Innovative Production Optimization Technique for Smart Well Completions Using Real-Time Nodal Analysis Applications

2017 ◽  
Author(s):  
Amer M. Al-Anazi ◽  
Obi L. Isichei ◽  
Mohammed A. Al-Yaha ◽  
Faleh M. Al-Shammeri
Keyword(s):  
Real Time ◽  
Optimization Technique ◽  
Production Optimization ◽  
Nodal Analysis ◽  
Well Completions

scholarly journals Production optimization in well-6 of Habiganj gas field, Bangladesh: a prospective application of Nodal analysis approach

2020 ◽  
Vol 10 (8) ◽  
pp. 3557-3568
Author(s):  
Md. Shaheen Shah ◽  
Md Hafijur Rahaman Khan ◽  
Ananna Rahman ◽  
Stephen Butt
Keyword(s):  
Optimization Technique ◽  
Gas Production ◽  
Production Optimization ◽  
Vertical Wells ◽  
Gas Reservoirs ◽  
Gas Field ◽  
Nodal Analysis ◽  
Prospective Application ◽  
Overall Performance ◽  
Inflow Performance

Abstract The overall performance of gas reservoirs and the optimization of production, as well as its sensitivity analysis, are affected by several factors such as reservoir pressure, well configuration and surface facilities. The Habiganj well no. 06 (HBJ-06) is one of the significant gas-producing vertical wells of the Habiganj gas field, currently producing 14.963 MMscfd of natural gas from the upper gas sand. The widely used Nodal analysis is an optimization technique to improve the performance and was applied for the HBJ-06 to increase its production rate by optimizing manners. By this analysis, each component starting from the reservoir to the outlet pressure of the separator was identified as a resistance in the system by evaluating their inflow performance relationship and vertical lift performance. The F.A.S.T. VirtuWell™ software package was used to perform this analysis, where the declinations of wellhead pressures were suggested as 1300, 1200, 1100 and 1000 psi(a) without any modification of the tubing diameter and skin factor. Hence, the respective optimized rates of the daily gas production were increased to 38.481, 40.993, 43.153 and 46.016 MMscfd. At the same time, the optimized condensate gas ratio was calculated as 0.07, 0.06, 0.06 and 0.05, associated with the optimized condensate water ratio of 0.11, 0.10, 0.09 and 0.08, respectively.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

Engineering Workflows Provide Integrated Operations and Streamline Real-Time Production Optimization

2011 ◽  
Author(s):  
Sebastiano Barbarino ◽  
Salvador Sarmiento Mendoza ◽  
Romel Cuadras ◽  
Jose Gerardo Suarez ◽  
Mailing Hung ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Time Production ◽  
Integrated Operations

scholarly journals Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1723
Author(s):  
Félix Dubuisson ◽  
Miloud Rezkallah ◽  
Hussein Ibrahim ◽  
Ambrish Chandra
Keyword(s):  
Real Time ◽  
Power Management ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Euler Method ◽  
Small Scale ◽  
Diesel Generator ◽  
Bacterial Foraging Optimization ◽  
Time Model ◽  
Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

Practical Applications of Real-time Surface Monitoring in Mature Offshore Brown Field for Production Optimization

2020 ◽  
Author(s):  
M.F. Haron ◽  
K.A. Md Yunos ◽  
K.L. Tan ◽  
F.L. Bakon ◽  
C. Tang Ye Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Practical Applications ◽  
Surface Monitoring

Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics

2021 ◽  
Author(s):  
Gaurav Modi ◽  
Manu Ujjwal ◽  
Srungeer Simha
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Optimization Technique ◽  
Material Balance ◽  
Oil Production ◽  
Data Driven ◽  
Petroleum Engineering ◽  
Short Term ◽  
Injection Water ◽  
Facility Level

Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

Samotlor: Implementation of Real Time Production Optimization Process for the Largest Russian Oil Field

2011 ◽  
Author(s):  
Sergey Shevchenko ◽  
Dmitry Mironov ◽  
V.A. Navozov ◽  
Eduard Muslimov ◽  
Roman Leonidovich Pchelnikov ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Oil Field ◽  
Optimization Process ◽  
Time Production
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