CASE STUDIES: MARCELLUS, EAGLE FORD, NIOBRARA

Transcription

1 FACT-BASED MODELING ANALYSIS, PREDICTION, & OPTIMIZATION OF HYDRAULIC FRACTURING PRACTICES IN SHALE ADVANCED DATA-DRIVEN ANALYTICS CASE STUDIES: MARCELLUS, EAGLE FORD, NIOBRARA Shahab D. Mohaghegh WEST VIRGINIA UNIVERSITY & INTELLIGENT SOLUTIONS, INC. August 2015

2 INTRODUCTION Production from Shale, specially with the introduction of massive multi-cluster, multi-stage hydraulic fractures, is not easy to model. Simplistic analyses usually result in superficial conclusions and cookiecutter approaches during implementation. Numerical Reservoir Simulation & Modeling (at its current state) is unable to model the complexities of production from Shale. 2

3 INTRODUCTION Current state of numerical and/or analytical modeling of production from Shale leaves much to be desired: Analytical Models: Too simplistic, Too many limiting assumptions. Numerical Models: Carbonate + CBM = Shale Cannot model realities of induced fracture and its interaction with natural fractures. Computationally expensive. 3

4 Introduction of an Alternative Solution Advanced Data-Driven Analytics Large amounts of data are collected during drilling, logging, completing and producing Shale wells. Advanced Data-Driven Analytics will let us: Distinguish between the influence of formation characteristics vs. completion practices, Help us understand the nuances of completion design, Assist us in optimizing completion design of future wells.

5 Case Studies Case studies are presented to demonstrate the value of data in developing Shale plays. Some operators have already started the journey and are reaping the benefits. Others are contemplating the advantages and disadvantages.

6 Data Collected during the Shale Development The data collected by operators during the shale development potentially include a treasure trove of information. Well Location/trajectory Reservoir Characteristics Completion Characteristics Hydraulic Fracturing Characteristics Production data 6

7 Marcellus Shale Asset

8 Lessons Learned: Hydraulic fracturing in shale wells can and should be optimized. We demonstrate that the One Size Fits All approach to design and implementation of frac jobs in shale wells fails to take full advantage of their production potentials. 8

9 Lessons Learned: Traditional statistical analysis, is not the answer: Multivariate Analysis, Regression, Off-the shelf, not fit-for-propose technology. Traditional statistical analysis fails to: Take full advantage of the existing engineering knowledge, Learn from the data in the context of reservoir and production engineering, Model all the complexities associated with production from shale. 9

10 Lessons Learned: A well-defined workflow is required that: Starts with the data we have, and Results in meaningful analyses and models that can be used to optimize operation. 10

11 EXECUTIVE SUMMARY Advanced Data-Driven Analytics based on pattern recognition is introduced. This technology enables operators to: Develop Estimate Design an predictive fluid optimum production model, multi-stage, for integrating: a newly frac job planned for a a. well, newly Well taking planned Location into well, account & Trajectory, by optimizing: uncertainties a) b. associated Shot Density Reservoir with & Clusters Characteristics, all involved per Stage parameters (well b) location/trajectory, Stimulated Lat. Length reservoir, & Lat. Length completion per Stages & c. stimulation). Actual Completion and Stimulation c) Total amount of Injected Fluid and Proppant Parameters, d) Injection Pressure and Injection Rates d. Production history. 11

12 Hard vs. Soft Data in Shale Hard Data Fluid Types Fluid Amounts (bbls) Pad Volume (bbls) Slurry Volume (bbls) Proppant Types Proppant Amounts (lbs) Mesh Size Proppant Conc. (Ramp Slope) Max. Proppant Concentration Injection Rate Injection Pressure: - Average Inj. Pressure - Breakdown Pressure - ISIP - Closure Pressure Soft Data Hydraulic Fracture Half Length Hydraulic Fracture Width Hydraulic Fracture Height Hydraulic Fracture conductivity Stimulated Reservoir Volume: - SRV height - SRV Width - SRV length SRV Permeability 12

13 Let s Examine the Data 13

14 Let s Examine the Data 14

15 Let s Examine the Data These slides demonstrate that there is not even the suggestion of a correlation between any of the well, reservoir, completion and stimulation characteristics with the field production. The lack of correlation persists, no matter how you chose to normalize the data. If you have worked with Shale, this is not news to you. This fact points to the complexity of the system. 15

16 Pattern Discovery Advanced Data-Driven Analytics Fuzzy Set Theory: Reality is usually neither completely black, nor completely white. Statements are usually neither completely true, nor completely false. Human brain, as the most sophisticated pattern recognition engine in the universe, does not operate with two-valued logic. Fuzzy Logic, uses Fuzzy Set Theory, in order to: Decipher complex patterns from seemingly chaotic data, Perform approximate reasoning with multiple truth values.

17 Pattern Discovery Advanced Data-Driven Analytics 17

26 Predictive Modeling & Analysis INPUT MODEL OUTPUT INPUT PARAMETERS Gas Production 26

27 Predictive Modeling & Analysis Training Calibration - BLIND Validation Training - BLIND Calibration - BLIND

28 Predictive Modeling & Analysis MODELING 180 DAYS CUMULATIVE GAS PRODUCTION (MSCF) 28

29 Predictive Modeling & Analysis MODELING 180 DAYS CUMULATIVE GAS PRODUCTION (MSCF) TRAINING dataset R 2 = Correlation Coefficient =

30 Predictive Modeling & Analysis MODELING 180 DAYS CUMULATIVE GAS PRODUCTION (MSCF) CALIBRATION (blind) dataset R 2 = Correlation Coefficient =

31 Predictive Modeling & Analysis MODELING 180 DAYS CUMULATIVE GAS PRODUCTION (MSCF) VALIDATION (blind) dataset R 2 = Correlation Coefficient =

32 Predictive Modeling & Analysis UNCERTAINTY ANALYSIS AVERAGE PRESSURE psi AVERAGE INJECTION RATE bbl/min TOTAL SLURRY VOLUME bbls PROPPANT CONCENTRATION lbs/gal TOTAL PROPPANT lbs TOTAL NUMBER OF STAGES ALLWELL PARAMETERS AND RESERVOIR CHARACTERISTICS IN THE MODEL ARE KEPT CONSTANT - MARCELLUS SHALE Actual Production 32

35 Frac Job Quality Control Performing Monte Carlo Simulation for 132 wells, compiling the results: Frac Job Quality Number of Wells Percentage Excellent 20 15% Better than Expected 45 34% Expected 39 30% Worse than Expected 12 9% Poor 16 12% 35

36 Full Field Reservoir Model When this technology is used to build full field reservoir model, the resulting reservoir model is called Top-Down Model (TDM) TDM technology uses field measurements and no assumptions regarding the storage and transport phenomena to build full filed reservoir model. TDM can be used to understand the intricacies of reservoir and production management of shale assets.

37 Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Shale Full Field Model (TDM)

38 Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Shale Full Field Model (TDM) 10096

42 Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Shale Full Field Model (TDM) History matching result of the entire asset.

43 Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Model Validation TDM is validated using: blind History matching August 2006 February 2012 March 2013 History Matching Blind Match production from new wells

44 Blind History Match Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Model Validation Blind History Match Forecasting Error=4.16% Blind History Match Forecasting Error= 4.33%

45 Blind History Match Production History Matching and Forecasting of Shale Assets Using Pattern Recognition Soodabeh Esmaili: Ph.D. Dissertation, West Virginia University, Petroleum & Natural Gas Engineering - July 2013 Model Validation Blind History Match

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