a hybrid data mining approach for automated reservoir surveillance

Transcription

1 a hybrid data mining approach for automated reservoir surveillance Michael Stundner, Decision Team - Software FORCE Seminar, October 2002

2 why automated reservoir surveillance? Problems with data overload and data delivery Inability to deliver the proper data in a timely fashion Integration of vast quantities of real-time, eposodic and static data is not automated Emerging technologies and new working processes are needed for better decisions faster

3 reservoir surveillance tools should... anticipate reservoir performance mechanisms which will likely deviate identify any discrepancies in performance as early as possible provide information regarding the cause of these deviations... use all data available to identify these discrepancies support decision making

4 objectives for a reservoir management system Accelerated production through permanent well control Better understanding of reservoir dynamics Faster decisions for production optimization sustainable work process optimization increased productivity of knowledge workers data handling modeling/reporting decision support 10% 15% 30% 60% 75% 10%

5 data mining a data driven modeling technique Automated (self-learning) analysis of large data sets to find patterns and trends that might otherwise be undiscovered. Real-time reservoir surveillance data allow data-driven modeling. Data Mining includes Artificial Intelligence tools as Neural Networks. hybrid approach Fixed (law of gravity) Parametric (reservoir simulation, well and network models, MB) User-driven (Decline Curve Analysis) Data-driven (Neural Networks) + user (knowledge) dependency + data dependency

6 data mining vs. deterministic modeling Deterministic models require more knowledge Data Mining models require more data Production data represent the response of the reservoir due to production strategy Combination of both techniques is best Surveillance Data Deterministic Modeling predictive Data mining Modeling Reservoir Parameters Fluid Behavior trial-and-error history matching = many steps + time-consuming work for Adjusting the reservoir model Automatic history matching = one step

7 decide! for oil&gas e&p data mining software manually edited sensors IWC settings historical data DECIDE! Knowledge Management Module DECIDE! Reservoir Surveillance Module + visualisation DECIDE! Desktop Modules + Data Mining methods DECIDE! Database Server Knowledge Base prepare model detection control + data preparation + data preprocessing + rate allocation + well-to-well interaction + reservoir modeling + event + notification system + exception reporting + process control + process check + production DECIDE! optimization Control DECIDE! Module Control Module + optimization + MBI DECIDE! Clients simulation models well models network models economic models Smart Wells data > information > knowledge > decision

8 field operations hierarchy Business Headquarters Optimization Level Surveillance Data Modelling Tools Capacity Planning Design (months/years) Operational Planning (weeks/months) Field Optimization Improve Recovery Factor, Determine Infill Locations 4D Seismic Reservoir Simulation, MBI, BPNN Scheduling (hours/weeks) Supervisory Control (minutes/hours) Regulatory Control (second/minutes) Well & Surface facilities Production Optimization Accelerate Production, Optimize Gas Lift and Water Injection Allocation Operation Optimization Q, p, T in Wells and Separators 4D Seismic, Microseismic, DTS, Downhole and Wellhead gauges, Sand Control, Multiphase Flow Meters, Surface Facilities Material Balance w/ Interference (MBI), Decline Curve Analysis, SOM, BPNN, Well & Flowline Simulation, Genetic Algorithms Self-Organizing Map (SOM), Back Propagation Neural Networks (BPNN)

9 implementation of an automated system control Decision Making users Event Detection Knowledge Management Reservoir and well models Surface network and Process models Economic models Data Historian realtime data export cleansed and aggregated data DECIDE! FOR OIL&GAS Knowledge Base historical data DECIDE! Reservoir Surveillance Module DECIDE! Database Server Data Preprocessing DECIDE! Desktop Modules Production Database Oracle EXCEL spreadsheets

10 surveillance data are brought to manageable time increments secs. 1 min. 15 min. 1 hrs. 1 day 1 month Sensor Data (Field Sample Rate) ,600 86,400 2,613, Aggregated Data (Reservoir Engineering Sample Rate) well rate estimation well-to-well interaction Time increment for Data Mining Models Manually recorded data 0 Time in seconds ,000 10, ,000 1,000,000 10,000,000

11 data are aggregated after automatic data preparation Handling Parameter Data QC Noise Aggre- Missing Assignment Reduction gation Values Data Preparation Data Preparation Data Preparation Data at field sample rate DECIDE! Production Database Data Preprocessing Data Preprocessing Data Preprocessing Manual Input Filtering Data Pattern Data Space Detrending Generation Extraction Cleansing Reduction Data at Reservoir Engineering sample rate DATA PREPARATION Rule based (expert system) Data Quality Control and Parameter Assignment of raw sensor data Alarm System and exception reporting for sensor metering Filtering and and moving average for reducing noise in data Neural Network models serve as multivariate and unbiased estimators for filling gaps (missing values) Prepared data are aggregated based to the user s requirements for meaningful sample rates in the DECIDE! Production Database Manual Data Cleansing is provided for final corrections DATA PREPROCESSING Filtering and Detrending techniques allow to subdivide datasets into various components Dependent parameters can be generated from sensor data on-the-fly Statistical methods, Neural Networks and a Pattern Extractor tool prepare the data for DECIDE! s data driven modules

12 neural networks provide reliable rate allocation Neural Networks are trained on well test data. The trained model is used to predict the oil and water production for the time between the well tests. lift gas rate THP choke size BHFP temperature oil production

13 well rate control text message WAP Apply "Automation Rules" to trigger DECIDE! reports, text messaging, s, web portal, etc. Apply "Control Rules" to optimize well control, e.g. choke settings, gas lift rate. Any time increment possible. raw Reservoir Surveillance data Apply Data Preparation Models Apply "Data Preparation Rules" to cleanse data and split sensor data uínto several parameters, e.g. flowing and shutin pressures Data Aggregation (e.g. 15 mins) Apply "Event Detection Rules" to give alarms in the notification system. E.g. if actual well rate is less than 80% of expected trend value then give red alert. These events then trigger "Control and Automation Rules". Any time increment possible. DECIDE! Production Database save calulated well rate compare well rate with expected trend, e.g., Decline Curve Analysis Run trained well rate estimation models lift gas rate THP choke size oil production BHFP temperature every 15 minutes or may be aggregated to any other time range, e.g. last 24 hrs. e.g. every 15 minutes

14 well-to-well communication situation and opportunity Inj_6 Prod_1 5 wells were supposed to be perforated in one channel sand Individual well production data should be used to evaluate possible communication between these wells Prod_2 Inj_11 Prod_5

15 well-to-well communication approach injected water per time increment Inj_11 CW11 Inj_6 WW06 injection pressure Inj_11 CW11 Inj_6 WW06 roduced total fluid per time increment Prod_1 CP01_A Prod_5 CP05_E Prod_2 WP02 ViewNet bottom hole flowing pressure Prod_1 CP01_A Prod_5 CP05_E A well-to-well interaction model was built where BHFP of two producing wells were used as model output. The pressure support relationship accounts for a great deal of the interaction between wells. Input Hidden Output

16 well-to-well communication neural network model Actual vs. Predict CP05_E Prod_5 bottom hole flowing pressure Train Actual Train Predicted Test Actual Test Predicted Training of the neural network model showed relationship between input and output Sensitivity analysis is needed for interpretation of results (black box) # o f p a t t e r n s

17 well-to-well communication results sensitivity analysis neural network prediction Output Channel: CP05_E Neural Network result: Prod_5 bottom hole flowing pressure Inj_11 -/+4000bbl Inj_6 -/+4000bbl Prod_ bbl bbl -2000bbl Prod_ bbl -2000bbl Prod_2 -/+2000bbl prediction time step

18 material balance with interference (mbi) dozens of compartments possible automatic history matching oil, gas, water interference vs. time new calculation concept avoids shortcomings of conventional techniques matched models can be run in realtime to evaluate incoming data MBI is therefore the best compromise beween the simplicity of MB models and the complexity and accuracy of numerical simulation models to make interactive and intelligent reservoir management available

19 water influx through faults in the world s largest off-shore field Situation abnormal increase in watercut in crestal wells of the upper reservoir occurred this could not be explained by edge water drive Opportunity water encroachment from the lower to the upper reservoir should be evaluated determine fluid migration through the conductive faults as a function of the pressure difference

20 a 42-compartment model was set up Approach MBI models with first 9 and finally 42- compartments were set up and matched oil and water migration between all these compartments was determined fluid migration due to conductive faults could be quantified

21 interference strength between reservoirs can be calculated Approach (cont.) A pressure match in the Material Balance model could only be obtained with a certain amount of water migration between the two reservoirs taken into account Gained value Results from the Material Balance model are used as a preprocessing tool for a full-scale reservoir simulation model Dynamic fluid flow processes in the reservoir are quantified. Time-savings in setting-up the simulation model

22 neural network model to predict and optimize field production Situation Numerical simulation model failed (highly fractured reservoir) Unreliable production data due to wrong rate allocation factors Opportunity prediction of field production from reservoir surveillance data Approach WHP and gas lift served as substitue for production rates in a single neural network

23 the trained model can be used for optimization Gained value reliable, shortterm predictions possible from production history optimization of water injection pattern and gas lift rates with trained neural network field model optimization/constraints

24 controlling/optimizing smart well (iwc) settings IWC settings Well temperature Well flows Well pressures Compartment pressures Compartment production Oil production Water production optimized IWC settings OPTIMIZER (GENETIC ALGORITHMS) Target Function Constraints Intelligent Well Completion (IWC)

25 summary Data Mining allows automated learning from real-time reservoir surveillance data Trained models can be used for optimization tasks including subsurface and surface facilities data As data volumes are cumulating, Data Mining becomes more and more important Data Mining bridges the gap between data visualization and reservoir simulation