Systems and control theory for increased oil recovery

Transcription

1 Systems and control theory for increased oil recovery Jan-Dirk Jansen, Delft University of Technology 1

2 Research & development drivers Increasing demand; reducing supply energy demand continues to grow world-wide renewables are developing too slow to keep up with demand easy oil has been found; few new discoveries; complex fields => produce more from existing reservoirs Increasing knowledge- and data intensity more sensors: pressure/temperature/flow, time-lapse seismic, passive seismic, EM, tracers, tilt meters, remote sensing, more control: multi-lateral wells, smart wells, snake wells, dragon wells, remotely controlled chokes, more modeling capacity: computing power, visualization => use a model-based systems and control approach 2

3 Closed-loop reservoir management Hypothesis: recovery can be significantly increased by changing reservoir management from a batch-type to a near-continuous model-based controlled activity Key elements: Optimization under geological uncertainties Data assimilation for frequent updating of system models Inspiration: Systems and control theory Meteorology and oceanography A.k.a. real-time reservoir management, quantitative reservoir management, integrated operations, smart fields, intelligent fields, 3

4 Closed-loop reservoir management Noise Input System (reservoir, wells & facilities) Output Noise Controllable input Optimization Sensors System models Geology, seismics, well logs, well tests, fluid properties, etc. Predicted output Model updating Measured output 4

5 12-well example 3D reservoir High-permeability channels 8 water injectors 4 oil producers Production period of 10 years 12 wells x 10 x 12 time steps gives 1440 optimization parameters Optimization of monetary value J Van Essen et al., 2006 J = (value of oil costs of water produced/injected) Cycle times: 4 years, 2 years, 1 year, 1 month Comparison: perfect knowledge; reactive control 5

6 12-well example 6

7 12-well example 7

8 Closed-loop effect of cycle time 1 month 1 year 2 years 4 years 10.5 x x NPV, $ Discounted water costs, $ Discounted oil revenues, $ open-loop reactive Hypothesis: recovery can be significantly increased by changing reservoir management from a batch-type to a near-continuous model-based controlled activity 8

9 Multi-level optimization Full cycle of assimilation and optimization too labor-intensive Optimal inputs difficult to implement in practice Cultural barriers hamper use of reservoir simulation in production environment Solution: multi-level control strategy (like in refineries) Outer loop: technical limit Inner loop: maximum production within constraints Noise u kk : N MPC controller yˆ 1: K Input y Optimization algorithms kk : N Predicted output System (reservoir, wells & facilities) Data-driven reservoir model x k Observer Physics-based reservoir simulation models CAHM algorithms Output y k States & parameters Sensors Measured output Noise 9

10 System-theoretical concepts System model input (p wf,q t ) state (p,s) parameters (k,, ) output (p wf,q w,q o ) Controllability of a dynamic system is the ability to influence the states through manipulation of the inputs. Observability of a dynamic system is the ability to determine the states through observation of the outputs. Identifiability of a dynamic system is the ability to determine the parameters from the input-output behavior. Well-defined theory for linear systems. More difficult for nonlinear ones. 10

11 System theory main findings so far Controllability, observability and identifiability are very limited Reservoir dynamics lives in a state space of a much smaller dimension than the number of model grid blocks For fixed wells: the (few) identifiable parameter patterns correspond just to the (few) controllable state patterns So, do we still need geology? 11

12 System theory main findings so far Yes, we very much need geology! 12

13 System theory main findings so far Yes, we very much need geology! Interpreting the history matched results requires geological insight Understanding optimization results also requires geological insight Well location-optimization requires a geological model However, we need to focus on the relevant geology: Which geological features are identifiable? Which geological features influence controllability? 13

14 Conclusions, questions, more work Specific optimization methods less important than workflow & human interpretation of results Use of multiple models to capture uncertainties is key Reservoir dynamics lives in low-order space so what? Control-relevant geology how do we define it? Combination with short-term production optimization essential to get buy-in from operations Big loop updating of geology essential to get buy-in from geoscientists Developments: well location/trajectory optimization, EOR optimization, structural uncertainties, multiple data sources (4-D seismics, gravity, EM, passive seismics, ), VOI, 14

15 Acknowledgments Collaborators: Okko Bosgra Arnold Heemink Paul Van den Hof and many other colleagues and students of TU Delft Department of Geoscience and Engineering TU Delft Delft Center for Systems and Control TU Delft Delft Institute for Applied Mathematics TU Eindhoven Department of Electrical Engineering TNO Built Environment and Geosciences Sponsors: Shell, ENI, Petrobras, Statoil 15