Short-Term Production Operation Management: Continuous Application of an Innovative Integrated Production Optimization Tool

2015 ◽  
Author(s):  
Sara Scaramellini ◽  
Paolo Cerri ◽  
Amalia Bianco ◽  
Silvia Masi
Keyword(s):  
Production Optimization ◽  
Operation Management ◽  
Production Operation ◽  
Short Term ◽  
Integrated Production ◽  
Continuous Application

Integrated Production Model as a Tool for Optimization the Development Strategy of the Sakhalin Oil and Gas Condensate Field

2021 ◽  
Author(s):  
Oleksandr Doroshenko ◽  
Miljenko Cimic ◽  
Nicholas Singh ◽  
Yevhen Machuzhak
Keyword(s):  
History Matching ◽  
Effective Field ◽  
Production Optimization ◽  
Production Model ◽  
Production Forecast ◽  
Hydrocarbon Production ◽  
Integrated Production ◽  
Wellhead Pressure ◽  
Field Development ◽  
The Impact

Abstract A fully integrated production model (IPM) has been implemented in the Sakhalin field to optimize hydrocarbons production and carried out effective field development. To achieve our goal in optimizing production, a strategy has been accurately executed to align the surface facilities upgrade with the production forecast. The main challenges to achieving the goal, that we have faced were:All facilities were designed for early production stage in late 1980's, and as the asset outdated the pipeline sizes, routing and compression strategies needs review.Detecting, predicting and reducing liquid loading is required so that the operator can proactively control the hydrocarbon production process.No integrated asset model exists to date. The most significant engineering tasks were solved by creating models of reservoirs, wells and surface network facility, and after history matching and connecting all the elements of the model into a single environment, it has been used for the different production forecast scenarios, taking into account the impact of infrastructure bottlenecks on production of each well. This paper describes in detail methodology applied to calculate optimal well control, wellhead pressure, pressure at the inlet of the booster compressor, as well as for improving surface flowlines capacity. Using the model, we determined the compressor capacity required for the next more than ten years and assessed the impact of pipeline upgrades on oil gas and condensate production. Using optimization algorithms, a realistic scenario was set and used as a basis for maximizing hydrocarbon production. Integrated production model (IPM) and production optimization provided to us several development scenarios to achieve target production at the lowest cost by eliminating infrastructure constraints.

Short-Term Production Optimization Under Water-Cut Uncertainty

SPE Journal ◽  
2020 ◽  
pp. 1-21
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Maria Clara Duque ◽  
Virgílio Ferreira Filho ◽  
Juliana Baioco ◽  
...  
Keyword(s):  
Latin Hypercube Sampling ◽  
Stochastic Approach ◽  
Injection Rate ◽  
Production Optimization ◽  
Support Vector ◽  
Well Test ◽  
Short Term ◽  
Stochastic Solution ◽  
Water Cut ◽  
Gas Lift

Summary Short-term production optimization is an essential activity in the oil/gasfield-development process because it allows for the maximization of field production by finding the optimal operational point. In the fields that use gas lift as an artificial-lift method, the gas-lift optimization is a short-term problem. This paper presents a stochastic approach to include uncertainties from production parameters in gas-lift optimization, called the uncertain-gas-lift-optimization problem (UGLOP). Uncertainties from production variables are originated from the measurement process and the intrinsic stochastic phenomena of the production activity. The production variables usually obtained from production tests play an important role in the optimization process because they are used to update reservoir and well models. To include the uncertainties, the strategy involves representing the well-test data using nonlinear regression [support-vector regression (SVR)] and using the Latin-hypercube-sampling (LHS) method. The optimization gives a stochastic solution for the operational point. In the solved problem, this operational point is composed of the individual wells’ gas-lift-injection rate, choke opening, and well/separator routing. The value of the stochastic solution is computed to evaluate the benefit of solving the stochastic problem over the deterministic. The developed methodology is applied to wells of a Brazilian field considering uncertainty in water-cut (WC) values. As a result, an up-to-4.5% gain in oil production is observed using this approach.

Production Optimization – Integrated Production Modeling – El Portón Oilfield

2005 ◽  
Author(s):  
P. Marin ◽  
V. Lattanzi ◽  
J.G. Rodriguez ◽  
C. Canel ◽  
C. Gilardone
Keyword(s):  
Production Optimization ◽  
Integrated Production ◽  
Production Modeling

Production Operation Management Research of Mixed Private Cloud Enterprise

2014 ◽  
Vol 635-637 ◽  
pp. 1935-1939
Author(s):  
Guang Rong Li ◽  
Zhi Liang Wang ◽  
Yun Xia Wang ◽  
Yong Lu ◽  
Sheng Ping Hu
Keyword(s):  
Operation Management ◽  
Production Operation ◽  
Object Model ◽  
Management Research ◽  
Private Cloud ◽  
Specific Content ◽  
Specific Process ◽  
Order Form ◽  
Production Order

According to manufacturing characteristics of mixed private cloud enterprise, production operation management requirements of mixed private cloud enterprise are analysed. Function of production operation management in mixed private cloud enterprise is summarized. Specific content of production operation management is inducted based on mixed cloud enterprise. The process of production operation management are designed in detail. Object model diagram of production operation management are elaborated. It is analysed that the content of the reform of production order and the specific process are obtained. It is researched that production order form to be queried and finally end process has been preliminaryly discussed.It has laid a basis for optimal and reasonable application of production operation management.

iPOS–an integrated production operation solution for Longtom Field

2011 ◽  
Vol 51 (1) ◽  
pp. 259
Author(s):  
Rajesh Trivedi ◽  
Shripad Biniwale ◽  
Adil Jabur
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Joint Venture ◽  
Direct Access ◽  
Quality Data ◽  
Processing Plant ◽  
Production Operation ◽  
Time Data ◽  
Integrated Production ◽  
Real Time Data

With a vision of innovation, integrity and agility, Nexus Energy began first production of Longtom field in October 2009. The Longtom gas field is located in the Gippsland Basin, offshore Victoria where the produced gas is transported to Santos’ Patricia Baleen gas processing plant. All production data is acquired by Santos with the supervisory control and data acquisition (SCADA) system. The challenge for Nexus Energy was to monitor the field remotely in the absence of a data historian and to support the operational people proactively. Data acquisition from Santos, validation, and storage in a secured centralised repository were therefore key tasks. A system was needed that would not only track accurate production volumes to meet the daily contractual quantity (DCQ) production targets but that would also be aligned with Nexus’s vision for asset optimisation. We describe how real-time data is acquired, validated, and stored automatically in the absence of a data historian for Longtom field, and how the deployed system provides a framework for an integrated Production Operation System (iPOS). The solution uses an integrated methodology that allows effective monitoring of real-time data trends to anticipate and prevent potential well and equipment problems, thus assisting in meeting DCQ targets and providing effective analysis techniques for decision making. Based on full workflow automation, the system is deployed for acquisition, allocation, reporting and analysis. This has increased accuracy, accountability and timely availability of quality data, which has helped Nexus improve productivity. The comprehensive reporting tool provides access to operational and production reports via email for managers, output reports in various formats for joint venture partners, and nontechnical users without direct access to the core application. A powerful surveillance tool, integrated with the operational database, provides alarms and notifications on operation issues, which helps engineers make proactive operational decisions. The framework allows a streamlined data flow for dynamic updates of well and simulation models, improving process integration and reducing the runtime cycle.

scholarly journals Short-Term Model-Based Production Optimization for a Gas Field in North Africa

2018 ◽  
Vol 10 (2) ◽  
pp. 65
Author(s):  
Arnaud Hoffmann
Keyword(s):  
Piecewise Linear ◽  
Treatment Plant ◽  
Gas Production ◽  
Production Optimization ◽  
Mixed Integer ◽  
Short Term ◽  
Gas Reservoir ◽  
Gas Field ◽  
Gas Treatment ◽  
Model Based

 This paper presents a model-based optimization solution suitable for short-term production optimization of large gas fields with wells producing into a common surface network into a shared gas treatment plant. The proposed methodology is applied to a field consisting of one dry gas reservoir with a CO2 content of 7.3% and one wet gas reservoir with a CO2 content of 2.8% and initial CGR of 15 stb/MMscf. 23 wells are producing, and all gas production is processed in a common gas treatment plant where condensates and CO2 are extracted from the reservoir gas. The final sales gas must honor compositional constraints (CO2 content and heating value). The proposed solution consists of a bi-level optimization algorithm. A Mixed Integer Linear Programming (MILP) formulation of the optimization problem is solved, assuming some key parameters in the gas plant to be constant. Hydraulic performances of the system, approximated using SOS2 piecewise linear models, and condensates and CO2 extraction, captured using simplified models, are included in the MILP. After solving the MILP, the values of the key parameters are calculated using a full simulation model of the gas plant and the new values are substituted in the MILP input data. This iterative procedure continues until convergence is achieved. Results show that the proposed methodology can find the optimum choke openings for all wells to maximize the total gas rate while honoring numerous surface constraints. The solution runs in 30 sec. and an average of 3-4 iterations is needed to achieve convergence. It is therefore a suitable solution for short-term production optimization and daily operations.

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