Day 2 Wed, November 16, 2022最新文献

{"title":"Intelligent Methods for Analyzing High-Frequency Production Data to Optimize Well Operation Modes","authors":"Evgeniy Judin, A. Andrianova, T.A. Ganeev, O. Kobzar, D.O. Isaev, M.A. Polinov, Mikhail Gudilov, Aleksandr Shestakov, G. Mosyagin, Evgeniy Chadin, Artem Sirotkin, A.Yu. Chervyak, Ivan Kradinov, Artem Bagaziy, Vlas Ovechkin","doi":"10.2118/212118-ms","DOIUrl":"https://doi.org/10.2118/212118-ms","url":null,"abstract":"\u0000 The digital transformation implies a new approach to petroleum engineering. It based on the analysing of high-frequency data, automatization of business processes and the spread of artificial intelligence.\u0000 Now modern wells are highly equipped, their operation is monitored by more than 50 sensor types (pressures and temperatures in different system units, electric telemetry data from submersible equipment and etc.). More than 10,000 measurements are accumulated daily for the well. Manual processing of such a large amount of information is impossible. A petroleum engineer usually analyses average values and only when there are problems does it necessary to check high-frequency data. Cause of high frequency measurements contain valuable information, the task of developing algorithms for the automatic analysis of large amounts of field data is relevant.\u0000 In addition, oil reserves are decreasing, the physics of processes in the reservoir, well and surface facilities is becoming more complex. Highly productive oil reservoirs are being replaced with hard-to-recover reserves, oil and gas condensates and fringes. In those conditions it is highly relevant to apply advanced methods of information analysis and mathematical models.\u0000 Methods of automatic analysis of high-frequency telemetry data are at the stage of active development and introduction into technological processes in petroleum industry [1]. The article presents the solutions of various problems of petroleum engineering by using advanced methods of data analysis and shows the tools that have allowed to achieve economic effects.\u0000 The most important intraday data reviewing duties are quickly identification of down time, well mode regime optimization, preventing frow rate deviations from the planned one. The prompt decision allows to identify decrease of oil rate and cut back non-production expense.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115632558","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}

{"title":"Auto-Adaptation of a Pilot Well with the Use of Special Options for Hydrodynamic Modeling and the Results of Stationary and Non-Stationary Modeling of Gas-Liquid Flow inside the Well During PLT on COI","authors":"A. Varavva, A. Yamaletdinov, R. Apasov, R. Badgutdinov, D. Samolovov","doi":"10.2118/212104-ms","DOIUrl":"https://doi.org/10.2118/212104-ms","url":null,"abstract":"\u0000 Fields X and Y are large gas condensate fields located on the Yamal Peninsula. The fields have a multi-layer structure, at field X, it is planned to bring into development 15 formations combined into 5 development targets, at field B – 18 formations combined into 10 development targets. Separate development targets include up to 6 formations penetrated with horizontal wells. In 2021, 4 horizontal and multilateral pilot commercial development wells were drilled at the fields previously studied only with exploratory wells in 1970-1990, which helped specify the petrophysical, conceptual, geological, fluid, and reservoir simulation model of the field. The new data helped make important investment decisions and switch the project to the Implement stage. During the creation and history matching of the reservoir simulation models of these fields, the small amount of actual data and the existing significant uncertainties in the initial data required to perform multivariant calculations and integrate all field studies (results of flow testing, gas condensate studies, downhole pressure and temperature readings, as well as sludge-and-water monitor readings obtained during downhole logging with coiled tubing) to determine the unknown parameters of the penetrated formations, the share of inflow from them and to narrow down the existing ranges of uncertainties. This work helped specify the RSM – the core of forecast production profiles that formed the basis of decisions on the construction of infrastructure facilities and drilling of additional wells to meet gas production obligations in 2025-2028.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644968","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}

{"title":"Modeling and Simulation of Non-Darcy or Turbulent Flow for Oil Wells","authors":"Dachang Li, C. Ionescu, I. T. Ehighebolo, Byron Haynes Jr., Ainur Zhazbayeva, Bakyt Yergaliyeva, Luigi Francia","doi":"10.2118/212067-ms","DOIUrl":"https://doi.org/10.2118/212067-ms","url":null,"abstract":"\u0000 Modeling and simulation of non-Darcy or turbulent flow are well documented in the literature and available in commercial reservoir simulators (E300, Intersect) only for gas wells rather than oil wells. There is a need to model non-Darcy or turbulent flow in reservoir simulation for oil wells in the carbonate reservoirs with highly connected and densely distributed fractures and karst. This paper proposes a new non-Darcy or turbulent flow modeling and simulation method for oil wells. Unlike the industry's existing methods for non-Darcy or turbulent flow that focus on the non-Darcy coefficient only, this paper presents a new method that models the ratio between non-Darcy and Darcy flows such that a unified model for a field or a region can be created, which significantly simplifies the non-Darcy or turbulent flow modeling process for multiple wells, especially for future wells. The ratio-based method is simple and comprehensive. It can be easily calibrated with MRT (multiple-rate test) data and implemented into in-house or commercial reservoir simulators using a simulator supported scripting language, e.g., Python etc. Kashagan is the world's largest oil reservoir discovered in the last 30 years that contains highly connected and densely distributed fractures and karst in its rim. The oil production rate for a well in the rim can be higher than several tens KSTB/D if it is not constrained by the facility. The current MRT data in all tested wells clearly show non-Darcy flow phenomenon and confirm that modeling non-Darcy flow is necessary to the field. Kashagan had experienced difficulties to match BHP (bottom hole pressure) and large errors in the blind test due to the OPEC's production curtailment and high-rate tests. Build-up pressure curves were miss-matched and HM (history match) of the crossflows (10 KSTB/D with less than 10 psi) in the bottomhole of a PLT (production logging tool) well during shut-in was challenging. Since modeling non-Darcy flow for oil wells in the commercial simulators, e.g., E300 and Intersect, is unavailable, the simulation team in NCOC has created a new method for the needs of non-Darcy modeling and simulation. The applications of the new method have resulted in the excellent results and solved the issues of history matching BHP, high/low-rate tests, build-up pressure trends, and bottomhole crossflows.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"19 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123555996","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}

{"title":"Application of Machine Learning Algorithms to Predict Plant Process Upsets During Well Cleanup","authors":"Bagdat Toleubay, Baurzhan Orazov, Renat Sadyrbakiyev, E. Neubauer","doi":"10.2118/212089-ms","DOIUrl":"https://doi.org/10.2118/212089-ms","url":null,"abstract":"\u0000 Tengizchevroil (TCO) has several stages of put-on production (POP) process to bring the new wells online. New wells need to be cleaned up before being fully brought online and directly routed to the plants. However, reservoir conditions in Tengiz require drilling and completion methods that often results in losing significant amounts of drilling and completion fluids which comprises of barite, cuttings, and emulsions. The presence of such drilling and completion materials may and does cause plant upsets which consequently leads to significant LPO (Lost Production Opportunity).\u0000 To minimize plant performance upsets and equipment problems during a controlled well ramp-up of new, reworked, and stimulated wells (or, in short: plant flowback), a prospect of data analytics application arose intending to study the leading hitters that affect the processing at the plant during flowbacks and subsequently optimize the well flowback process.\u0000 By applying machine learning and statistical methodologies, a machine was given the ability to perform prognosis on plant upsets and duration of the plant flowback. The approach is broken into three main stages. Stage 1: create a consolidated history of plant flowbacks. Stage 2: determine variables that have the highest impact on plant performance (this stage would reduce the complexity of the model by removing irrelevant variables and subsequently increase the accuracy of the machine learning model). Stage 3: build an algorithm that predicts plant upsets from the variables extracted in Stage 2 (provides a data-driven method for evaluating risks associated with plant flowback).\u0000 A supervised classification model was built on historic data from 2011 that evaluated plant performance risks associated with the well flowback process. Given the risk probabilities, we could predict how long it would take to complete plant flowbacks on a subset that the model had not seen before. Additionally, it was shown that given the evaluated risk for the subset the model has not seen, a sequence in which these wells were put online could have been optimized to maximize production.\u0000 The feasibility of machine learning capabilities was tested on the historic plant flowback data. The study results have confirmed the intuition of the subject matter experts but in a robust and data-driven way. The model and the approach show data analytics methodologies’ applicability to optimize production operations further.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128549168","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}

{"title":"Modeling and Optimization of ESP Wells Operating in Intermittent Mode","authors":"E. Yudin, G. Piotrovskiy, N. Smirnov, D. Bayrachnyi, Michael Petrushin, S. Isaeva, Paul Yudin","doi":"10.2118/212116-ms","DOIUrl":"https://doi.org/10.2118/212116-ms","url":null,"abstract":"\u0000 The problem of effective oil reservoir managing becomes quite challenging during the late stages of its production. That's why, going for the intermittent operational mode is typical use-case for low-rate wells with electrical submersible pump since it's more efficient from both economical and engineering point of view. So, finding optimal parameters becomes crucial for the getting best value from the changes made. Tackling this problem leads to creation of the complex algorithm which will allow to compose an accurate physically motivated mathematical representation of a real-world object and provide functionality of calculating the best-possible operating regime.\u0000 The physically driven mathematical model was designed which represents system of three key elements: \"tubing–annulus–drainage area\". They are connected with each other through boundary conditions defined in the submersible pump intake by mass conservation equations.\u0000 The optimization task can be split into two blocks. The first stage, adjusting the model to the real operational data to match calculated dynamics, is handled by introducing \"adaptation\" parameters which represents specifics of a certain tubing system and varying IPR curve.\u0000 The second stage, optimization itself, is implemented by various optimization-aimed algorithms which objective is maximizing income while following operational and geological constraints.\u0000 As the result the physically motivated model of well operated in intermittent mode was obtained. The program module was developed with use of designed model. This software provides functionality to predict operational dynamics of production, hydraulic and electrical behavior. The accuracy of this approach to representing periodic well was verified by close analysis of convergence with well-trusted transient flow oil and gas simulator.\u0000 Developed modeling tools were the core for optimization program which are developed for the sake of finding the best possible parameters for a well with an ESP pump operated in periodic mode. It is worth to mention that this algorithm is also takes into account all geological and mechanical constraints.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115203138","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}

{"title":"Application of Machine Learning Techniques for Rate of Penetration Prediction","authors":"Asad Safarov, Vusal Iskandarov, D. Solomonov","doi":"10.2118/212088-ms","DOIUrl":"https://doi.org/10.2118/212088-ms","url":null,"abstract":"\u0000 In this paper, several supervised machine learning algorithms have been used to develop the model for rate of penetration prediction. To train the models, real-time drilling parameters and geological log data from 3 distinct wells in the South Caspian basin are used. The different machine learning techniques, such as linear and non-linear machine learning and deep artificial neural networks, trained the well data. The evaluation metric for training is Root Mean Square Error, however the performances of the regressions are evaluated on the data using R-squared for their comparison.\u0000 Rate of penetration, or simply ROP, is the speed of the drill bit penetrating into the formation. Overall, it indicates at which rate the borehole deepens. Its value depends on the drilling parameters, such as weight on bit, applied torque, mud flow rate, rotation per minute and others. In addition, the mechanical strength of the rock formation also plays a great role, and well log data is used to assume this value for each point. That is why these features in the training datasets have high vulnerability.\u0000 Comparing various techniques, Random Forest gives us the most optimal model in terms of accuracy and computational power. The average R-squared for Random Forest is 0.90. Although RNN and LSTM models can give nearly the same fit for given test data, it takes considerably much more time to train the models due to their complexity and show relatively lower accuracy on test data, therefore it is not a reasonable choice. Furthermore, another deep learning model is deployed to generate well logs for the following sections which supports optimizing ROP and drilling performance.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127365505","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}

{"title":"Drilling and Cementing to Isolate Productive Series and High Pressure Zones: A Successful Case History Enables Zonal Isolation in High Pressure Gas Well with Close PPFG Margins in South Caspian Basin","authors":"Apparao Cherukuri, Elshan Ismayilov, Elgun Zeynalov, Fuad Cabbarov, Vusal Iskandarov, Elkhan Karimov","doi":"10.2118/212071-ms","DOIUrl":"https://doi.org/10.2118/212071-ms","url":null,"abstract":"\u0000 The objective of this paper is to share the experience of how 7-5/8\" section was drilled, and cased-off to enable the zonal isolation in the depleted VII horizon and high-pressure zone with close PPFG margins by managing high risks of differential sticking and loss of circulation at Bulla gas-condensate reservoir which is one of the most complex high-pressure fields with close PPFG margins in the world.\u0000 The Bulla wells are highly complex wells the Caspian region. Before drilling into the reservoir section which requires high MW, the drilling liner was run to the cased-off VII horizon and high-pressure zone, which have significant challenges such as differential sticking and loss of circulation, previously resulting in stuck pipe and losses. To overcome the challenges, under reaming/liner cementing at MW lower than PP were evaluated and applied to have good cementing across the open hole and good shoe, to drill into the reservoir section in the project.\u0000 The study helped to identify the issues and actions taken that resulted in significant improvement in well delivery and reduced the risks despite having underbalanced condition during under-reaming/cementing. So is accepting the bitter truth, situation offering limited sets of options was very challenging as having:\u0000 high risks of differential sticking and loss of circulation due to high MW, high- pressure interval near the setting depth, which required high MW to stabilize the well, need to isolate the high-pressure zone and depleted VII horizon for drilling into the reservoir section.\u0000 However, the operation plans of managing the ECD within limits by using different MWs, higher MW for running casing and lower MW for UR and cementing have been risk-assessed with all the mitigation, and as a result, the aforementioned intervals were well isolated which was supported by CBL results. No gains or losses were observed during the operations including both casing running and cementing. Finally, having a good cement in the shoe allowed to successfully drill into the reservoir section with high MW accomplishing successful completion of the well without any major problems previously occurring. Therefore, this approach not only reduced the time, cost and also helped to deliver the quality wellbore.\u0000 The case study of liner cementing at underbalanced conditions provides useful insights of how unconventional techniques can be successfully implemented to achieve the overall accomplishment in well delivery performance.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126907622","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}

{"title":"Using Hybrid Digital Models to Analyse Well Interference and Optimise Field Development Management","authors":"Nadir Husein, A. Drobot, Dmitry Vasechkin, S. Urvantsev, A.Y. Bydzan, V. Bolshakov","doi":"10.2118/212120-ms","DOIUrl":"https://doi.org/10.2118/212120-ms","url":null,"abstract":"\u0000 Effective development of oil fields is impossible without reliable information on the degree of well interference.\u0000 The interference between production and injection wells is evaluated using indicators that are commonly known as the interference coefficients or connectivity coefficients. There is a number of statistical, analytical, and predictive methods for hydrodynamic connectivity (interference) assessment.\u0000 This paper focuses on a new approach designed to overcome the challenges associated with the rational development of reserves and well interference assessment with building an improved hybrid material balance model.\u0000 The purpose of the research was to evaluate various scenarios for well interference assessment, as well as to identify hydrodynamic communication between injection wells and specific producing wells for prompt decision-making to optimise the development system. The paper discusses the results of studies conducted on one field of Western Siberia, having a complex geological structure and highly heterogeneous.\u0000 Well interference coefficients were obtained as the result of the studies, which helped understand what average proportion of water injected from a particular injection well effectively impacts the producing wells in question located in the immediate vicinity. The digital hybrid model was used to identify the injection wells that are hydrodynamically connected with producing wells, estimate the degree of their impact on the production performance of the latter, as well as monitor the dynamics of such influence for each well. Based on the analysis performed, some recommendations were elaborated for improving the oil recovery factor in the oil fields and reducing development costs.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115192205","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}

{"title":"Managed Pressure Drilling and Managed Pressure Cementing: First Successful Implementation of Advanced Technologies for Unique Wells with Constant Inflow in Slim Drill Project","authors":"Almas Kaidarov, A. Magda, A.E. Samarin, Fuad B. Aliyev, Zhanna Kazakbayeva","doi":"10.2118/212108-ms","DOIUrl":"https://doi.org/10.2118/212108-ms","url":null,"abstract":"\u0000 While drilling well in the Pre-Caspian basin, a presumably technogenic nature zone of influx was exposed, which did not fit into the model of the geological structure of the section. Attempts of influx management were unsuccessful. The well had to be abandoned without reaching the target. This article describes the experience of Managed Pressure Drilling and Cementing technologies deployment, process features and equipment hookup in unconventional wellhead configuration and Slim Drill type of rig.\u0000 Application of MPD technology made reaching target depth successful in the condition of constant water influx. Drilling proceeded in complicated geological conditions: simultaneous crossflow between influx and losses zones, as well as drilling mud gradual replacement by formation fluid. Bottomhole pressure was controlled by Surface Back Pressure made by MPD choke and sealed wellhead using a Rotary Control Device (RCD). The compact set of MPD equipment was rigged up as ergonomically as possible due to space restriction of slim hole drilling techniques.\u0000 The MPD technology made it possible to complete the construction of the section without curing losses and weighting the drilling fluid up to control technogenic influx, safely and effectively control bottomhole pressure and achieve geological targets. MPD tests were performed at various depths to determine formation pressure and formation integrity, which made it possible to determine the drilling window for successful construction of the section. After finishing drilling, a pull out of hole and well logging was carried out in an open hole on a drilling pipe with pressure control, which was previously impossible due to the high intensity of influx.\u0000 Running 4-1/2\" (114 mm) casing with pressure control, as well as cementing in MPC mode were applied. The surface backpressure was gradually reduced during cementing as heavy cement was pumped to minimize the risk of cement loss while controlling formation fluid influx. As a result, cement was lifted through the annulus to the wellhead, which finally eliminate the complication associated with the presence of incompatible zones of loss and influx, and to eliminate behind-the-casing flows. The economic feasibility of well construction was maintained by set of equipment adapted to non-standard conditions The first application of MPD technology on the Slim Drill (small-sized drilling rig) project successfully revitalized previously abandoned well. It shows the validity of using MPD technology by the help of optimized set of equipment. The work reveals broad prospects for replication on a previously inaccessible wells due to economic reasons.","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855412","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}

{"title":"Engineered Combined Keys Drive an Accomplished Drilling Record in a Challenging Field","authors":"Abdullah Hammad, Fahad AlHosni, Mohammed Julaidan, A. Ruzhnikov, E. Garnica, Gustavo Moreira, R. Varela","doi":"10.2118/212073-ms","DOIUrl":"https://doi.org/10.2118/212073-ms","url":null,"abstract":"\u0000 Enhancing drilling performance for surface sections across multi-layer formations is relatively challenging in the Middle East due to the carbonate's soft nature combined with interbedded formations resulting in lost circulation, harsh drilling environment leading to shock-and-vibration behaviors, and bit/BHA damage; factors including bit size and applied drilling parameters affect these conditions. This manuscript demonstrates the study and successful field application performed on different approaches, including bit/BHA selection, hydraulics enhancement, and drilling fluid recipe.\u0000 Firstly, by lowering Total Flow Area (TFA), hydraulics analysis demonstrated an increase of Horsepower per Square Inch (HSI) and Jet Impact Force (JIF) by up to ~28% compared to the existing current designs. Furthermore, the advantage of the partially hydrolyzed polyacrylamide (PHPA) additive for the Mix on Fly (MOF) recipe is valuable as a lubricant shale inhibitor, sealing microfractures and coating shale surfaces with films retarding dispersion and disintegration. Hence, reducing torque and friction and minimizing shock-and-vibration behaviors. The BHA was engineered and redesigned in order to increase WOB limits and reduce building tendency. Additionally, bit selection was accomplished from IADC 435x to IADC 425x to optimize the ROP and durability.\u0000 Improvement results have been observed while drilling the section in one run (shoe to shoe runs) without any wellbore instabilities and bit/BHA damages. The BHA has maintained the hole angle from deviation. For instance, the Gyro logs showed that the maximum inclination of the wellbore was less than ~1.5 degrees. Additionally, the BHA higher WOB was applied on thicker shale layers, indicating a higher performance once applying higher WOB. Ultimately, due to the PHPA additive on MoF, a smooth trip out of the hole illustrated that the hole was in good condition, eliminating the wellbore instability risks and the wiper trip.\u0000 Moreover, the shocks and vibrations were reduced considerably based on the nearby offset. In addition, a new record for enhanced drilling ROP of 43.83 FPH was achieved. Showing illustrated improvements with an increase of ~35% compared to offsets of 22in section across that formation interval. Indeed, TCI 425x bit established a higher durability during this drilling run with medium dull grading compared to 435x, where it had to POOH for bit damage. This led to reducing the section's time by 0.75 days compared to the operator's existing best performance time of the offset wells in that field.\u0000 This manuscript offers engineered key solutions to numerous challenges encountered across various surface sections relying on the well types while drilling with full circulation or lost circulation across the formation compositions such as but not limited to Anhydrite and Shale. The outcomes could be extended as lesson-learned for such challenges and easily implemented considering full risk assessment for dri","PeriodicalId":131012,"journal":{"name":"Day 2 Wed, November 16, 2022","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129794177","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}