Energy Production Innovation Center (EPIC) gostariam de comunicar o oferecimento da disciplina PP590 “An Introduction to CCUS: Carbon Capture, Utilization and Storage“, que será aberta também a alunos de pós-graduação externos à UNICAMP. A disciplina terá início em 03/03/2022 e terá duração de 15 semanas, com aulas sempre às quintas-feiras das 9h00 às 12h00.

A disciplina tem como pré-requisitos graduação em ciências exatas ou tecnológicas e habilidades para comunicação em inglês (nível C1 ou superior). Mais informações podem ser obtidas no banner abaixo e as pré-inscrições devem ser feitas em https://forms.gle/N5obKKdjvjojb3yi6.

A matrícula no curso, para alunos regulares e pós-docs, deve ser realizada no período de alteração de matrícula (08 a 11/03). Para os alunos externos que tenham interesse, é necessário realizar a matrícula como aluno especial por meio deste link até o dia 08/03/2022.

Informações sobre o curso de CCUS

 

 

Evidence that wax deposition is a phase transition rather than a molecular diffusion phenomenon

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Over the past five decades, wax deposition has been widely considered a mass transfer-controlled phenomenon. Despite the highly inaccurate predictions, engineers cannot accurately predict the final thickness of the deposit, the hypothesis that wax deposition is a mass transfer phenomenon was not commonly questioned, but this has recently changed. This paper shows evidence that wax deposition is limited by phase transition (heat transfer), by analyzing a vast experimental matrix previously presented in the literature and clearly showing that the thickness decreases as the Reynolds number increases, which cannot be explained by molecular diffusion alone, also by showing that the Reynolds number does not influence the ratio between the deposit’s thermal resistance and the total thermal resistance (dimensionless temperature) for all cold flow experiments, which is further evidence of phase transition. When comparing the limits of the molecular diffusion approach with the experimental data, without any fitting parameter, one observes that not only the experimental data cannot be predicted, but the trend is also incorrect. When using the phase transition model (heat transfer), the accuracy in the thickness prediction is high, which is evidence that what limits the wax deposition is the phase transition. This shows that heat transfer equations can accurately predict wax deposition thickness. Since all wax deposition simulators have the heat transfer calculations, to improve their predictions, one must only implement a single boundary condition.

Critical Review on Wax Deposition in Single-Phase flow

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Wax deposition is a costly problem for the O&G industry, especially for pipelines in cold environments. For at least three decades, the scientific community has overwhelmingly agreed that molecular diffusion is the main mechanism for wax deposition. There are, however, severe problems with models based on molecular diffusion. They rely on untested hypotheses and several empirical correlations; hence, they can hardly predict the experimental data from laboratory. For real fields, the prediction is no better than an educated guess – heuristic solutions. Several research areas in wax deposition need to be better understood, and these are discussed in detail here, with a highlight to the most important concern: the controlling mechanism. Is wax deposition indeed a mass transfer controlled phenomenon? What is the evidence supporting this “general knowledge”? Is it possible that, for some conditions, mass transfer is dominant, and for others, the phase transition mechanism is dominant? Apart from this, we also discuss other issues: the accuracy of empirical correlations for diffusivity, the behavior of crystals in the deposit and how that influences the general deposit behavior, non-Newtonian influence on heat transfer and mass transfer, among others. Wax deposition is a complex topic that has been reviewed over and over. In this review, however, we focus on both presenting what has been discussed in the literature and make a critical analysis. The goal is to increase the general knowledge by highlighting a number of gaps and challenges related to this complex and financially exorbitant issue.

Selection of Representative Scenarios Using Multiple Simulation Outputs for Robust Well Placement Optimization in Greenfields

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In greenfield projects, robust well placement optimization under different scenarios of uncertainty technically requires hundreds to thousands of evaluations to be processed by a flow simulator. However, the simulation process for so many evaluations can be computationally expensive. Hence, simulation runs are generally applied over a small subset of scenarios called representative scenarios (RS) approximately showing the statistical features of the full ensemble. In this work, we evaluated two workflows for robust well placement optimization using the selection of (1) representative geostatistical realizations (RGR) under geological uncertainties (Workflow A), and (2) representative (simulation) models (RM) under the combination of geological and reservoir (dynamic) uncertainties (Workflow B). In both workflows, an existing RS selection technique was used by measuring the mismatches between the cumulative distribution
of multiple simulation outputs from the subset and the full ensemble. We applied the Iterative Discretized Latin Hypercube (IDLHC) to optimize the well placements using the RS sets selected from each workflow and maximizing the expected monetary value (EMV) as the objective function. We evaluated the workflows in terms of (1) representativeness of the RS in different production strategies, (2) quality of the defined robust strategies, and (3) computational costs. To obtain and validate the results, we employed the synthetic UNISIM-II-D-BO benchmark case with uncertain variables and the reference fine- grid model, UNISIM-II-R, which works as a real case. This work investigated the overall impacts of the robust well placement optimization workflows considering uncertain scenarios and application on the reference model. Additionally, we highlighted and evaluated the importance of geological and dynamic uncertainties in the RS selection for efficient robust well placement optimization.

A multi-scale mixed method for a two-phase flow in fractured reservoirs considering passive tracer

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In this research, the mathematical model represents a two-phase flow in a fractured porous reservoir media, where the Darcy law represents the flow in both fractures and matrix. The flux/pressure of the fluid flow is approximated using a hybridized mixed formulation coupling the fluid in the volume with the fluid flow through th fractures. The spatial dimension of the rock matrix is three and and is coupled with two-dimensional discrete frac- tures. The transport equation is approximated using a lower order finite volume system solved through an upwind scheme. The C++ computational implementation is made using the NeoPZ framework, an object oriented finite element library. The generation of the geometric meshes is done with the software Gmsh. Numerical simulations in 3D are presented demonstrating the advantages of the adopted numerical scheme and these approximations are compared with results of other methods.

A posteriori error estimates for primal hybrid finite element methods

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We present new fully computable a posteriori error estimates for the primal hybrid finite element methods based on equilibrated flux and potential reconstructions. The reconstructed potential is obtained from a local L2 orthogonal projection of the gradient of the numerical solution, with a boundary continuous restriction that comes from a smoothing process applied to the trace of the numerical solution over the mesh skeleton. The equilibrated flux is the solution of a local mixed form problem with a Neumann boundary condition given by the Lagrange multiplier of the hybrid finite element method solution. To establish the a posteriori estimates we divide the error into conforming and non-conforming parts. For the former one, a slight modification of the a posteriori error estimate proposed by Vohral ́ık [1] is applied, whilst the latter is bounded by the difference of the gradient of the numerical solution and the reconstructed potential. Numerical results performed in the environment PZ Devloo [2], show the efficiency of this strategy when it is applied for some test model problems.