Workover Rig Scheduling Platform Maximizing Cumulative Gain
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Solution Overview
Problem
Traditional workover rig scheduling methods are inefficient in terms of time and computing resources, and they fail to consider all constraints and business rules effectively, leading to suboptimal allocation of expensive workover rigs in oil and gas operations, resulting in delays and loss of production.
Innovation Solution
A workover rig scheduling platform that generates schedules in a time- and resource-efficient manner by calculating virtual gains for each well based on production gain, travel time, workover time, and other parameters, prioritizing wells with higher gains and optimizing the allocation of workover rigs to maximize cumulative production gain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional workover rig scheduling methods are used, then scheduling can be performed with simple methods, but time efficiency and computing resource efficiency are poor, leading to suboptimal allocation
Solution Approach 1:
The system pre-calculates and stores travel times between all pairs of wells in advance, creating a lookup table that can be quickly referenced during scheduling. This preliminary action eliminates the need for real-time distance calculations, significantly reducing computation time while maintaining scheduling optimality
Solution Approach 2:
The scheduling problem is segmented into discrete decision points where the system evaluates virtual gains for selecting the next well to service. By breaking down the complex optimization problem into sequential well selection decisions based on calculated virtual gains, the system achieves efficient computation without sacrificing solution quality
2Productivity
If traditional workover rig scheduling methods are used, then implementation is simpler, but computing resource consumption increases and allocation optimality decreases
Solution Approach 1:
The system replaces complex iterative optimization algorithms with a direct calculation approach using virtual gains. By substituting the traditional mechanical optimization process with a streamlined calculation method that evaluates well selection based on pre-computed parameters, the system reduces computing resource consumption while improving allocation efficiency
Solution Approach 2:
The system transforms the scheduling problem by introducing virtual gain as a composite parameter that combines production gain, travel time, and workover time. This parameter transformation simplifies the optimization criteria, enabling more efficient computation with fewer resources while maintaining allocation optimality
3Productivity
If workover rigs are allocated to maximize production gain, then cumulative production increases, but travel time and scheduling complexity increase
Solution Approach 1:
The system extracts and isolates the key factors affecting production gain (production gain, travel time, workover time) and combines them into a single virtual gain metric. By separating these critical factors from other less important considerations, the system simplifies the scheduling logic while maintaining focus on maximizing cumulative production gain
Data Source
AI summary
Methods, systems, and computer-readable storage media for resource-efficient generation of a workover rig schedule by receiving well data and workover rig data for wells, each well producing oil, determining sets of schedules by, for each schedule in a set of schedules, incrementally: calculating a virtual gain for each well in a set of remaining wells, each virtual gain being a function of a production gain for a respective well, a travel time of a workover rig to travel from a current well to the respective well, a workover time for the respective well, a first parameter applied to the workover time, and a second parameter applied to the travel time, and selecting a well as a next well in a schedule based on a respective virtual gain; identifying, from the sets of schedules, a schedule having a maximum cumulative gain, and outputting the schedule as an optimal schedule.