MAXIMIZING VALUE OF OIL AND GAS DEVELOPMENTS THROUGH RESERVOIR TO SURFACE INTEGRATED MODELING

Transcription

1 MAXIMIZING VALUE OF OIL AND GAS DEVELOPMENTS THROUGH RESERVOIR TO SURFACE INTEGRATED MODELING AND OPTIMIZATION EWO meeting, 19 th of October 2017 Dr R. Marmier, Total SA, Head of Integrated Asset Modeling entity

2 OBJECTIVES All Oil Companies have the same objectives MAXIMIZE PRODUCTION AND MINIMIZE COSTS From Exploration / Appraisal and Development phases (green field) to operation and end of life (brown field) Maximizing production and minimizing costs rapidly becomes a complex Optimization Problem requiring Reservoir to Surface Integrated Modeling - More and more complex developments (subsea, ultra-deep, long tieback, unconventional,...), environments (Arctic, ) and fluids (acid, viscous, waxy, ) - Under Controlled and Safe conditions 2

3 NEED FOR INTEGRATED MODELING AND OPTIMIZATION EXAMPLES KEY MESSAGES 3

4 NEED FOR INTEGRATED MODELING AND OPTIMIZATION EXAMPLE OF A FIELD DEVELOPMENT Process Network Wells Reservoir Source: Petroleum Experts HOW MAXIMIZE PRODUCTION AND MINIMIZE COSTS? 4

5 NEED FOR INTEGRATED MODELING AND OPTIMIZATION Iterative approach - Disciplines iteratively exchange fictive and approximated boundary conditions between each others Reservoir Well Network Process Costs All constraints? Consistent? Optimal solution? Reservoir to Surface Integrated Asset Modeling - Disciplines work together at Asset level Reservoir to Surface Integrated Asset Modeling 5

6 NEED FOR INTEGRATED MODELING AND OPTIMIZATION OPTIMIZATION EXAMPLE The production of an onshore oil field is delivered to a process facility where the gas is separated and compressed to the required pressure to join an existing gas transportation network. What is the optimum production rate to maximize production and minimize costs? Maximized Production & Minimized Costs FIELD OPTIMUM PRODUCTION SUM (SINGLE COMPONENT OPTIMUM PRODUCTION)

7 NEED FOR INTEGRATED MODELING AND OPTIMIZATION DEFINE BEST DEVELOPMENT CONCEPT 3 offshore oil discoveries: which optimum development concepts? - stand-alone? tie-back to field A or B?... Tie-back to field A 7

8 NEED FOR INTEGRATED MODELING AND OPTIMIZATION DEFINE BEST OPERATING SET POINTS Monitoring, production tests validations, alarming, virtual metering 8

9 NEED FOR INTEGRATED MODELING AND OPTIMIZATION COMMERCIAL TOOLS COLLABORATION WITH CMU KEY MESSAGES 9

10 COMMERCIAL TOOLS ONE APPROACH: PETEX SUITE APPROACH 10

11 INTEGRATION AND OPTIMIZATION PROCESSES PETEX SUITE APPROACH OPTIMIZATION PROCEDURES First level optimization: GAP - The solver solves mass balance equations through Reservoirs (Mbal and Decline curves), wells and Networks, accounting for pressure and temperature pressure drops - The optimizer (Non-Linear Programming, NLP) identifies the optimum controls setting (well head choke, ESP frequencies, compressor speeds, gas-lift rate, ) that maximizes the production of hydrocarbons whilst honoring physical constraints throughout the system. 11

12 INTEGRATION AND OPTIMIZATION PROCESSES PETEX SUITE APPROACH OPTIMIZATION PROCEDURES Second level Optimization: Resolve SLP optimizer - When connecting thrid party application for instance, a higher level optimizer is required. - The SLP (Sequential Linear Programming) optimizer When Resolve performs a SLP optimization process, it starts by making a single pass through the system which may include a GAP optimization to provide a starting point. After this, linear equations to describe the system response with respect to the objective function and constraint equations will be derived by perturbing each of the control variables. The linear optimizer will then be ran on the resulting set of linear equations to obtain a proposed new optimum 12

13 INTEGRATION AND OPTIMIZATION PROCESSES PETEX SUITE APPROACH OPTIMIZATION PROCEDURES Second level Optimization: Resolve GIRO optimizer - Most of the optimization problem involving routing (while not limited to them) also contain continuous variables (choke opening, gas-lift injection rates, ESP frequencies,. Due to the complexity to solve Mixed Integer Non-Convex Non-Linear Optimization Problems, the approach followed by Petex is to divide the problem into two Having a non linear optimizer dealing with the continuous variables and constraints Combined with an Integer Optimizer dealing with the global search of the optimum routing -> GIRO - Initial Population - Evaluating the initial population - Survival of the fittest population - Creating a new population (X-Over) - Loop 13

14 COMMERCIAL TOOLS PETEX SUITE APPROACH GLOBAL OPTIMIZATION EXAMPLE Field Description: Offshore Oil Field developed with 9 wells Problematic: It is required to increase the current oil production by accounting for process constrain (min 1300 psig export pressure). Process Entry Pressure Resolve -SLP Wellhead chokes GAP -NLP 14

15 COMMERCIAL TOOLS PETEX SUITE APPROACH GLOBAL OPTIMIZATION EXAMPLE Field Description: Offshore Oil Field developed with 8 wells Problematic: It is required to maximize the production. Pipeline routing Resolve - GIRO Compressor speed Resolve -SLP Wellhead chokes GAP -NLP 15

16 NEED FOR INTEGRATED MODELING AND OPTIMIZATION COMMERCIAL TOOLS COLLABORATION WITH CMU KEY MESSAGES 16

17 COLLABORATION WITH CMU Current successful collaboration with CMU on a reduced model - Multi period NLP optimization Most of commercial tools perform optimization at every time step - Bi-objective function optimization Most of commercial tools perform optimization on production - Benchmarking of different solvers - Stochastic optimization Most of commercial tools doesn t account for uncertain parameters 17

18 NEED FOR INTEGRATED MODELING AND OPTIMIZATION COMMERCIAL TOOLS COLLABORATION WITH CMU KEY MESSAGES 18

19 KEY MESSAGES Maximizing production and minimizing costs is a complex Optimization Problem requiring Integrated Modeling Developments (subsea, ultra-deep, long tie-back, unconventional,...), environments (Arctic, ), fluids (acid, viscous, waxy, ) and Models (Reservoir, Well, Network and Process) are getting more and more complex within a tight economical context From Exploration / Appraisal and Development phases (green field) to operation and end of life (brown field) Commercial tools exist requiring further Research and Developments for more powerful integration / optimization algorithms and tools 19

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