OILFIELD ANALYTICS: OPTIMIZE EXPLORATION AND PRODUCTION WITH DATA-DRIVEN MODELS

Transcription

1 Sas day 2014 OILFIELD ANALYTICS: OPTIMIZE EXPLORATION AND PRODUCTION WITH DATA-DRIVEN MODELS

2 AGENDA The Intelligent Field Data Mining Virtuous Cycle Data Mining: What is it? Data Mining: O&G Input Space Deterministic to Probabilistic SEMMA Process: Case Study Analytical Centers of Excellence

3 THE INTELLIGENT FIELD Image courtesy of Forrester research, Inc.

4 (R)EVOLUTION DEVELOPMENT OF THE INTELLIGENT FIELD Ageing Technology Measurements Infrastructure Capabilities Standards COE Interoperability Experience based workers Chain Slingers Bus Sensors WIFI Sensors Satellite Comms Fiber Comms Remote Visualization Wired Pipe 4D SEISMIC Advanced MWD Rotary Steerable Artificial Lift WITS WITSML PRODML OPC PPDM Remote Centers Decision Support Data management Remote Control Data Quality Workforce changes Mechanical focus Automation Lack of subsurface data TEX T HS&E

5 DATA MINING VIRTUOUS CYCLE Those who do not learn from the past are condemned to repeat it. George Santayana

6 DATA MINING: WHAT IS IT? Data Mining Styles Hypothesis Testing Directed Data Mining Undirected Data Mining

7 Artificial Intelligence & Predictive Analytics Neural Networks Fuzzy Logic Computational Intelligence Evolutionary Computation Virtual Environments Data Mining Surrogate Models Proxy Models Artificial Intelligence & Data Driven Analytics Rules Based Case Reasoning Bayesian Networks Workflow Automation Top Down Models Automatic Process Control Expert Systems

8 Artificial Intelligence & Predictive Analytics Computational Intelligence Self- Organizing Maps Artificial Neural Networks Swarm Intelligence Intelligent Agents Genetic Algorithms Machine Learning Neural Networks Fuzzy Logic Evolutionary Computation

9 Artificial Intelligence & Predictive Analytics Data Mining AI techniques Random Forest Regression Cluster Analysis Segmentation Classification Prediction

10 DATA MINING: O&G INPUT SPACE

11 DETERMINISTIC TO PROBABILISTIC Data Deterministic analysis Outcomes Historical Real-time Experience Situation A Situation B Situation C Data Probabilistic analysis Predictive Outcomes Actionable workflows Historical Real-time Experience Variability Complex relationships Situation A 95% Situation B 22% Situation C 36% Workflow A Workflow B Workflow C

12 SUBJECT MATTER EXPERTS DATA SCIENTISTS

13 THE WITCH METHODOLOGY SAS DAY 2014

14 Which Witch? THE WITCH METHODOLOGY

15 BREAK OUT SAS DAY 2014

16 DATA-DRIVEN MODELS THE WITCH ANALYSIS Objective Function Target Variable: Optimize Identification of Witch (Burn) Descriptive Statistics Attributes: Big Nose, Hat & Cloak, Wart Operational parameters: Turned a villager into a newt Why do witches burn and float? Made of Wood? What else is made of wood? What else floats in water - Correlations Cluster Analysis: Good and Bad Female profiles Null Hypothesis: Weighs the same as a duck Generate hypotheses: Knight, King Arthur and villagers Directed DM Target Variable: gradations of being a witch Boolean & Fuzzy Logic Undirected DM Classify Patterns

17 CASE STUDY SAS DAY 2014

18 MULTIPHASE FLOW RATE ESTIMATION BUSINESS ISSUES To optimize well and reservoir management, it is critical to continuously monitor the production for each well in an asset s portfolio. Only a small number of wells are equipped with individual multiphase flow meters (MPFM). SOLUTION Neural networks, regression algorithms and a classification method called random forest were implemented in analytical workflows to ascertain optimum model. All three methodologies were trained on real-time data from step rate well tests obtained from multiple wells. UNCONVENTIONAL GAS SAS Enterprise miner enabled us to accelerate and implement a easy to use and intuitive solution to the multivariate uncertainties inherent in subsurface environments Production Engineering Advisor RESULTS AND EXPECTED RESULTS Data-driven methods successfully estimate accurate production rates. All methods have a very short run time and can be implemented in a real-time system as virtual metering systems. Most wellheads in offshore operations are sufficiently equipped with transmitters to provide all requisite input data. The models can also be used to analyze production system dependencies, such as the effect of back pressure or changes of the gas lift rate. Copyr i g ht 2012, SAS Ins titut e Inc. All rights res er ve d.

19 DATA-DRIVEN MODELS DATA-DRIVEN METHODOLOGY FOR STOCHASTIC MPFM

20 DATA-DRIVEN MODELS MPFM AND SCADA DATA Downhole pressure Downhole temperature Well head\tubing head pressure Well head\tubing head temperature Casing head pressure and temperature Choke settings Gaslift injection rates Gaslift Supply pressure ESP intake and discharge pressure Cumulative Oil Rates Cumulative Water Rates Cumulative Liquids Rates Flow-line pressure at the beginning of the export pipeline

21 CASE STUDY: SEMMA

22 DATA-DRIVEN MODELS EXPLORATORY DATA ANALYSIS OF INPUT SPACE FOR STOCHASTIC MPFM Surface hidden patterns Identify trends and correlations Establish relationships among independent and dependent variables [Factors and Targets] Data QC Reduce input space: Factor Analysis, PCA Identify key parameters for model building

23 Case Study DATA MODIFICATION Traditional Well Tests Data-driven techniques Well Flow Rates Exploratory Data Analysis Outliers Imputation Factor Analysis Clustering module Data mining workflow

24 CASE STUDY: SEMMA CREATE MODELS TOWARDS OBJECTIVES 1. Cumulative liquid production 2. Cumulative oil or gas production 3. Gaslift Injection Rates 4. Wellhead pressure and temperature 5. Tubing head pressure and temperature 6. Estimated production rates

25 CASE STUDY: SEMMA Score the dataset generated from a trained model or models Convert scoring code to Java or C to enable an operational model Asses best model based on statistical fit AIC & BIC

26 DATA-DRIVEN MODELS DATA-DRIVEN METHODOLOGY FOR STOCHASTIC MPFM Multiple Linear Regression Random Forest Classification Artificial Neural Network

27 DATA-DRIVEN MODELS MULTIPLE LINEAR REGRESSION Parametric model Model relationships between 2+ Explanatory variables and a Response variable Every value of the independent variable x is associated with a value of the dependent variable y Fit a regression line Investigate residuals Ascertain significance of variables: p-values

28 DATA-DRIVEN MODELS RANDOM FOREST CLASSIFICATION Grow multiple classification trees Classify each variable from an input vector Put the input vector down each tree in the forest Each tree yields a classification or vote Forest selects the classification with most votes Unexcelled in accuracy Efficient on large datasets Handle thousands of independent variables Estimates importance of each independent variable Does not over-fit

29 DATA-DRIVEN MODELS ARTIFICIAL NEURAL NETWORK Computational model inspired by the central nervous system Pattern recognition Machine learning Consists of: Adaptive weights Neurons Supervised learning

30 MODEL COMPARISONS MLR: Parametric Model Easy to interpret model & parameters Direct measurements [Temperature and Pressure] Coefficients of models reflect response changes with unit change Big coefficients => More change Easy to score ANN: Nonparametric information Response increases linearly with temperature and dips Behavior not captured in low-order polynomial regression Hardest to interpret Difficult scoring Tree-Based models: Nonparametric Ideal for decision making owing to interpretability IF-THEN-ELSE Random-Forest: Ensemble of tree-based models with randomness Complicated scoring

31 ANALYTICAL CENTERS OF EXCELLENCE The fundamental idea of cross-functional teams and goals appears to surface about every 10 years with a new label. Usually, attempts to implement this concept in the E&P business ended with utter failure for a variety of reasons.

32 Q&A SAS DAY 2014