Field Development Plans Optimization by Modeling Fluids Flow Impact and Assessing Intelligent Wells on Reservoir Performance

2008 ◽  
Author(s):  
Emad Ahmed Elrafie ◽  
Ghazi Dhafer Al-Qahtani ◽  
Mohammed Alawi Agil ◽  
Alexander Rincon ◽  
Francois-Michel Colomar
2019 ◽  
Author(s):  
Sofiane Tahir ◽  
Salem Al Kindi ◽  
Kassem Ghorayeb ◽  
Elin Haryanto ◽  
Abdur Rahman Shah ◽  
...  

2021 ◽  
Author(s):  
Obinna Somadina Ezeaneche ◽  
Robinson Osita Madu ◽  
Ishioma Bridget Oshilike ◽  
Orrelo Jerry Athoja ◽  
Mike Obi Onyekonwu

Abstract Proper understanding of reservoir producing mechanism forms a backbone for optimal fluid recovery in any reservoir. Such an understanding is usually fostered by a detailed petrophysical evaluation, structural interpretation, geological description and modelling as well as production performance assessment prior to history matching and reservoir simulation. In this study, gravity drainage mechanism was identified as the primary force for production in reservoir X located in Niger Delta province and this required proper model calibration using variation of vertical anisotropic ratio based on identified facies as against a single value method which does not capture heterogeneity properly. Using structural maps generated from interpretation of seismic data, and other petrophysical parameters from available well logs and core data such as porosity, permeability and facies description based on environment of deposition, a geological model capturing the structural dips, facies distribution and well locations was built. Dynamic modeling was conducted on the base case model and also on the low and high case conceptual models to capture different structural dips of the reservoir. The result from history matching of the base case model reveals that variation of vertical anisotropic ratio (i.e. kv/kh) based on identified facies across the system is more effective in capturing heterogeneity than using a deterministic value that is more popular. In addition, gas segregated fastest in the high case model with the steepest dip compared to the base and low case models. An improved dynamic model saturation match was achieved in line with the geological description and the observed reservoir performance. Quick wins scenarios were identified and this led to an additional reserve yield of over 1MMSTB. Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism.


2021 ◽  
Author(s):  
Mikhail Ivanovich Samoilov ◽  
Vladimir Nikolaevich Astafyev ◽  
Evgeny Faritovich Musin

Abstract The paper describes a system of approaches to the design and engineering support of multistage hydraulic fracturing: A method of developing multiple-option modular design of multistage hydraulic fracturing which is a tool for operational decision-making in the process of hydraulic fracturing.Building a Hydraulic Fracturing Designs Matrix when optimizing field development plans. The result was used to build decision maps for finding well completion methods and selecting a baseline hydraulic fracturing design. The paper also describes how the systematization of approaches, methodological developments, and decision templates can help in optimizing field development by drilling directional and horizontal wells followed by multi-stage hydraulic fracturing. The sequence of events and tasks that led to the development of the methodology, as well as its potential, is briefly described. The methodologies were developed during the execution of a hydraulic fracturing project at JK 29 reservoirs of the Tyumen Suite of Em-Yogovskoye field, after which they were applied in a number of other projects for the development of hard-to-recover hydrocarbon reserves in West Siberia.


SPE Journal ◽  
2019 ◽  
Vol 24 (04) ◽  
pp. 1526-1551
Author(s):  
Atefeh Jahandideh ◽  
Behnam Jafarpour

Summary Reservoir simulation is a valuable tool for performance prediction, production optimization, and field-development decision making. In recent years, significant progress has been made in developing automated workflows for optimization of production and field development by combining reservoir simulation with numerical optimization schemes. Although optimization under geologic uncertainty has received considerable attention, the uncertainty associated with future development activities has not yet been considered in field-development optimization. In practice, reservoirs undergo extensive development activities throughout their life cycle. Disregarding the possibility of future developments can lead to field-performance predictions and optimization results that might be far from optimal. This paper presents a stochastic optimization formulation to account for the uncertainty in future development activities while optimizing current decision variables (e.g., well controls and locations). A motivating example is presented first to demonstrate the significance of including the uncertainty in future drilling plans in oilfield-development optimization. Because future decisions might not be implemented as planned, a stochastic optimization framework is developed to incorporate future drilling activities as uncertain (random) variables. A multistage stochastic programming framework is introduced, in which the decision maker selects an optimal strategy for the current stage decisions while accounting for the uncertainty in future development activities. For optimization, a sequential approach is adopted whereby well locations and controls are repeatedly optimized until improvements in the objective function fall below a threshold. Case studies are presented to demonstrate the advantages of treating future field-development activities as uncertain events in the optimization of current decision variables. In developing real fields, where various unpredictable external factors can cast uncertainty regarding future drilling activities, the proposed approach provides solutions that are more robust and can hedge against changes/uncertainty in future development plans better than conventional workflows.


2008 ◽  
Author(s):  
Christopher Hadley ◽  
David Peters ◽  
Andrew Vaughan ◽  
Daniel Bean

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