This paper presents the use of real-time microseismic (MS) monitoring to understand hydraulic fracturing of a horizontal well drilled in the minimum stress direction within a high-temperature high-pressure (HTHP) tight sandstone formation. The well achieved a reservoir contact of more than 3,500 ft. Careful planning of the monitoring well and treatment well setup enabled capture of high quality MS events resulting in useful information on the regional maximum horizontal stress and offers an understanding of the fracture geometry with respect to clusters and stage spacing in relation to fracture propagation and growth. The maximum horizontal stress based on MS events was found to be different from the expected value with fracture azimuth off by more than 25 degree among the stages. Transverse fracture propagation was observed with overlapping MS events across stages. Upward fracture height growth was dominant in tighter stages. MS fracture length and height in excess of 500 ft and 100 ft, respectively, were created for most of the stages resulting in stimulated volumes that are high. Bigger fracture jobs yielded longer fracture length and were more confined in height growth. MS events fracture lengths and heights were found to be on average 1.36 and 1.30 times, respectively, to those of pressure-match.
{"title":"Utilizing Microseismic to Monitor Fracture Geometry in a Horizontal Well in a Tight Sandstone Formation","authors":"A. M. Sani, Hatim S. AlQasim, Rayan A. Alidi","doi":"10.2118/204736-ms","DOIUrl":"https://doi.org/10.2118/204736-ms","url":null,"abstract":"\u0000 This paper presents the use of real-time microseismic (MS) monitoring to understand hydraulic fracturing of a horizontal well drilled in the minimum stress direction within a high-temperature high-pressure (HTHP) tight sandstone formation. The well achieved a reservoir contact of more than 3,500 ft. Careful planning of the monitoring well and treatment well setup enabled capture of high quality MS events resulting in useful information on the regional maximum horizontal stress and offers an understanding of the fracture geometry with respect to clusters and stage spacing in relation to fracture propagation and growth. The maximum horizontal stress based on MS events was found to be different from the expected value with fracture azimuth off by more than 25 degree among the stages. Transverse fracture propagation was observed with overlapping MS events across stages. Upward fracture height growth was dominant in tighter stages. MS fracture length and height in excess of 500 ft and 100 ft, respectively, were created for most of the stages resulting in stimulated volumes that are high. Bigger fracture jobs yielded longer fracture length and were more confined in height growth. MS events fracture lengths and heights were found to be on average 1.36 and 1.30 times, respectively, to those of pressure-match.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84070243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drilling oil and gas wells with stable and good quality wellbores is essential to minimize drilling difficulties, acquire reliable openhole logs data, run completions and ensure well integrity during stimulation. Stress-induced compressive rock failure leading to enlarged wellbore is a common form of wellbore instability especially in tectonic stress regime. For a particular well trajectory, wellbore stability is generally considered a result of an interplay between drilling mud density (i.e., mud weight) and subsurface geomechanical parameters including in-situ earth stresses, formation pore pressure and rock strength properties. While role of mud system and chemistry can also be important for water sensitive formations, mud weight is always a fundamental component of wellbore stability analysis. Hence, when a wellbore is unstable (over-gauge), it is believed that effective mud support was insufficient to counter stress concentration around wellbore wall. Therefore, increasing mud weight based on model validation and calibration using offset wells data is a common approach to keep wellbore stable. However, a limited number of research articles show that wellbore stability is a more complex phenomenon affected not only by geomechanics but also strongly influenced by downhole forces exerted by drillstring vibrations and high mud flow rates. Authors of this paper also observed that some wells drilled with higher mud weight exhibit more unstable wellbore in comparison with offset wells which contradicts the conventional approach of linking wellbore stability to stresses and rock strength properties alone. Therefore, the objective of this paper is to analyze wellbore stability considering both geomechanical and drilling parameters to explain observed anomalous wellbore enlargements in two vertical wells drilled in the same field and reservoir. The analysis showed that the well drilled with 18% higher mud weight compared with its offset well and yet showing more unstable wellbore was, in fact, drilled with more aggressive drilling parameters. The aggressive drilling parameters induce additional mechanical disturbance to the wellbore wall causing more severe wellbore enlargements. We devised a new approach of wellbore stability management using two-pronged strategy. It focuses on designing an optimum weight design using geomechanics to address stress-induced wellbore failure together with specifying safe limits of drilling parameters to minimize wellbore damage due to excessive downhole drillstring vibrations. The findings helped achieve more stable wellbore in subsequent wells with hole condition meeting logging and completion requirements as well as avoiding drilling problems.
{"title":"Wellbore Stability Beyond Mud Weight","authors":"Khaqan Khan, M. Altwaijri, Sajjad Ahmed","doi":"10.2118/204860-ms","DOIUrl":"https://doi.org/10.2118/204860-ms","url":null,"abstract":"\u0000 Drilling oil and gas wells with stable and good quality wellbores is essential to minimize drilling difficulties, acquire reliable openhole logs data, run completions and ensure well integrity during stimulation. Stress-induced compressive rock failure leading to enlarged wellbore is a common form of wellbore instability especially in tectonic stress regime. For a particular well trajectory, wellbore stability is generally considered a result of an interplay between drilling mud density (i.e., mud weight) and subsurface geomechanical parameters including in-situ earth stresses, formation pore pressure and rock strength properties. While role of mud system and chemistry can also be important for water sensitive formations, mud weight is always a fundamental component of wellbore stability analysis. Hence, when a wellbore is unstable (over-gauge), it is believed that effective mud support was insufficient to counter stress concentration around wellbore wall. Therefore, increasing mud weight based on model validation and calibration using offset wells data is a common approach to keep wellbore stable.\u0000 However, a limited number of research articles show that wellbore stability is a more complex phenomenon affected not only by geomechanics but also strongly influenced by downhole forces exerted by drillstring vibrations and high mud flow rates. Authors of this paper also observed that some wells drilled with higher mud weight exhibit more unstable wellbore in comparison with offset wells which contradicts the conventional approach of linking wellbore stability to stresses and rock strength properties alone. Therefore, the objective of this paper is to analyze wellbore stability considering both geomechanical and drilling parameters to explain observed anomalous wellbore enlargements in two vertical wells drilled in the same field and reservoir.\u0000 The analysis showed that the well drilled with 18% higher mud weight compared with its offset well and yet showing more unstable wellbore was, in fact, drilled with more aggressive drilling parameters. The aggressive drilling parameters induce additional mechanical disturbance to the wellbore wall causing more severe wellbore enlargements. We devised a new approach of wellbore stability management using two-pronged strategy. It focuses on designing an optimum weight design using geomechanics to address stress-induced wellbore failure together with specifying safe limits of drilling parameters to minimize wellbore damage due to excessive downhole drillstring vibrations. The findings helped achieve more stable wellbore in subsequent wells with hole condition meeting logging and completion requirements as well as avoiding drilling problems.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86611556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Al Gharbi, A. Al-Majed, A. Abdulraheem, S. Patil, S. Elkatatny
Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.
{"title":"Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques","authors":"S. Al Gharbi, A. Al-Majed, A. Abdulraheem, S. Patil, S. Elkatatny","doi":"10.2118/204658-ms","DOIUrl":"https://doi.org/10.2118/204658-ms","url":null,"abstract":"\u0000 Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations.\u0000 To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality.\u0000 To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87544817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uchenna Odi, K. Ayeni, Nouf Alsulaiman, Karri Reddy, Kathy Ball, Mustafa A. Basri, C. Temizel
There are documented cases of machine learning being applied to different segments of the oil and gas industry with different levels of success. These successes have not been readily transferred to production forecasting for unconventional oil and gas reservoirs because of sparsity of production data at the early stage of production. Sparsity of unconventional production data is a challenge, but transfer learning can mitigate this challenge. Application of machine learning for production forecasting is challenging in areas with insufficient data. Transfer learning makes it possible to carry over the information gathered from well-established areas with rich data to areas with relatively limited data. This study outlines the background theory along with the application of transfer learning in unconventionals to aid in production forecasting. Similarity metrics are utilized in finding candidates for transfer learning by using key drivers for reservoir performance. Key drivers include similar reservoir mechanisms and subsurface structures. After training the model on a related field with rich data, most of the primary parameters learned and stored in a representative machine or deep learning model can be re-used in a transfer learning manner. By employing the already learned basic features, models with sparse data have been enriched by using transfer learning. The approach has been outlined in a stepwise manner with details. With the help of the insights transferred from related sites with rich data, the uncertainty in production forecasting has decreased, and the accuracy of the predictions increased. As a result, the details of selecting a related site to be used for transfer learning along with the challenges and steps in achieving the forecasts have been outlined in detail. There are limited studies in oil and gas literature on transfer learning for oil and gas reservoirs. If applied with care, it is a powerful method for increasing the success of models with sparse data. This study uses transfer learning to encapsulate the basics of the substructure of a well-known area and uses this information to empower the model. This study investigates the application to unconventional shale reservoirs, which have limited studies on transfer learning.
{"title":"Applied Transfer Learning for Production Forecasting in Shale Reservoirs","authors":"Uchenna Odi, K. Ayeni, Nouf Alsulaiman, Karri Reddy, Kathy Ball, Mustafa A. Basri, C. Temizel","doi":"10.2118/204784-ms","DOIUrl":"https://doi.org/10.2118/204784-ms","url":null,"abstract":"\u0000 There are documented cases of machine learning being applied to different segments of the oil and gas industry with different levels of success. These successes have not been readily transferred to production forecasting for unconventional oil and gas reservoirs because of sparsity of production data at the early stage of production. Sparsity of unconventional production data is a challenge, but transfer learning can mitigate this challenge.\u0000 Application of machine learning for production forecasting is challenging in areas with insufficient data. Transfer learning makes it possible to carry over the information gathered from well-established areas with rich data to areas with relatively limited data. This study outlines the background theory along with the application of transfer learning in unconventionals to aid in production forecasting. Similarity metrics are utilized in finding candidates for transfer learning by using key drivers for reservoir performance. Key drivers include similar reservoir mechanisms and subsurface structures. After training the model on a related field with rich data, most of the primary parameters learned and stored in a representative machine or deep learning model can be re-used in a transfer learning manner. By employing the already learned basic features, models with sparse data have been enriched by using transfer learning. The approach has been outlined in a stepwise manner with details.\u0000 With the help of the insights transferred from related sites with rich data, the uncertainty in production forecasting has decreased, and the accuracy of the predictions increased. As a result, the details of selecting a related site to be used for transfer learning along with the challenges and steps in achieving the forecasts have been outlined in detail.\u0000 There are limited studies in oil and gas literature on transfer learning for oil and gas reservoirs. If applied with care, it is a powerful method for increasing the success of models with sparse data. This study uses transfer learning to encapsulate the basics of the substructure of a well-known area and uses this information to empower the model. This study investigates the application to unconventional shale reservoirs, which have limited studies on transfer learning.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89493088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maoshan Chen, Z. Wan, Changhong Wang, Jingyan Liu, Zhao Chen
Due to the rapid increase in the amount of seismic volumes, the traditional seismic interpretation mode based on manual structure interpretation and single-horizon automatic tracking has encountered many challenges. The seismic interpretation of large or super-large 3-D seismic surveys is facing serious accuracy and efficiency bottlenecks. Aiming to the goal of improving the accuracy and efficiency of seismic interpretation, we propose a dynamic seismic waveform matching technology based on the sparse dynamic time warping algorithm under the guidance of the relative geological time volume theory, and realize multi-horizon simultaneous tracking based on the technology. Has been verified by a model and a real seismic volume, it can realize simultaneous horizon automatic tracking, full spatial tracking and high-density tracking, and can significantly improve the accuracy and efficiency of structure interpretation.
{"title":"Multi-horizon Simultaneous Tracking Based on Dynamic Seismic Waveform Matching","authors":"Maoshan Chen, Z. Wan, Changhong Wang, Jingyan Liu, Zhao Chen","doi":"10.2118/204759-ms","DOIUrl":"https://doi.org/10.2118/204759-ms","url":null,"abstract":"\u0000 Due to the rapid increase in the amount of seismic volumes, the traditional seismic interpretation mode based on manual structure interpretation and single-horizon automatic tracking has encountered many challenges. The seismic interpretation of large or super-large 3-D seismic surveys is facing serious accuracy and efficiency bottlenecks. Aiming to the goal of improving the accuracy and efficiency of seismic interpretation, we propose a dynamic seismic waveform matching technology based on the sparse dynamic time warping algorithm under the guidance of the relative geological time volume theory, and realize multi-horizon simultaneous tracking based on the technology. Has been verified by a model and a real seismic volume, it can realize simultaneous horizon automatic tracking, full spatial tracking and high-density tracking, and can significantly improve the accuracy and efficiency of structure interpretation.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85126443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Hati, Hemlata Chawla, A. Ghosh, U. Guru, R. Guru
The present study attempts to use 3D slowness time coherence (STC) technique to characterize the far-field fractures based on the reflector locations and attributes such as the dip and azimuth of fractures. These, in integration with the rest of the available data are used to accurately characterize the producing horizons in fractured basement reservoirs. The first step of the workflow involves the generation of 2D image to see if there are evidences of near and far wellbore reflectors. Since this is subjective in nature and does not directly provide quantitative results for discrete reflections, a new automated sonic imaging technique – 3D slowness time coherence (STC), has been incorporated to address this challenge. This method complements the image by providing the dip and azimuth for each event. The 2D and 3D maps of the reflectors can be readily available to integrate with the interpretations provided by other measurements, to better correlate and map the producing horizons. A field example is presented from the western offshore, India in which a fractured basement reservoir was examined using 3D STC technique to provide insight to the near and far field fracture network around the borehole. Few of the interpreted fractures from the resistivity image and conventional sonic fracture analysis coincide with the far field 3D STC reflectors, indicated by significant acoustic impedance. Further, the zones where the near and far field events coincide, represent a producing horizon. Comparing the near wellbore structures from the borehole images with the reflectors identified through the far field sonic imaging workflow provides necessary information to confirm the structural setting and characteristics of fractures away from the borehole. For the present case, it indicates the continuity of the fracture network away from the wellbore and explains the possibility of high production from the reservoir horizon. This study opens new perspective for identifying and evaluating fractured basement reservoirs using the sonic imaging technique. As more wells are drilled, it will be possible to better correlate and map the producing horizons in the field. This will allow better planning of location of future wells and help in optimizing field economics. A robust, automated and synergistic approach is used to locate and characterize individual arrival events which allows a more reliable understanding of the fracture extent and geologic structures. The 2D and 3D visualizations/maps can be readily integrated with the interpretations provided by other measurements.
{"title":"Identifying Producing Horizons in Fractured Reservoirs a Far Field Acoustic Approach","authors":"S. Hati, Hemlata Chawla, A. Ghosh, U. Guru, R. Guru","doi":"10.2118/204614-ms","DOIUrl":"https://doi.org/10.2118/204614-ms","url":null,"abstract":"\u0000 The present study attempts to use 3D slowness time coherence (STC) technique to characterize the far-field fractures based on the reflector locations and attributes such as the dip and azimuth of fractures. These, in integration with the rest of the available data are used to accurately characterize the producing horizons in fractured basement reservoirs.\u0000 The first step of the workflow involves the generation of 2D image to see if there are evidences of near and far wellbore reflectors. Since this is subjective in nature and does not directly provide quantitative results for discrete reflections, a new automated sonic imaging technique – 3D slowness time coherence (STC), has been incorporated to address this challenge. This method complements the image by providing the dip and azimuth for each event. The 2D and 3D maps of the reflectors can be readily available to integrate with the interpretations provided by other measurements, to better correlate and map the producing horizons.\u0000 A field example is presented from the western offshore, India in which a fractured basement reservoir was examined using 3D STC technique to provide insight to the near and far field fracture network around the borehole. Few of the interpreted fractures from the resistivity image and conventional sonic fracture analysis coincide with the far field 3D STC reflectors, indicated by significant acoustic impedance. Further, the zones where the near and far field events coincide, represent a producing horizon.\u0000 Comparing the near wellbore structures from the borehole images with the reflectors identified through the far field sonic imaging workflow provides necessary information to confirm the structural setting and characteristics of fractures away from the borehole. For the present case, it indicates the continuity of the fracture network away from the wellbore and explains the possibility of high production from the reservoir horizon.\u0000 This study opens new perspective for identifying and evaluating fractured basement reservoirs using the sonic imaging technique. As more wells are drilled, it will be possible to better correlate and map the producing horizons in the field. This will allow better planning of location of future wells and help in optimizing field economics.\u0000 A robust, automated and synergistic approach is used to locate and characterize individual arrival events which allows a more reliable understanding of the fracture extent and geologic structures. The 2D and 3D visualizations/maps can be readily integrated with the interpretations provided by other measurements.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75227137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert W. Adams, Jinjiang Xiao, M. Cross, M. Deffenbaugh
Switched reluctance motors may be advantageous when used as the primary motor for an electric submersible pump system. They are less susceptible to jamming failures due to their high starting torque and ability to reverse direction. Driving these motors requires well-timed pulse waveforms and precise control of the motor based on its rotational position. It is demonstrated that the pulses required to drive switched reluctance motors can still be applied over along cable lengths. Additionally, the current at the surface can be used to monitor and control the operation of the motor downhole, even with long cable lengths separating the surface power source and downhole motor.
{"title":"Switched Reluctance Motor for Electric Submersible Pump","authors":"Robert W. Adams, Jinjiang Xiao, M. Cross, M. Deffenbaugh","doi":"10.2118/204720-ms","DOIUrl":"https://doi.org/10.2118/204720-ms","url":null,"abstract":"\u0000 Switched reluctance motors may be advantageous when used as the primary motor for an electric submersible pump system. They are less susceptible to jamming failures due to their high starting torque and ability to reverse direction. Driving these motors requires well-timed pulse waveforms and precise control of the motor based on its rotational position. It is demonstrated that the pulses required to drive switched reluctance motors can still be applied over along cable lengths. Additionally, the current at the surface can be used to monitor and control the operation of the motor downhole, even with long cable lengths separating the surface power source and downhole motor.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73329498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Saha, Prajit Chakrabarti, Johannes Vossen, S. Mitra, T. Podder
This paper discusses the Integrated Role of Geomechanics and Drilling Fluids Design for drilling a well oriented towards the minimum horizontal stress direction in a depleted, yet highly stressed and complex clastic reservoir. There are multiple challenges related to such a well that need to be addressed during the planning phase. In this case, the well needs to be drilled towards the minimum horizontal stress direction (Shmin) to benefit multi-stage hydraulic fracturing. At the same time, the most prominent challenge is that this well orientation is more prone to wellbore failure and requires a maximum mud weight, due to the present strike slip stress environment. Well planning challenges in such an environment include (a) the determination of formation characteristics and rock properties, (b) the anticipation of higher formation collapse pressure during the course of drilling the lateral section within the reservoir, (c) the determination of the upper bound mud weight to prevent lost circulation due to a low fracture gradient against depleted sections, or due to the presence of pre-existing natural fractures, d) mitigating the higher risk of differential sticking against depleted porous layers, and determining appropriate bridging in the drilling fluids, (e) recognizing the prolonged exposure time of the formation due to the length of the lateral and the lower rate of penetration against the tight highly dense formations. For successful drilling, and to mitigate the above risks, the first step is to prepare a predrill GeoMechanical model along with adequate fluid design and drillers action plans to be considered during drilling. Offset well petrophysical logs and core data are considered for the preparation of the predrill GeoMechanical model, along with the drilling experiences in the offset locations. Based on the above, a predrill GeoMechanical model is prepared, a risk matrix is being established, and a representative mud weight window is recommended (Wellbore Stability Analysis). In most cases, the offset well locations considered are vertical- or inclined-, or lateral wells of different trajectory azimuth than the target well location and the predrill GeoMechanical model can incorporate such variations easily; however, any Geology uncertainty, leading to a different rock property- and stress set-up (or even different pore pressure than expected), at the actual well location will be part of the uncertainty of the predrill GeoMechanical model and Wellbore Stability Analysis. This is where the real time monitoring is playing out its full potential: giving an updated model and wellbore stability analysis during drilling. While drilling the lateral section, the wellbore condition is being monitored using LWD (logging while drilling) tools, e.g. Gamma Ray, Density, Neutron, Acoustic Caliper, Azimuthal density image and ECD (equivalent circulating density). While gamma ray helps in determining the lithology, density logs help to understand
{"title":"Proactive Decision-Making Through Real-Time Geomechanical Support Leveraging Drilling a Long Horizontal Section Through a Tight Unconventional Reservoir of an Oil and Gas Field: Middle East","authors":"S. Saha, Prajit Chakrabarti, Johannes Vossen, S. Mitra, T. Podder","doi":"10.2118/204776-ms","DOIUrl":"https://doi.org/10.2118/204776-ms","url":null,"abstract":"\u0000 This paper discusses the Integrated Role of Geomechanics and Drilling Fluids Design for drilling a well oriented towards the minimum horizontal stress direction in a depleted, yet highly stressed and complex clastic reservoir. There are multiple challenges related to such a well that need to be addressed during the planning phase. In this case, the well needs to be drilled towards the minimum horizontal stress direction (Shmin) to benefit multi-stage hydraulic fracturing. At the same time, the most prominent challenge is that this well orientation is more prone to wellbore failure and requires a maximum mud weight, due to the present strike slip stress environment.\u0000 Well planning challenges in such an environment include (a) the determination of formation characteristics and rock properties, (b) the anticipation of higher formation collapse pressure during the course of drilling the lateral section within the reservoir, (c) the determination of the upper bound mud weight to prevent lost circulation due to a low fracture gradient against depleted sections, or due to the presence of pre-existing natural fractures, d) mitigating the higher risk of differential sticking against depleted porous layers, and determining appropriate bridging in the drilling fluids, (e) recognizing the prolonged exposure time of the formation due to the length of the lateral and the lower rate of penetration against the tight highly dense formations.\u0000 For successful drilling, and to mitigate the above risks, the first step is to prepare a predrill GeoMechanical model along with adequate fluid design and drillers action plans to be considered during drilling. Offset well petrophysical logs and core data are considered for the preparation of the predrill GeoMechanical model, along with the drilling experiences in the offset locations. Based on the above, a predrill GeoMechanical model is prepared, a risk matrix is being established, and a representative mud weight window is recommended (Wellbore Stability Analysis). In most cases, the offset well locations considered are vertical- or inclined-, or lateral wells of different trajectory azimuth than the target well location and the predrill GeoMechanical model can incorporate such variations easily; however, any Geology uncertainty, leading to a different rock property- and stress set-up (or even different pore pressure than expected), at the actual well location will be part of the uncertainty of the predrill GeoMechanical model and Wellbore Stability Analysis. This is where the real time monitoring is playing out its full potential: giving an updated model and wellbore stability analysis during drilling.\u0000 While drilling the lateral section, the wellbore condition is being monitored using LWD (logging while drilling) tools, e.g. Gamma Ray, Density, Neutron, Acoustic Caliper, Azimuthal density image and ECD (equivalent circulating density). While gamma ray helps in determining the lithology, density logs help to understand ","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"218 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77694699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper illustrates the value of seismic data in different environments after assessing the benefits and costs of processes such as seismic acquisition, seismic processing and seismic interpretation. Global examples from conventional and unconventional fields are discussed to show how seismic data plays a significant role in determining low-risk and high-reward wells and also eliminating the high-risk and low-reward wells. This paper shows an example of a conventional field in the state of Kansas, USA, where the net present value (NPV) increased by more than 17 times when 3D seismic data was acquired, while in an unconventional field the commercial success rate rose from 30% to 70% due to 3D seismic acquisition. However, two offshore fields in the Republic of Trinidad and Tobago are discussed to show that the NPV as impacted by advanced seismic processing was more than 111 ($M). Another example comes from Viking Field, a conventional field in Canada, where the NPV was increased from 3800 ($M) to 5000 ($M) when the seismic data was re-processed. Furthermore, the value of investing in seismic data was investigated and quantified by comparing two synthetically modeled scenarios in Saudi Arabia. Overall, the four examples from North America, Central America and Saudi Arabia illustrate that investment in seismic data has a positive impact on both conventional and unconventional fields. That provides strong evidence to encourage more investments in geophysical technologies.
{"title":"The Value of Seismic Data in Conventional and Unconventional Fields","authors":"Nawaf M. Alghamdi, H. Alghenaim","doi":"10.2118/204715-ms","DOIUrl":"https://doi.org/10.2118/204715-ms","url":null,"abstract":"\u0000 The paper illustrates the value of seismic data in different environments after assessing the benefits and costs of processes such as seismic acquisition, seismic processing and seismic interpretation. Global examples from conventional and unconventional fields are discussed to show how seismic data plays a significant role in determining low-risk and high-reward wells and also eliminating the high-risk and low-reward wells.\u0000 This paper shows an example of a conventional field in the state of Kansas, USA, where the net present value (NPV) increased by more than 17 times when 3D seismic data was acquired, while in an unconventional field the commercial success rate rose from 30% to 70% due to 3D seismic acquisition. However, two offshore fields in the Republic of Trinidad and Tobago are discussed to show that the NPV as impacted by advanced seismic processing was more than 111 ($M). Another example comes from Viking Field, a conventional field in Canada, where the NPV was increased from 3800 ($M) to 5000 ($M) when the seismic data was re-processed. Furthermore, the value of investing in seismic data was investigated and quantified by comparing two synthetically modeled scenarios in Saudi Arabia.\u0000 Overall, the four examples from North America, Central America and Saudi Arabia illustrate that investment in seismic data has a positive impact on both conventional and unconventional fields. That provides strong evidence to encourage more investments in geophysical technologies.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85445760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the current oil and gas drilling industry, the modernization of rig fleets has been shifting toward high mobility, artificial intelligence, and computerized systems. Part of this shift includes a move toward automation. This paper summarizes the successful application of a fully automated workflow to drill a stand, from slips out to slips back in, in a complex drilling environment in onshore gas. Repeatable processes with adherence to plans and operating practices are a key requirement in the implementation of drilling procedures and vital for optimizing operations in a systematic way. A drilling automation solution has been deployed in two rigs enabling the automation of both pre-connection and post-connection activities as well as rotary drilling of an interval equivalent to a typical drillpipe stand (approximately 90 ft) while optimizing the rate of penetration (ROP) and managing drilling dysfunctionalities, such as stick-slip and drillstring vibrations in a consistent manner. So far, a total of nine wells have been drilled using this solution. The automation system is configured with the outputs of the drilling program, including the drilling parameters roadmap, bottomhole assembly tools, and subsurface constraints. Before drilling every stand, the driller is presented with the planned configuration and can adjust settings whenever necessary. Once a goal is specified, the system directs the rig control system to command the surface equipment (draw works, auto-driller, top drive, and pumps). Everything is undertaken in the context of a workflow that reflects standard operating procedures. This solution runs with minimal intervention from the driller and each workflow contextual information is continuously displayed to the driller thereby giving him the best capacity to monitor and supervise the operational sequence. If drilling conditions change, the system will respond by automatically changing the sequence of activities to execute mitigation procedures and achieve the desired goal. At all times, the driller has the option to override the automation system and assume control by a simple touch on the rig controls. Prior to deployment, key performance indicators (KPI), including automated rig state-based measures, were selected. These KPIs are then monitored while drilling each well with the automation system to compare performance with a pre-deployment baseline. The solution was used to drill almost 60,000 ft of hole section with the system in control, and the results showed a 20% improvement in ROP with increased adherence to pre-connection and post-connection operations. Additionally, many lessons were learned from the use and observation of the automation workflow that was used to drive continuous improvement in efficiency and performance over the course of the project. This deployment was the first in the region and the system is part of a comprehensive digital well construction solution that is continuously enriched with new capa
{"title":"Drilling Automation: The Step Forward for Improving Safety, Consistency, and Performance in Onshore Gas Drilling","authors":"E. Gomez, E. Ombe, B. Goodkey, R. Carvalho","doi":"10.2118/204849-ms","DOIUrl":"https://doi.org/10.2118/204849-ms","url":null,"abstract":"\u0000 In the current oil and gas drilling industry, the modernization of rig fleets has been shifting toward high mobility, artificial intelligence, and computerized systems. Part of this shift includes a move toward automation. This paper summarizes the successful application of a fully automated workflow to drill a stand, from slips out to slips back in, in a complex drilling environment in onshore gas.\u0000 Repeatable processes with adherence to plans and operating practices are a key requirement in the implementation of drilling procedures and vital for optimizing operations in a systematic way. A drilling automation solution has been deployed in two rigs enabling the automation of both pre-connection and post-connection activities as well as rotary drilling of an interval equivalent to a typical drillpipe stand (approximately 90 ft) while optimizing the rate of penetration (ROP) and managing drilling dysfunctionalities, such as stick-slip and drillstring vibrations in a consistent manner. So far, a total of nine wells have been drilled using this solution.\u0000 The automation system is configured with the outputs of the drilling program, including the drilling parameters roadmap, bottomhole assembly tools, and subsurface constraints. Before drilling every stand, the driller is presented with the planned configuration and can adjust settings whenever necessary. Once a goal is specified, the system directs the rig control system to command the surface equipment (draw works, auto-driller, top drive, and pumps). Everything is undertaken in the context of a workflow that reflects standard operating procedures. This solution runs with minimal intervention from the driller and each workflow contextual information is continuously displayed to the driller thereby giving him the best capacity to monitor and supervise the operational sequence. If drilling conditions change, the system will respond by automatically changing the sequence of activities to execute mitigation procedures and achieve the desired goal. At all times, the driller has the option to override the automation system and assume control by a simple touch on the rig controls.\u0000 Prior to deployment, key performance indicators (KPI), including automated rig state-based measures, were selected. These KPIs are then monitored while drilling each well with the automation system to compare performance with a pre-deployment baseline. The solution was used to drill almost 60,000 ft of hole section with the system in control, and the results showed a 20% improvement in ROP with increased adherence to pre-connection and post-connection operations. Additionally, many lessons were learned from the use and observation of the automation workflow that was used to drive continuous improvement in efficiency and performance over the course of the project.\u0000 This deployment was the first in the region and the system is part of a comprehensive digital well construction solution that is continuously enriched with new capa","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86959272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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