Dynamic pricing mechanism for multi-agent based system of well scheduling

Well scheduling is a complex business process which includes the tasks of planning and execution of locating land, constructing, drilling, completing, and tie-in of a well. In our previous work, a multi-agent based framework was proposed to automate well scheduling, where a challenge was to automate the estimation of the price for the tasks that the oil and gas industries may pay and the service providers may accept while gaining economic efficiencies. In this paper, we propose a mechanism to automate this negotiation process. When a new well is added, a scheduling agent (SA) releases the tasks in bundles, and a set of service provider agents (SPAs) bid on the bundles to achieve them. For bundle creation, combinatorial auction is used by the SA, whereas for bid forecasting, a time-series analysis of the historical data is performed by the SPAs using auto regression. The results are discussed to ensure wide acceptance of this mechanism in well scheduling.

[1]  Mohammad Ali Nematbakhsh,et al.  A Bidding Strategy in Combinatorial Auctions , 2011, 2011 Second International Conference on Intelligent Systems, Modelling and Simulation.

[2]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[3]  Ernan Haruvy,et al.  Internet Auctions , 2009 .

[4]  Peter Stone,et al.  Autonomous Bidding Agents in the Trading Agent Competition , 2001, IEEE Internet Comput..

[5]  Eduardo Camponogara,et al.  Mixed-integer linear optimization for optimal lift-gas allocation with well-separator routing , 2012, Eur. J. Oper. Res..

[6]  Bjørn Nygreen,et al.  Alternative approaches to the crude oil tanker routing and scheduling problem with split pickup and split delivery , 2015, Eur. J. Oper. Res..

[7]  Wolfgang Jank,et al.  Real-Time Forecasting of Online Auctions via Functional K-Nearest Neighbors , 2010 .

[8]  R. Weron Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[9]  Pericles A. Mitkas,et al.  A Sequence Mining Method to Predict the Bidding Strategy of Trading Agents , 2009, ADMI.

[10]  Ho-fung Leung,et al.  An Adaptive Bidding Strategy in Multi-round Combinatorial Auctions for Resource Allocation , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[11]  Marcia Tomie Takahashi,et al.  The strategic importance of teaching Operations Research for achieving high performance in the petroleum refining business , 2015 .

[12]  Xiaowei Yang,et al.  Adaptive pruning algorithm for least squares support vector machine classifier , 2010, Soft Comput..

[13]  Qian Chen,et al.  An Empirical Analysis of Bidding Behavior in Simultaneous Ascending-Bid Auctions , 2010, 2010 International Conference on E-Business and E-Government.

[14]  M. P. Wellman,et al.  Price Prediction in a Trading Agent Competition , 2004, J. Artif. Intell. Res..

[15]  Pericles A. Mitkas,et al.  Designing Pricing Mechanisms for Autonomous Agents Based on Bid-Forecasting , 2005, Electron. Mark..

[16]  Fuhua Lin,et al.  Modeling Well Scheduling as a Virtual Enterprise with Intelligent Agents , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[17]  Jie Lu,et al.  A price prediction model for online auctions using fuzzy reasoning techniques , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).