How Minimum Interval Concept Can Improve Bit Performance Evaluation
Abstract: The paper analyzes a novel operative procedure of geological and geomechanical subdivisions of the formations, called Minimum Interval (MI). The MI concept, used in combination with other evaluation indexes, can improve consistently the evaluation of bit performances. This research compares three indexes for bit performance evaluation, i.e., cost-per-meter (CM), Specific Energy (SE) and Bit Index (BI), applied to a new type of drilling-bit database, named Formation Drillability Catalog (FDC), organized in orde… Show more
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Cited by 3 publications
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The paper is aimed to calculate an innovative numerical index for bit performance evaluation called Bit Index (BI), applied on a new type of formation and bit database also known as Formation Drillability Catalogue (FDC). A dedicated research program developed by Eni E&P Div. and the University of Bologna (Italy) studied a drilling model for drill-bit performance evaluation (the BI) derived from data recorded while drilling (bit records, master log, wireline log, etc.) that includes numerical information from dull bit evaluation. This index is calculated with data collected inside the FDC, a novel classification of formations aimed to the geotechnical and geomechanical characterization and subdivisions of the formations, called Minimum Interval (MI). FDC was conceived and prepared at Eni E&P Div., and contains a large number of significant drilling parameters. Some wells have been identified inside the FDC and have been tested for bit performance evaluation. The values of BI are calculated for each bit run and are compared with the values of the cost per meter. The case study analyzes bits of the same typology and run in the same formation. The BI methodology implemented on MI classification of FDC can improve consistently the bit performances evaluation, and it helps to identify the best performer bits. Moreover, FDC turned out to be particularly functional to BI, since it discloses and organizes many formation details that are not easily detectable or usable from bit records or master logs, allowing for targeted bit performance evaluations. At this stage of development, the BI methodology proved to be economic and reliable, helping as well to make drill bit selection less erroneous and more logical. Introduction Drill-bit performance evaluation is one of the most important issues of drilling optimization. When optimizing bit performances, drilling engineers ordinarily think in terms of factors affecting the rate of penetration, and so indirectly correlate bit performances to cost-per-meter only. This criterion, however, cannot by itself show where improvement is needed. Nowadays, drill-bit performance evaluation methods involve the development of mathematical tools along with the analysis of large number of information from various records of previously drilled wells, even though to date there is not an industry standard or a widely used methodology to address it. Bit performance evaluation and bit selection is of paramount importance for the drilling engineer. Many attempts to model and predict drill-bit selection and to evaluate their performances have been developed for a number of years. As advancements in drilling bit technology and drilling measurement systems have increased, so have the number and the sophistication of evaluation models available. Almost all the approaches are similar in objective, to make drilling bit selection less erroneous and more logical 1 and possibly free from any personal interpretation. Recently, the methods for the evaluation of bit performance improvements have not always kept the pace with technological advancements. Methods such as offset wells and cost per foot, specific energy, bit factor, formation drillability, design index and neural networks have been used for bit performance evaluation 2–10, although most of them still seem not completely satisfactory. The industry is unlikely to find a means to predict drilling without a means of relating it to some rock criteria. This study illustrates the application of an innovative mathematical tool (jointly developed by Eni E&P Div. and the University of Bologna) based on Bit Index implemented on data collected in Eni's Formation Drillability Catalogue 11–12, a data collection addressed to the geological and geomechanical classification of the formations. The method proposes a new way for using the existing information under day-to-day competitive drilling circumstances, accomplishing the desired objective at the lowest cost through the use of the most effective procedures. Finally, the paper is completed with a case study analyzing some drill-bit performances in Val d'Agri area (Southern Italy). In particular, here is presented both an application concerning the evaluation of top hole drilling in a heterogeneous formation with hard stringers, and a case of a deep and hard reservoir drilling, where a new generation of impregnated bits has been recently tested.
The paper is aimed to calculate an innovative numerical index for bit performance evaluation called Bit Index (BI), applied on a new type of formation and bit database also known as Formation Drillability Catalogue (FDC). A dedicated research program developed by Eni E&P Div. and the University of Bologna (Italy) studied a drilling model for drill-bit performance evaluation (the BI) derived from data recorded while drilling (bit records, master log, wireline log, etc.) that includes numerical information from dull bit evaluation. This index is calculated with data collected inside the FDC, a novel classification of formations aimed to the geotechnical and geomechanical characterization and subdivisions of the formations, called Minimum Interval (MI). FDC was conceived and prepared at Eni E&P Div., and contains a large number of significant drilling parameters. Some wells have been identified inside the FDC and have been tested for bit performance evaluation. The values of BI are calculated for each bit run and are compared with the values of the cost per meter. The case study analyzes bits of the same typology and run in the same formation. The BI methodology implemented on MI classification of FDC can improve consistently the bit performances evaluation, and it helps to identify the best performer bits. Moreover, FDC turned out to be particularly functional to BI, since it discloses and organizes many formation details that are not easily detectable or usable from bit records or master logs, allowing for targeted bit performance evaluations. At this stage of development, the BI methodology proved to be economic and reliable, helping as well to make drill bit selection less erroneous and more logical. Introduction Drill-bit performance evaluation is one of the most important issues of drilling optimization. When optimizing bit performances, drilling engineers ordinarily think in terms of factors affecting the rate of penetration, and so indirectly correlate bit performances to cost-per-meter only. This criterion, however, cannot by itself show where improvement is needed. Nowadays, drill-bit performance evaluation methods involve the development of mathematical tools along with the analysis of large number of information from various records of previously drilled wells, even though to date there is not an industry standard or a widely used methodology to address it. Bit performance evaluation and bit selection is of paramount importance for the drilling engineer. Many attempts to model and predict drill-bit selection and to evaluate their performances have been developed for a number of years. As advancements in drilling bit technology and drilling measurement systems have increased, so have the number and the sophistication of evaluation models available. Almost all the approaches are similar in objective, to make drilling bit selection less erroneous and more logical 1 and possibly free from any personal interpretation. Recently, the methods for the evaluation of bit performance improvements have not always kept the pace with technological advancements. Methods such as offset wells and cost per foot, specific energy, bit factor, formation drillability, design index and neural networks have been used for bit performance evaluation 2–10, although most of them still seem not completely satisfactory. The industry is unlikely to find a means to predict drilling without a means of relating it to some rock criteria. This study illustrates the application of an innovative mathematical tool (jointly developed by Eni E&P Div. and the University of Bologna) based on Bit Index implemented on data collected in Eni's Formation Drillability Catalogue 11–12, a data collection addressed to the geological and geomechanical classification of the formations. The method proposes a new way for using the existing information under day-to-day competitive drilling circumstances, accomplishing the desired objective at the lowest cost through the use of the most effective procedures. Finally, the paper is completed with a case study analyzing some drill-bit performances in Val d'Agri area (Southern Italy). In particular, here is presented both an application concerning the evaluation of top hole drilling in a heterogeneous formation with hard stringers, and a case of a deep and hard reservoir drilling, where a new generation of impregnated bits has been recently tested.
Drilling operations became more expensive and complicated day by day due to many reasons affecting directly the daily drilling cost. One of the most effective cost reductions was the fixed cutter bit solution which effectively achieved higher drilling progress and reduces overall well cost. Also, in some cases PDC bit may raise the well cost due to slow down in the penetration rate or stopped drilling to retrieve mechanical parts from the hole due to bit fatigue and followed by extra trips for junking or fishing operations. The present study focuses on most of the factors affecting fixed cutter bit design, drilling parameters that influence the bit cutting structure wear and led to the bit poor progress. All previous PDC bit mathematical models used before for determining the cutters wear value not considered real methodology on rig site to assure cutters wear and gives a proper decision to terminate the PDC bit run. Study investigated four mathematical models had calculated the PDC cutters wear using the influences of the rock strength, rock temperature and mechanical drilling parameters. These models are theoretical and applied within the lab test devices, most of models not achieved significant benefit to use in rig site applications. New model had developed to compute PDC wear value as function of surface torque arises from the friction between the drill string, bit interaction with well bore and rock on bottom. Both resistance created by the string and bit are converted to output data realized by the gauge in front the driller in rig floor and mud logging unit. Analog or digital data reflects the torque obtained from rotating the bottom hole assembly (Drill pipe, string stabilizer and bit) against the wall of hole and formation strength. Mathematical model calculates the theoretical torque for string tool joint, stabilizer and PDC bit cutters allocated in nose up to gauge area. In reality, when BHA rotates off bottom torque created is representing the summation of string tool joint and stabilizer. An additional torque realizes on surface when BHA on bottom and WOB applied then this bit torque value will be compared by model bit torque. This later obtained bit torque percentage that represents the cutters wear value preoperational to cutter height. New mathematical model had created visual analog graphs will help to take decision when stop drilling and terminate the PDC bit run. Field validation test had run in two main concessions in Egypt Western desert and Gulf of Suez with total 6 PDC bit runs using different bit size, type, cutter sizes and different bottom hole assembly (Rotary-steerable). The filed results showed significant correction in cutter wear magnitude between the model calculation and field runs, this error factor less than 10% in hard formation and less than 20% in medium to soft formations. Author set the correction in model and treated in easy way to be used on field by driller and drilling engineers to help them how to determine the PDC bit cutting structure wear value.
Although visual data analytics using image processing is one of the most growing research areas today and is largely applied in many fields, it is not fully utilized in the petroleum industry. This study is inspired by medical image segmentation in detecting tumor cells. This paper uses a supervised Machine Learning technique through video analytics to identify bit dullness that can be used in the drilling industry in place of the subjective screening approach. The evaluation of bit performance can be affected by subjective evaluation of the degree of dullness. The present approach of using video analytics is able to grade bit dullness to avoid user subjectivity. The approach involves the use of datasets in good quantity and quality by separating them into training datasets, testing datasets, and validation datasets. Due to the large datasets, Google Collaboratory was used as it provides access to its Graphic Processing Unit (GPU) online for the processing of the bit datasets. The processing time and resource consumption are minimized using Google GPU. Using the Google GPU resources, the procedure is automated without any installation. After the bit is pulled out and cleaned, a video is taken around and up and down in 360°. Further, it is compared against the green bit. By this approach, multiple video datasets are not required. The algorithm was validated with new sets of bit videos and the results were satisfactory. The identification of the dullness or otherwise of each screened bit is done with the aid of a bounding box with a stamp of a level of confidence (range 0.5–1) and the algorithm assigns for its decision on the identified or screened object. This method is also able to screen multiple bits stored in a single place. In an event where several drill bits are to be screened, manual grading will be a huge task and will require a lot of resources. This model and algorithm will take a few minutes to screen and provide grading for several bits while videos are passed through the algorithm. It has also been found that the grading with the video was much better than the single image as the contextual information extracted are much higher at the level of the entire video, per segment, per shot, and per frame. Also, methodology is made robust so that the video model test starts successfully without error. The time penalty for the processing is fast and it took less time for a single video screening. The work developed here is probably the first to handle the dull bit grading using video analytics. With more of these datasets available, the future automation of the IADC bit characterization will soon evolve into an automated process.
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