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
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
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
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
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);
EOR with Chemical Methods
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;
EOR with Chemical Methods
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;
EOR with Chemical Methods
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;
EOR with Chemical Methods
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;
EOR with Chemical Methods
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);
EOR with Chemical Methods
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
EOR with Chemical Methods
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;
EOR with Chemical Methods
EOR with Chemical Methods
Alkaline-Surfactant-Polymer (ASP):
Student Project (master):
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;
EOR with Chemical Methods
Outline
Presentation
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
Partners
Acknowledgement
EOR with CO2-WAG Injection
FAPESP Project: Optimization of Improved Oil Recovery
through CO2-WAG Injection
Partnership:
USP
UNICAMP
Texas A&M
EOR with CO2-WAG Injection
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;
EOR with CO2-WAG Injection
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);
EOR with CO2-WAG Injection
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
EOR with CO2-WAG Injection
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;
EOR with CO2-WAG Injection
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;
EOR with CO2-WAG Injection
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)
EOR with CO2-WAG Injection
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
EOR with CO2-WAG Injection
SPE Reservoir Simulation Symposium
Houston, Texas, USA, 2015
EOR with CO2-WAG Injection
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;
EOR with CO2-WAG Injection
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;
Modeling physical phenomena:
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;
Maximization of NPV of the field;
EOR with CO2-WAG Injection
Student Project (undergratuation):
Building a synthetic reservoir model;
Modeling physical phenomena:
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;
Maximization of NPV of the field;
EOR with CO2-WAG Injection
Proxy Model
Outline
Presentation
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
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;
Recovery with Intelligent Wells
Can generate:
profit;
oil recovery;
water rate (or anothers fluids);
well intervention (high cost);
project risk: Economic
Geological
Operations
Recovery with Intelligent Wells
More expensive completion
necessary to estimate the gains
Investment in the begining
return
during the production
Recovery with Intelligent Wells
Many control variables
high computational time
Complexity of problem
difficult to use the traditional
optimization methods
Difficulties:Global Maximum
Recovery with Intelligent Wells
Type of valves Operation Control
Scenarios
Difficulties:
Recovery with Intelligent Wells
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;
Recovery with Intelligent Wells
Optimization of control valves:
Proactive 2:
Decrease of water rate;
Increase of oil rate;
Highlight: reduction of waterrate at the end of production;
Recovery with Intelligent Wells
Closing of valves
Deterministic Scenario
increase of Np and NPV
decrease Wp
proactive controls: better in order to maximize the NPV
Recovery with Intelligent Wells
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;
Recovery with Intelligent Wells
Recovery with Intelligent Wells
Recovery with Intelligent Wells
Smart Field in EOR
Additional variables in theoptimization process;
Additional time due tocompositional simulation;
Outline
Presentation
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
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;
History Matching with EnKF
Source: Sintef
Student Project (master):
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?
History Matching with EnKF
Outline
Presentation
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
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:
Outline
Presentation
EOR with Chemical Methods
EOR with CO2-WAG Injection
Recovery with Intelligent Wells
History Matching using EnKF
Partners
Acknowledgement
Acknowledgement
Thanks!
Contact: [email protected]
LABORATORY OF PETROLEUM RESERVOIR
SIMULATION AND MANAGEMENT
www.lasg.poli.usp.br