ENHANCED OIL RECOVERY: RESEARCHES OF LASG …fapesp.br/eventos/2017/statoil/PDF/10-Prof-Marcio-Sampaio_13h15.pdf · History Matching using EnKF ... Development of efficient optimization

POLYTECHNIC SCHOOL OF THE UNIVERSITY OF SAO PAULO

MINING AND PETROLEUM ENGINEERING DEPARTMENT

Prof. PhD Marcio Augusto Sampaio Pinto

Polytechnic School of the University of Sao Paulo

Mining and Petroleum Engineering Department

Coronel Narciso de Andrade, Sq, w/n, Vila Mathias

CEP: 11013-560, Santos-SP, Brazil

www.pmi.poli.usp.br

ENHANCED OIL RECOVERY:

RESEARCHES OF LASG-POLI

Outline

Presentation

EOR with Chemical Methods

EOR with CO2-WAG Injection

Recovery with Intelligent Wells

History Matching using EnKF

Partners

Acknowledgement

Outline

Presentation





Partners

Acknowledgement

Bachelor in Physics at UNICAMP – 2005

Master in Electrical Engineering at UNICAMP – 2007

PhD in Petroleum Science and Engineering at UNICAMP – 2013

Post-doc in Petroleum Engineering at UNICAMP – 2014

Adjunct Professor at Santa Catarina State University – 2014/2015

Assistant Professor at Polytechnic School of the USP – since 2015

Presentation

Personal Presentation:

Presentation

Laboratory of Petroleum Reservoir Simulation and Management (LASG)

• Created in 2016 with the support of the Direction of the Polytechnic School;

• It was built with 100% of FAPESP resources;

• Located in Santos, Sao Paulo;

• Room with 32 m2:

• 16 individual computer tables;

• 1 meeting table;

• 1 projector and projection screen;

Presentation

Master students:

Lab Team:

Undergraduation students:

Outline

Presentation





Partners

Acknowledgement

Sweep Efficiency:

M =𝜆𝑤𝜆𝑜

=𝑘𝑤/𝜇𝑤𝑘𝑜/𝜇𝑜

=𝑘𝑤 𝜇𝑜𝑘𝑜 𝜇𝑤

≈ 1

Decrease 𝜇𝑜

Increase 𝜇𝑤

Thermal methods:• Hot water injection;• Steam injection;• In-situ combustion;• SAGD;

Chemical methods:• Polymer Injection;• Alkaline-Surfactant-Polymer (ASP);• Alkaline-Surfactant-Foam (ASF);


M > 1 unfavorable conditionM < 1 favorable condition

Offshore Reservoirs with heavy oil:

Thermal methods:

Hot water and steam injection;

Steam Assisted Gravity Drainage (SAGD )

Difficulties:

Away from the coast;

Loss of heat until reach the reservoir;

Steam plant in the platform high cost and much space;


Alternative: recovery by chemical methods;

Polymer Flooding;

Alkaline-Surfactant-Polymer (ASP);

Alkaline-Surfactant-Foam (ASF);

The success of a chemical flooding requires finding the correct slug characteristics for the unique conditions of each reservoir;


Polymer flooding is indicated when:

Heavy oil;

High permeability avoid excessive retention of polymers;

Low temperature avoid polymers degradation;

Horizontal wells: ensure higher injectivity;

Water with low salinity avoid polymers degradation (except for Xanthan);

Extra heavy oil high concentration of polymers high cost;


Polymer Flooding:

Student Project (undergraduation):

Building a synthetic reservoir model: with some characteristics of Peregrino;

Modeling physical phenomena:

Adsorption, degradation, retention, etc;

Economic evaluation: considering costs of polymer injection;

Optimization of main variables: pre-flush, start of polymer injection, polymer concentration, well rates, water salinity;

Maximization of NPV of the field;


Polymer Flooding:

Student Project (master):

Building a synthetic reservoir model: with some characteristics of Peregrino;

Modeling physical phenomena;

Development of efficient optimization workflow with SPSA;

Geological uncertainty: models with different heterogeneities;

Economic uncertainty: different economic scenarios;

Maximization of Expected Monetary Value (EMV);


Displacement Efficiency:

It is related to capillary number (Nc):

𝑁𝑐 =𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦 𝑓𝑜𝑟𝑐𝑒𝑠

𝑐𝑎𝑝𝑖𝑙𝑙𝑎𝑟𝑦 𝑓𝑜𝑟𝑐𝑒𝑠=

ν 𝜇𝑖𝑛𝑗

𝜎𝑤𝑜

A larger capillary number results in a smaller residual oil saturation

We also need to decrease the interfacial tension


ν: Darcy velocity𝜇𝑖𝑛𝑗: displacing fluid viscosity

𝜎𝑤𝑜: interfacial tension between the displaced and displacing fluids

Alkaline-Surfactant-Polymer (ASP):

Surfactant reduce the interfacial tension;

increases the displacement efficiency;

Polymer decrease the mobility ratio;

increases the sweep efficiency;

ASP solution lower cost;

Can alter the rock wettability interesting for pre-salt!

API > 20;



Alkaline-Surfactant-Polymer (ASP):


Laboratory Characterization;

Critical micelle concentration determination (Surface Tension);

Aqueous stability and microemulsion phase behavior;

Rheology and viscosity tests for polymer selection;

Surfactant adsorption (Langmuir model);

Reservoir Simulation;

Use of a synthetic carbonate reservoir model;

Maximization of NPV considering the costs of chemicals used;

Alkaline-Surfactant-Foam (ASF):

Can be injected:

High permeability contrast zones;

Foam injectionmobility control fluid;

Not problem with degradation such as polymers;


Outline

Presentation





Partners

Acknowledgement


FAPESP Project: Optimization of Improved Oil Recovery

through CO2-WAG Injection

Partnership:

USP

UNICAMP

Texas A&M


Introduction

Pre-salt reservoirs: high concentration of CO2 (5 to 20%);

Difficulties:

Distance from the coast (~300 km): make impossible the flow through

pipelines;

Impossibility to release of the gas in the atmosphere;


Motivation

Lack of methodologies to quantify the increase of the NPV of these fields under

this type of injection;

Applied in Lula field by Petrobras since 2011;

Necessity to assess quickly and efficiently, increasing the reservoir recovery with

-CO2-WAG injection;

Increased efficiency: above the minimum miscibility pressure (MMP);


MotivationCombination of advantages of water and CO2 injections:

Water Injection: increase of macroscopic sweep efficiency;

CO2 Injection: greater efficiency in the microscopic displacement (miscible

conditions);

decreasing the interfacial tension

between oil and gas phases

Recovery efficiency is the product of both


Challenges

Operation of many wells involves a large number of control variables

in the optimization process;

High processing time of compositional simulation increases significantly the

time spent on the process;


Methodology

Phase 1: Development of a black-oil research simulator (In progress)

First step to develop the compositional simulator;

Three-dimensional;

Multiphase Flow;

Formulation: IMPES (Implicit Pressure Explicit Saturation);

Language: Matlab;


Methodology

Phase 2: Development of a compositional research simulator (expected)

In order to integrate the reduced order model ;

Decrease the simulation time;

Modeling the physical phenomena;

Adsorption, diffusion, solubility and relative permeability hysteresis;

Formulation: IMPEC (Implicit Pressure Explicit Concentration)


Methodology Proposed

Phase 3: Application of reduction order model POD-DEIM (expected)

Reduce computational time without losing the quality of results;

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

days

Wa

ter

Cu

t


SPE Reservoir Simulation Symposium

Houston, Texas, USA, 2015


Methodology Proposed

Phase 4: Application of optimization method called simultaneous perturbation with stochastic approximation (SPSA) (expected)

Obtaining a more efficient production scenario;

Method has showed to be more efficient than other global methods:

Genetic algorithms;

Simulated annealing;


Expected Results

Get a robust and efficient optimization method;

Reduce the compositional simulation time, integrating with the reduction order

model:

Acceleration in computational time of 1 to 2 orders of magnitude;

Assess the incremental recovery with CO2-WAG injections optimized;

Evaluate the increase of NPV of the field;

Student Project (undergratuation):

Building a synthetic reservoir model;


Adsorption, diffusion and solubility;

Economic evaluation: costs of CO2 reinjection;

Optimization of main variables: WAG cycle, WAG ratio, RGO limit, WCUT limit, volume of gas reinjection;



Student Project (undergratuation):

Building a synthetic reservoir model;


Adsorption, diffusion and solubility;

Economic evaluation: costs of CO2 reinjection;

Optimization of main variables: WAG cycle, WAG ratio, RGO limit, WCUT limit, volume of gas reinjection;

Experimental Design;

Response Surface;



Proxy Model

Outline

Presentation





Partners

Acknowledgement

Pre-salt reservoirs are very

heterogeneous application of intelligent

completion becomes recommended;

Introduction

Research line It aims to study the

optimum application conditions and control

of valves;


Can generate:

profit;

oil recovery;

water rate (or anothers fluids);

well intervention (high cost);

project risk: Economic

Geological

Operations


More expensive completion

necessary to estimate the gains

Investment in the begining

return

during the production


Many control variables

high computational time

Complexity of problem

difficult to use the traditional

optimization methods

Difficulties:Global Maximum


Type of valves Operation Control

Scenarios

Difficulties:


Goals:

Development of efficient and robust optimization method;

Optimization of number, placement and control valves;

Optimization under uncertainty: economic, geological andtechinical;

Decision analysis considering uncertainties:

economic feasibility of valves in the well;


Optimization of control valves:

Proactive 2:

Decrease of water rate;

Increase of oil rate;

Highlight: reduction of waterrate at the end of production;


Closing of valves

Deterministic Scenario

increase of Np and NPV

decrease Wp

proactive controls: better in order to maximize the NPV


Well

Oil Production (106 std m3)

Water Production (106 std m3)

Water Injection

(106 std m3)

NPV (US$

millions)

ΔNPV (US$

millions)Conventional 28.25 70.10 107.91 532.73 0

IW - Reactive 28.28 70.06 107.92 533.32 0.59

IW – Proactive 1 28.43 69.91 107.98 543.88 10.15

IW – Proactive 2 28.27 68.93 106.77 544.45 11.73

Decision Analysis considering Uncertainties

Reactive: ~75% of chance of loss;

Proactive 1: ~80% of chance ofpositive return;

Proactive 2: ~97% of chance ofpositive return;

Increase of expected returnDecrease of risk;




Smart Field in EOR

Additional variables in theoptimization process;

Additional time due tocompositional simulation;

Outline

Presentation





Partners

Acknowledgement

Ensemble Kalman Filter – EnKF

Efficient algorithm for the assimilation of data observed in non-linear problems;

History Matching

Static and dynamic parameters can be used to adjust the model;

Steps:

• Initial sampling;

• Assimilation;

• Forecast through reservoir simulator;

History Matching with EnKF

Ensemble Kalman Filter – EnKF

Advantages:

• Computationally efficient;

• Easy implementation;

• It can predict uncertainty in the future performance of the reservoir;

• It can also be used in the assimilation of seismic data;


Source: Sintef


Initial application: five-spot configuration;

History matching with EnKF of the Norne field ;

Development of algorithm;

How we can do the automatic history matching?

Why some applications work very well and others fail?


Outline

Presentation





Partners

Acknowledgement

Internal Partners:

Partners

Profa. Dra. Carina UlsenMining Engineer, PhD in Mining Engineering

Characterization and Properties of Reservoirs

Prof. Dr. Henrique KahnGeologist, PhD in Mining Engineering

Characterization of Rocks

Prof. Dr. Rafael dos Santos GioriaMechanical Engineer, PhD in Mechanical Engineering

Fluid Dynamics Simulation

Prof. Dr. Caetano MirandaPhysicist, PhD in Science

Pore Scale Simulation

Prof. Dr. Cleyton de Carvalho CarneiroGeologist, PhD in Geosciences

Geotechnologies, Multivariate Analysis

Prof. Dr. Jean Vicente FerrariChemist, PhD in Science

Petrochemical and Characterization of Corrosive Environments

Prof. Dr. Marcio Sampaio

Prof. Dr. Denis Schiozer Prof. Dr. Eduardo Gildin

Partners

External Partners:

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Partners

Acknowledgement

Acknowledgement

Thanks!

Contact: [email protected]

LABORATORY OF PETROLEUM RESERVOIR

SIMULATION AND MANAGEMENT

www.lasg.poli.usp.br

http://www.lasg.poli.usp.br/

Documents

ENHANCED OIL RECOVERY: RESEARCHES OF LASG …fapesp.br/eventos/2017/statoil/PDF/10-Prof-Marcio-Sampaio_13h15.pdf · History Matching using EnKF ... Development of efficient optimization