Developing Digital Informational Model for Well Productivity Estimation through Markov Processes Tools
Gubkin University presents a study on developing a digital informational model for estimating well productivity using Markov processes tools in oil and gas production. The research covers industrial data processing, reservoir model uncertainty evaluation, and organizational process planning. It emphasizes the importance of quality, reliability, and risk assessment in well operations, with a focus on digital reservoir model calibration and mathematical model formalization. The aim is to enhance decision-making support platforms for effective well operations management and industrial production systems.
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Presentation Transcript
National University of Oil and Gas Gubkin University DIGITAL INFORMATIONAL MODEL DEVELOPING DIGITAL INFORMATIONAL MODEL DEVELOPING FOR WELL PRODUCTIVITY ESTIMATION FOR WELL PRODUCTIVITY ESTIMATION THROUGH MARKOV PROCESSES TOOLS THROUGH MARKOV PROCESSES TOOLS APPLICATION APPLICATION Mukhina A.G. Moscow 2021
Digital Transformation of Technological Operations in Oil and Gas Production 1.Industrial data processing tools 2. Reservoir model uncertainty evaluation methods 3. Technological parameters retrospective analysis 4. Organizational processes planning in long and short terms 5. Well productivity estimation phases implementation
Analytical Apparatus Formation for Well Productivity and Rhythmical Parameters Estimation Systematization and hierarchical structure building Informational model for Well Operations Paces Rise and Productivity Parameters Estimation Industrial objects state evaluation Planning operations organization Quality, reliability and risk evaluation through oil and gas production operations Digital Analytical Application for Well States and Reservoir Conditions Control Well states observation and mathematical model formalization Hydrodynamic model creation and calibration Maple Platform Implementation for Maple Platform Implementation for Digital Reservoir Model Calibration Digital Reservoir Model Calibration Maple Visualization Tools Application for Well Maple Visualization Tools Application for Well Observations Obtaining and Processing Observations Obtaining and Processing
Mathematical Model Implementation for Digital Model Development Quality, reliability Digital reservoir Digital reservoir model uncertainty model uncertainty reduction and reduction and informativeness informativeness degree increase degree increase and Making decisions support platform formation for the well operations management processes effectiveness rise for industrial production system Actuality, correctness Actuality, correctness and true interpretation of and true interpretation of well measurements well measurements Risk management and Risk management and systematical control systematical control
Digital Analytical Decision Content Essential technological characteristics of the industrial object Analytical Tool for Well Control Potential states and consistent probability values Qualitative and rhythmical model of the production process Information actuality estimation Active operation time
Key Criteria Components for Digital Production Processes Model Estimation 1. Management stages and mechanisms identification 2. Planning prospective solutions obtaining and updating 3. Time scales evaluation of the industrial object stable development
Well Productivity Parameters Informational Model Formalization Industrial processes correct description and investigation Well Productivity Parameters Informational Model creation P Probability robability indexes occasion as well as risks minimization indexes reduction of the possible dangerous situation risks minimization is also among the prior tasks Reservoir Surfaces Formation with Mathematical and Visualization Tools Application on Maple Basics
Digital structural approach for data organization Data Data cubes building and calibration cubes building and calibration Gas Flow Rate, m3/day Evaluation Processing Time, days Visualization Temperature, K informational cubes are distinguished by parameters of functional dependence of two parameters as well as the third time measure using.
Markov Processes Tools Application and Average States Count Evaluation for Well States Identification
States graph for the well testing process observation ? ? - intensity coefficients for states transformation intensity coefficients for states transformation from from i i to j to j ?? - - probability for the well state probability for the well state i i ?? - - average state count for the well state average state count for the well state i i ?? - well state well state i i ?? testing procedures; ?? well operation running; ?? maitenance control; States graph for the well testing process observation and unexpected situation risks reduction ?? pump equipment observation; ?? well potential power estimation; ?? well reservation
Well State Probabilities Identification ??1(?) ?? ??2? ?? ??3? ?? ??4? ?? ??5? ?? ??5? ?? Solution stage Solution stage: : Kolmogorov-Chapman equations system (1) = ?12?1? + ?61?6? = ?12?1? + ?52?5(?) ?23?2? = ?23?2? ?34?3? (1) = ?34?3? ?45?4? = ?45?4? (?56+?52)?5(?) = ?56?5? ?61?6? 6 ??= 1 ?=1
Well State Probabilities Estimation Solution stage Solution stage: : If???(?) ?? 0 = ?12?1? + ?61?6? 0 = ?12?1? + ?52?5(?) ?23?2? 0 = ?23?2? ?34?3? 0 = ?34?3? ?45?4? 0 = ?45?4? (?56+?52)?5(?) 0 = ?56?5? ?61?6? ?=1 If the intensities are known the system (2) will be transferred into (3): = 0then the equations (2) well be obtained: (2) 6 ??= 1 0 = 5?1? + 2?6? 0 = 5?1? + 2?5(?) 4?2? 0 = 4?2? 6?3? 0 = 6?3? 6?4? 0 = 6?4? 5?5(?) 0 = 3?5? 2?6? ?1? + ?2? + ?3? + ?4? + ?5? = 1 Evaluated Results Evaluated Results: : ?1= 0,099, ?2= 0,207, ?3= 0,138, ?4= 0,138, ?5= 0,166, ?6= 0,249 the system states probabilities in each mode type. (3) (3)
Well State Average Count Well State Average Count Evaluation Tool Development Evaluation Tool Development For the system average states count m m estimation in each mode it is necessary to take into account the components number of the production subsystem: ??= ? ??, where N is numerical parameter of each part of the industrial subsystem. If N=10 then: ?1=0,99, ?2= 2,07,?3= 1,38,?4= 1,38,?5= 1,66,?6= 2,49 - system average states count in stable mode. Equations system (1) for the non-stable mode (4). Initial probability for the first state S1: ?10 = 1. ??1? ?? ??2? ?? ??3? ?? ??4? ?? ??5? ?? ??5? ?? = ?12?1? + ?61?6? = ?12?1? + ?52?5? ?23?2? = ?23?2? ?34?3? = ?34?3? ?45?4? (?) = ?45?4? (?56+?52)?5(?) = ?56?5? ?61?6? 6 ?=1 ??? = 1, ?10 = 1,?20 = 0,?30 = 0,?40 = 0,?50 = 0,?60 = 0
Well State Average Count Well State Average Count Estimation Operation Estimation Operation Solution Stage Solution Stage: : With the certain intensities values the system will be solved as (5): ??1? ?? = 5?1? + 2?6? ??2? ?? = 5?1? + 2?5? 4?2? ??3? ?? = 4?2? 6?3? ??4? ?? = 6?3? 6?4? ??5? ?? = 6?4? 5?5? ??5? ?? = 3?5? 2?6? ?=1 ??? = 1, ?10 = 1,?20 = 0,?30 = 0,?40 = 0,?50 = 0,?60 = 0 (5) Result of the visualization procedures implementation with the opportunity of final probabilities estimation 6 Solutions for the system probabilities in each state in non-stable mode will be identified with Maplesoft tools.
Summary Maple tools and functional decisions are assumed as the prospective way of informational digital model creation with correct evaluation operations realization and estimation module developing. Processing and visualization apparatus is the great platform for the real technological objects and operations research and future management decision definition presentation and improvement.
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