For data entry of wells DDRs which have been drilled in the past, the Casing program, Bit information, mud logging unit data, geological data etc, this advanced and user-friendly section called the Data Entry module is designed and developed. Also, information about NPT and PT times and the classification of these times are also included in this database. The data output of each well in this section is reported in the form of specific formats, which are used for the relevant analyzes.
At this step, wells grouping takes place using three different methods (Top of Formation, Casing Program, and Coordinates of adjacent wells). Then, for each group, a virtual optimized well based on the data used for drilling offset wells of each The group is formed and using the output data of this step, the hydraulic and mechanical parameters are optimized and the best type of bit and BHA is proposed for drilling each section of the well.
At this module, considering the general conditions of the hole, the properties of the drilling fluid, the properties of the down-hole motor, the specifications of the bit, the limitation of the mud pumps and the drilling rig and etc, The best combination of hydraulic parameters (nozzle size, GPM, etc.) And the most suitable BHA in each section of hole is proposed, and the mechanical parameters of WOB and RPM are optimized by the method of hydromechanical energy-mechanical energy.
The prediction of the penetration rate (ROP) is done in two ways, or the analytical equations developed in this field should be used or artificial intelligence methods should be used. In the software developed, all the previous analytical equations are included in the software, as well as new artificial intelligence models in the field of prediction of ROP have been developed to allow the lowest possible error to be predicted for the ROP.
The time to use a drilling rig to drill a well is called a daily rig time, which is divided into productive-time and non-productive-time periods. The daily rent of a drilling rig forms a huge part of the daily cost of drilling operation, so reducing the drilling time is very important. Optimization of drilling operation refers to a set of management, engineering, and operational efforts that minimize PT times and eliminate NPT times.
The basis of drilling optimization is the use of offset wells data as well as real-time data as a base and the application of optimization methods on these data, in order to reduce the time to drill a well in future. Using these methods, the optimized drilling parameters is determined, followed by increased ROP and reduced the cost of drilling operation.
- Integrate all data
- Analysis of previous wells data with data mining and risk management
- Using of mechanical energy and hydro-mechanical energy diagrams to find engineering problems in previous wells
- Practical Optimization to Find Best Practices and Lesson Learns
- Using artificial intelligence methods to predict the ROP
- Using of scientific optimization techniques to find fully hydraulic and mechanical parameters
- Comprehensive report for future drilling program every 50 meters
Hydraulic and Mechanical Energy
The ROP is influenced by many parameters such as bit type, bit nozzle size, drilling fluid pump rate, WOB, RPM, bit torque, drilling fluid properties, formation characteristics, rock mechanical parameters, and so on. In order to categorize these parameters in two categories of mechanical and hydraulic parameters, the ZDOC team, by considering the simultaneous optimization of these parameters, explains its plan for drilling optimization.
Hydraulic Parameters Optimization50%
Mechanical Parameters Optimization50%
Optimization of Mechanical and Hydraulic Parameters together100%
Drilling Optimization Program
In the first step, the input of the wells should be entred and then the practical optimization is carried out. Using the outcomes of this step, the ROP can be greatly increased and the costs reduced. The main advantage of this section is that the data of the wells are based on reality and we can reuse them.
In the second step, the management of the drilling operation risks should be done taking into account all the cases of non-productive time of drilling operation that were previously involved and all strategic decisions to eliminate the risks and those that lead to additional costs for the NPT times.
In the next step, scientific optimization should be performed. After completing the practical optimization and risk and cost reduction decisions, the computational phase of the operation was initially analyzed by analyzing the geological status of the field, the total depth of the drilled is divided into several sections, and in each section the optimization formulas are developed, so that in each phase the Hydraulic and mechanical parameters will be determined. With practical and scientific computational data, the most suitable BIt type and the most appropriate BHA is also selected.
In the formulated program of this company, using practical optimization and scientific optimization methods, optimum data including WOB, RPM, TFA, Liner Size, SPP, SPM, Bit type, BHA type and more are determined by ROP prediction. This Parameters are selected as optimal initial data.
In the final stage, the method of optimizing mechanical and hydromechanical energy is used to improve the ROP using real time data. At this stage, it is necessary to obtain the output data from the Mud Logging Units and use them to determine the best drilling parameters to increase the ROP and reduce costs.
Business Intelligence in OptiTech Software
The software consists of four main parts, data entry, practical optimization, scientific optimization and ROP prediction. In different parts of this software, except the scientific optimization module, Business intelligence-based methods for adding value to previous wells data, have been used. In the data entry module uses data cube and OLAP methods for quick analysis and data operations. In the practical optimization section, intelligence statistical methods have been used to extract the best parameters of the offset wells, and in the ROP prediction module, all data mining methods and neural network have been used for its development.
Develop a database of cubic data and OLAP to accelerate various operations and report100%
Development of statistical methods and advanced artificial intelligence for extracting the best parameters of offset wells100%
Development of data mining methods for grouping different wells100%
Developing neural network methods to accurately predict the penetration rate (ROP)100%
Using optimization algorithms to optimize various parameters100%
The main basis of the software is the use of artificial intelligence, machine learning, and optimization algorithms to achieve optimal results. It should be noted that the ability of this software has been evaluated in one of the oil fields of Iran and the results are acceptable.
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