Intelligent Drilling Advisory System Optimizes Performance
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In the current work, a rig advisory system is developed to continually improve rate of penetration (ROP) and drilling performance. An intelligent drilling advisory system (IDAS), based on a soft closed-loop solution with multiple regression analysis—called optimum parameters global retrieval—has been established. As an effective tool for further achieving the optimal depth of cut (DOC), the control system led to satisfactory outcomes that overcame drilling challenges in Saudi Arabia and China, thereby serving as a step toward automated drilling operations.
The IDAS was developed to inform drillers of optimal weight on bit (WOB), rotary speed, and mud-flow rate to penetrate rock and achieve higher ROP and longer bit runs. Optimized drilling parameters are calculated and updated using a soft closed-loop solution, which monitors the ROP and energy input to the bit in real time by means of a mud logger and topdrive system controller on the rig site, and calculates parameters by maintaining an optimal relationship between the ROP and energy input. If the ROP is under expectation, it automatically evaluates new conditions and updates optimal parameters by maintaining a proper relationship between the ROP and energy input. The following features of the IDAS improve drilling performance:
- A combination of evaluation factors [such as DOC and drilling specific energy (DSE)] that may be advantageous to drilling performance. Simple optimization for ROP improvement may not achieve maximum potential drilling performance given current drilling technologies and geology, and the consideration of ROP over all other factors generally leads to serious bit wear.
- The system and solutions are capable of recommending operational changes during drilling operations while the limiter occurs (referred to as a “founder point”). The limiter identification uses probability distributions to determine whether the incoming data stream falls outside of a specified estimated probability-distribution-significance level. An outlier in comparison with the probability distribution space may indicate a change in drilling conditions, allowing the driller to detect environmental changes periodically during drilling.
Optimization Mechanism and Work Flows
The relationship between ROP, DSE, and DOC creates three regions in any rock. As shown in Fig. 1, Region 2 is a highly effective cutting region; the other regions produce inadequate or excessive potential cutting performance. Tracing parameter deviation, lithology-change plots and limiters such as bit balling and vibrations can be identified in real time during drilling operations. Moreover, the parameters are updated in an optimal direction to maximize potential performance in line with Region 2.
At the start of drilling, the system gathers data related to the drilling parameters. Meanwhile, an information prompt is triggered that tells the driller to scan the overall drilling-parameter space designated in the drilling program. The specific process normally is to check whether the quantity and quality of the drilling parameters are sufficient for the subsequent statistical analysis. The requirements include at least two controllable parameters and at least one uncontrollable parameter such as bit torque to characterize downhole environment changes. Generally, the system gathers data over a certain interval depending on ROP. When the data-window length lies within some threshold amount, global optimization is triggered. Once optimal results are recommended, a decision tree is run to validate the recommendation. Once validation is passed, the result is displayed. If the validation fails, the downhole environment-detection engine is launched to check whether the bit has encountered a substantial formation change, in which case the data window is refreshed and global optimization may be restarted. Alternatively, the local retrieval process may be triggered. Finally, the optimal operating parameters are derived by the system.
Drilling Optimization Models
The complete paper discusses several models in detail, including the following:
- Rock-breaking efficiency evaluation model
- Torsional vibration evaluation model
- Drilling performance evaluation model
Global and Local Retrieval Models for Optimal Parameters
- Global retrieval model
- Downhole-environment detection model
- Local retrieval model
Real-Time Data-Gathering Module
- Provides real-time data for drilling surveillance and optimization
- Acquires real-time source data in accordance with the wellsite-information transfer-specification format and stores them in the database
- Automatically detects information regarding communications and operational status
- Provides diagnostic alarms in the form of an indicator; a continuously blinking indicator shows that the system is in communication with the data provider
Rock-Breaking Monitoring Module
- Calculates minimum mechanical specific energy (MSEmin), DOC, and DSE and correlates drilling-performance measurements in real time
- Displays parameters on the driller interface as curves to analyze the drilling process in real time
- Provides value-added benefits including timely DSE and DOC values
- Uses MSEmin as optimization criteria to evaluate drilling efficiency
- Uses data trends to alert the driller of changes in formation or other drilling conditions such as bit balling, bottom balling, and vibrations
Vibration-Strength Estimate Module
- Simulates working stress and side loads on the bottomhole assembly (BHA) before drilling begins
- Calculates critical speeds to avoid on the basis of the BHA and drilling conditions, preventing vibration damage
- Designs fit-for-purpose downhole tools
- Estimates vibration strength in real time to ensure that ROP is free from critical vibrations
- Optimizes distribution of ROP and DSE and recommends optimal operating parameters for drillers
- Provides “Configuration” menu item that includes well information, communication setup, and formation-data load
- Provides “Model Selection” menu item that allows for selection of drilling methods including mud-motor drilling, gas-hammer drilling, and reaming
- Provides “Parameter Monitor” for a primary view of real-time drilling parameters along with real-time display and calculation-parameter drop list
Field Test Cases
IDAS was applied successfully in the MX009-H9# Southwest oil and gas field in China from 7 to 22 May 2018. The bit run was 459 m from a measured depth of 3538 to 4037 m.
Performance-Evaluation Strategy. To evaluate the performance of IDAS compared with that of conventional methods, the hole section was drilled conventionally during the daytime from 0800 to 2000 while the hole section was drilled with IDAS guidance, using the same bit, BHA, mud properties, and formation at night from 2000 to 0800.
Critical-Vibration Identification and Mitigation. From 3500 to 3610 m, the formation is composed of mudstone. The initial WOB was increased from 80 to 120 kN, and the rotary speed was 50 rev/min. In IDAS, the stick/slip strength curve increased, showing the occurrence of critical stick/slip and bit bounce. As the parameters were adjusted in IDAS, energy consumption immediately declined below 500 MPa, with stick/slip strength mitigation.
Lithology-Change Identification. As the bit ran from 3980 to 3996 m, the MSEmin curve increased significantly to approximately 300 Mpa. The IDAS indicated the formation change, telling the driller to increase WOB and rotary speed or flow rate. The lithology changed from limestone to black mudstone, as shown in Fig. 2. According to actual field records, the geology changes at a depth of 3987 m. After parameter adjustment, the DSE curve decreased, as did drilling time per meter.
Bit-Wear Identification. From a depth of 4010 m, the geology changed, but, to achieve higher ROP using the same parameters, the driller did not follow the recommendations from IDAS. Although higher ROP was achieved, as a result of neglecting WOB and rotary speed, it had exceeded thresholds, and critical vibration destroyed the cutters. The degree of wear was greater than 50% when the bit was pulled out of the hole. Vibration data verified that excessive ROP does not improve rock-breaking efficiency but, on the contrary, generally comes at the expense of bit life.;
Application Results. For one geology division, 121 m was drilled using IDAS guidance, with a penetration time of 27.58 hrs and an ROP of 4.39 m/h. By contrast, 78 m was drilled conventionally, with a penetration time of 21.44 hrs and an ROP of 3.64 m/h. Meanwhile, for another geology division, 52 m was drilled using IDAS guidance, with a penetration time of 6.47 hrs and an ROP of 8.04 m/h, while 69 m was drilled conventionally, with a penetration time of 10.44 hrs and an ROP of 6.61 m/h. Moreover, the rock strength in both mauve mudstone and limestone was approximately 200 to 250 MPa. The average energy used to break the rocks was approximately 400 MPa for the IDAS guidance section, and the variation was less than 10%, indicating efficient cutting. However, the energy applied in the conventional sections varied more, with an average value of 560 MPa, nearly three times the rock strength itself. IDAS guidance showed 24.59% gains in average ROP compared with the conventionally drilled sections, and average DSE decreased 33.1%.
IDAS is an effective, convenient smart tool for ROP enhancement through monitoring bit performance. The field pilots have received impressive drilling-performance improvements in comparison with conventional drilling practices. According to pilot feedback, overwhelming ROP may induce critical vibration, sacrificing bit life and wasting input energy. The effective recommendations provided by IDAS are able to mitigate downhole vibrations and achieve the optimal correlation between ROP and energy input.
Intelligent Drilling Advisory System Optimizes Performance
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