Schlumberger implemented the first instrumented platform integrating advanced measurements in real-time while perforating with plug-in gun connections. This paper presents the results obtained at field level using this technology in Ecuador and a comparison of this technology with perforating operations and techniques in terms of efficiency, reliability, and correlation between the predicted and measured dynamic events to perform a full evaluation of the perforating design process, execution and its impact in well performance Out of more than 120 jobs performed year to date in Ecuador, the authors selected 5 wells covering operations with dynamic underbalance (simultaneous and standalone) and gas fracturing to illustrate the efficiency gains of a faster and more reliable arming system and to analyze the match between simulated and measured dynamic events (pressure and shock). Among the 5 wells, two of them achieved positive production results, and the real time measurements showed additional information in terms of: reservoir pressure, well response, and perforations cleanup during the job execution. The other three wells provided key information related to well control and pressure buildup after perforations, withal they did not provide comparatively consolidated production results.

The coal-seam gas (CSG) industry has long been considered as a high volume, low cost market. As the industry has matured, the selective application of high-tier technologies has realized a step change in performance and real-time formation evaluation results. We investigated whether a high-tier LWD multi-function service could provide a suite of quantitative real-time measurements in several deviated wells. The key objective was to reduce the amount of non-productive rig time spent waiting for memory data in order to confirm the completion design. Significant savings in rig time could be realised if reliable, high-quality real-time data enabled the early identification of coal seams and permeable aquifers such that the swellable packer and slotted liner completion design could be completed without the need for final memory logs. The area of interest is characterized by thin Jurassic coal seams rather than thick Permian seams. It was critical to accurately identify thin coal beds in real-time whilst maintaining a high rate of penetration (ROP). Low-resolution data would result in poor completion design, underestimation of net coal reserves, and sub-optimal static models. Measuring coal thickness and properties can be difficult due to the fundamental differences between the formation evaluation measurements and their relative axial resolutions. The presence of thin coals can further complicate the interpretation. Another challenge was to optimize the real-time data transmission to prevent any limitation on the key directional drilling data parameters. Conventional LWD logs (gamma ray, nuclear, and resistivity measurements) provide formation evaluation information while drilling. The selection of a rotary steerable system (RSS) was critical as it ensured directional control and avoided any sliding intervals over key aquifers and coal zones, thereby ensuring optimal LWD acquisition. Advanced formation evaluation options of the LWD data also included using dual-pass resistivity inversions for Rt/Rxo to determine the invasion profile in a permeable aquifer zone above the main coal-producing reservoir. Having this information in real-time was critical in guiding well-specific competition decisions. Induction and laterolog-type resistivity tools were run on one well to quantify differences in the measurements and to determine the best resistivity acquisition tool for CSG wells drilled with saline muds in freshwater formations. The results showed that high-tier LWD technologies provide multiple benefits in CSG wells. The project was executed with all directional and logging objectives achieved. Quantitative real-time data was critical for completion decisions including ECP placement together with swellable packer and slotted liner designs. This resulted in significant cost savings which are important to major CSG developments operating within a low-cost operating model. LWD memory data provided a rich suite of additional measurements to complement the real-time data. Memory data was used for advanced reservoir analysis with industry-unique measurements.

This paper describes the extensive integrated engineering collaboration and optimization process to allow the operator to push the drilling and completion envelope to drill complex ultra extended reach drilling (ERD) wells in a mature field. The Wandoo field is located within WA-14L in the Carnarvon Basin, offshore Western Australia and is wholly owned and operated by Vermilion Oil & Gas Australia Pty Ltd (VOGA). Due to the shallow reservoir depth, extreme ERD profile and the high tortuosity requirement for the wells, significant challenges were identified during the planning stages of the campaign. To optimize well placement, update geological information and reduce collision risk with nearby producing wells, the Reservoir Mapping While Drilling (RMWD) service was run in real-time. The primary objective of the service was to place the well in the optimum position within the reservoir, this included (1) provide early detection of moved oil water contact (MOWC) while approaching well crossings with minimum separation factor to reduce collision risk and geological uncertainties; (2) to steer azimuthally to optimize the placement of the well with respect to the field bounding fault and reduce the drilling risk associated with the intersection of the fault; and (3) to distinguish between the A3 Upper (A3U), A3 Lower (A3L), and B sands to optimize well placement. These challenges were successfully overcome with extensive planning, integrated engineering designs, application of new state-of-the-art technology, good quality real-time data interpretation and a strong execution support from both rig site and town. As the result of good collaborative teamwork in planning and execution, the two highly technically challenging wells were drilled and completed successfully and brought online for production. A number of drilling performance highlights captured during the execution phase were identified as having significantly contributed to the successful completion of the project.

Drilling is considered one of the most challenging and costly operations in the oil and gas industry. Several initiatives were applied to reduce the cost and increase the effectiveness of drilling operations. One of the frequent difficulties that faces these operations is unexpected drilling troubles that take place and stops the operation, resulting in losing a lot of time and money, and could lead to safety issues culminating in a fatality situation. For that, the industry is in continues efforts to prevent drilling troubles. Part of these efforts is utilizing the artificial intelligence (AI) technologies to identify troubles in advance and prevent them before maturing to a serious situation. Multiple approaches were tried; however, errors and significant deviation were observed when comparing the prediction results to the actual drilling data. This could be due to the improper design of the artificial intelligent technology or inappropriate data processing. Therefore, searching for dynamic and adequate artificial intelligent technology and encapsulated data processing model is very essential. This paper presents an effective data-mining methodology to determine the most efficient artificial intelligent technology and the applicable data processing techniques, to identify the early symptoms of drilling troubles in real-time. This methodology is CRISP-DM that stands for Cross Industry Standard Process for Data Mining. This methodology consists of the following phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. During these phases, multiple data-quality techniques were applied to improve the reliability of the real-time data. The developed model presented a significant improvement in identifying the drilling troubles in advance, compared to the current practice. Parameters such as hook-load and bit-depth, were studied. Actual data from several oil fields were used to develop and validate this smart model. This model provided the drilling engineers and operation crew with bigger window to mitigate the situation and resolve it, prevent the occurrence of several drilling troubles, result in big time and cost savings. In addition to the time and cost savings, CRISP-DM provided the artificial intelligent experts and the drilling domain experts with a framework to exchange knowledge and sharply increase the synergy between the two domains, which lead to a common and clear understanding, and long-term successful drilling and AI teams collaboration. The novelty of this paper is the introduction of data-mining CRIPS methodology for the first time in the prediction of drilling troubles. It enabled the development of a successful artificial intelligence model that outperformed other drilling troubles prediction practices.

The emergence of Real-Time Well Construction Performance Monitoring Centers has significantly improved the service delivery for Operators across numerous offshore oil fields in Australia. New technologies and workflows were implemented for 3 Australian offshore wells, with the primary objective being to improve drilling efficiency while managing the associated risks. Additional objectives included optimizing daily operational performance, thus delivering time savings for the Operator and highlighting areas of possible improvements. The workflows combined proven technologies, software and wellbore surveillance services, which delivered risk-free wells ahead of Authority for Expenditure (AFE), by targeting the reduction of nonproductive time (NPT) and invisible lost time (ILT), together with risk management. The workflow began with a preliminary field analysis to identify the main challenges and areas that needed improvement. The key learnings were then consolidated on a comprehensive drilling road map by well section that outlined prevention and mitigation measures, expected rate of penetration (ROP) and recommended drilling parameters. The study also identified performance benchmarks such as average and on bottom ROP and connection time. Key performance indicators (KPI) were developed to monitor progress and track the well with respect to well improvements versus the benchmarks identified. The selection of the monitoring tools and modules required during the Execution Stage were developed based on the risks and KPIs identified during the field study and were further tailored to target specific challenges such as stuck pipe prevention, hole cleaning, shock and vibration and connection time. The workflows enabled the flawless execution of 3 offshore wells 29.0 days ahead of AFE which saved the Operator USD 15.5M. The closed loop monitoring enabled real time interventions which aided in preventing risks such as stuck pipe, ensured efficient shoe-to-shoe drilling and avoided potential lost-in-hole (LIH) costs. This case study demonstrates the use of a novel approach to increase well construction efficiency by eliminating lost time while enhancing risk control. The main basis for success of this workflow was using existing and cost-effective technologies while capitalizing on the renewed synergy between different departments such as Drilling, Mudlogging and Well Operations analysis. The workflow was and can be customized based on different needs, by combining specific modules for monitoring, analysis and wellbore surveillance services, to increase the efficiency of any well construction.

The objective of this paper is to discuss and outline the invention of the data while tripping (DWT) device, why it was developed, the development process, introduction to the market, and proven value for the end user. A ‘Fail fast’ and ‘Test to Failure’ approach was taken where 3-D printing, simple prototypes, and components testing was performed before building gradually more sophisticated prototypes, all of which were tested to destruction. Based on learnings, a final test version was built and tested to 60 times the customer's requirements in a controlled environment. The DWT device was deployed in the fall of 2019 on two offshore rigs in the North Sea for final prototype testing. This gave the basis for refinements leading up to the commercial version of the DWT device, as well as proving the conceptual value of the device; no people required in the red zone while tripping a wired drill pipe string, continuous downhole along string pressure measurements, continuous logging while tripping utilizing bottomhole assembly (BHA) tools, and continuous down hole and along string data input for automated drilling applications. Additionally, considerable time savings were proven. The concept of being connected to a wired drill string during tripping operations has been on the industry's wish list for years. The lack of this technology has, on many occasions, been a showstopper for selecting wired drill pipe for drilling operations. The introduction of the DWT device has shown to be a success for both proving the value of deploying wired drill pipe (turning the lights on downhole) as well as staying connected to the downhole network while tripping (keeping the lights on downhole) – both systems enabling data flow without mud flow.

Coiled tubing (CT) intervention in a subhydrostatic gas injector well carries several challenges. For example, excessive fluid leakoff during a CT sand cleanout operation requires nitrifying the cleanout fluids to achieve a stable solid return rate, which necessitates a huge volume of liquid nitrogen. For perforation with CT, methods of activating the firing head are limited because the fluid column in the well cannot be sustained if a hydraulically activated firing head is used. Alternatively, cleanout operations for such wells can be done by pushing all the solids into the formation instead of taking returns through surface solids-handling equipment. For this case describing such an operation, real-time downhole measurements were used to accurately determine the top of fill, monitor for signs of formation plugging during the cleanout, and detect sudden increases in downhole overpull indicating solids starting to plug around the bottomhole assembly (BHA). The fiber optic telemetry within the real-time CT system also enabled use of an electrical firing head powered by downhole batteries, which allows CT perforation without the need to pump fluids. The method of cleanout by injecting into the formation through a sacrificial open perforation can be considered an unconventional CT intervention. Solvent was used with a high-pressure jetting tool to break down asphaltene residues across the open perforation before injecting into the formation at high flow rate. Real-time downhole weight measurements on the BHA provided valuable information to continuously locate the top of fill because the depth changes as the solids are pushed into the formation. This cleanout method reduced the equipment footprint on the offshore installation since no surface solids-handling equipment was needed. Early detection of formation plugging through real-time monitoring of downhole pressure eliminates the need to take solid returns to the surface production separator because the injection flow rate was adjusted in real time before gaining back the desired leakoff as pressure built in the wellbore to create a breakthrough. The ability to add new perforations with the same CT setup and using a fiber-optic–enabled electrical firing head avoiding the need to replace CT with wireline equipment, increasing the overall efficiency and saving more than 48 hours of operational time. Downhole measurements were also used to correlate depth and indicate perforation gun firing. The newly perforated zone contributed 13% of the injected gas into the targeted formation and no further intervention was required after the CT cleanout and perforation. The underbalanced perforation for the new zone was critical to minimize near-wellbore damage for this gas injector well. Two surrounding producers that were not producing before have benefited from this CT intervention, producing hydrocarbon at 500 Sm 3 /d and 350 Sm 3 /d post-intervention.

The super-giant field under study is one of largest oil producers in Middle East and been producing for more than 50 years from a multilayered carbonate reservoir. Long-term sustainability of production of the field is a key mission for the field operator and national oil company, considering both productivity and recovery of the field. Gas lift has been considered as an effective and economically feasible method to increase production of the wells and as a solution to revive the closed wells and to maintain the production of flowing wells. Prior to full field implementation, a gas lift pilot has been initiated in the field. The objective of the pilot is to collect further essential information and evaluate the challenges prior to the full field implementation. At the well scale, managing the well lift rate allocation, monitoring the performance of gas lift valves and running diagnostics, and optimizing the production rate are the main challenges for the gas lift pilot. At the full field network level, the backpressures of the lifted wells are affecting the production of the other wells producing naturally. A commercial three-phase flow simulator was used to build the well models as well as the pilot network, with certain boundary conditions to develop a scalable solution to enable the operations and headquarters teams to provide a timely solution to the mentioned challenges. To integrate all inputs from various disciplines, the information from the monitoring activities and from reservoir saturation and production logging tools in addition to well test data and gas lift surveys was used to develop the solution. The gas lift management solution was evaluated in the pilot area to diagnose the gas lift valves, validate well tests, optimize gas lift allocation, enhance well production, and perform field-wide production optimization The solution also reveals gas lift candidate wells with the potential to increase the production and directs redistribution of the lift gas in the pilot area to minimize gas utilization. Additionally, to introduce scalable solution which can be utilized at very large wells count, automation of the workflow process has been considered to provide efficiency for engineers which will get advise from the solution.

Vibrations impact the drilling process by reducing reliability, increasing maintenance costs and reducing rate of penetration and drilling efficiency. Herein, torsional vibrations are typically distinguished into low-frequency torsional oscillations and stick/slip with frequencies below 1 Hz and high-frequency torsional oscillations (HFTO) with frequencies up to 450 Hz. HFTO is associated to high accelerations with critical values above 150 g and dynamic torsional torque values above the make-up torque. A HFTO mitigation strategy is mandatory for prone applications to guarantee high reliability and drilling efficiency. In this work different best practices for mitigation of HFTO and their interdependency are discussed along with their operational efficiency. The discussed scenarios include coupling between stick/slip and HFTO, different formation properties, and tools that are used for vibration mitigation such as isolators and mud motors. The analysis includes the review of time-based acceleration and load data with a sampling frequency of 1000 Hz and 2500 Hz and numerical modeling to determine the application and environment specific critical range of operational parameters in a holistic approach. The extracted critical range of operational parameters enables operations to optimally adjust parameters with a minimum of torsional loads and optimal drilling efficiency. This analysis again unveils that the general effect of HFTO is triggered by the cutting forces between PDC bits and hard and dense formation. It is shown qualitatively and quantitatively that high WOB values and low rotary speed values correspond to HFTO. The case study shows opportunities of a reduction of HFTO related loads by increase of the rotary speed without compromising the ROP in specific applications and environments. It is shown that special tools for vibration mitigation influence the stable operational window and need to be considered. Depending on the scenario a complete mitigation of HFTO or at least a significant reduction of loads can be achieved by targeted adjustment of operational parameters and use of tools for vibration mitigation. The drilling process can be optimized leading to a reduced cost of the well delivery since HFTO can be a major cause for non-productive time if not handled properly.

Upstream Oil & Gas industry recognizes that there are significant gains to be had by the implementation of new digital technologies. For offshore exploration and development, the goal is to bring together all domains, all data, and all engineering requirements in a seamlessly interconnected solution. The industry is putting significant efforts into using instrumentation and software to optimize operations in all domains for exploration and production (E&P) to move towards the digital oil field of the future. an innovative digital solution has been designed and implemented to cover all different aspects of the well planning and engineering workflows, delivering a step change in terms of capabilities and efficiency. As part of this transformation process, CNOOC have implemented integrated data management project of geological engineering for covering all different aspects of the well engineering workflows, delivering a step change in terms of capabilities and efficiency. The objective is to provide a continuous improvement platform to users for: Digitalization can reduce the time spent with daily documentation and simultaneously increase the quality by removing an error prone way of work. Technological solution enabling real-time data transmission from all rigs to CNOOC onshore headquarters and enabling real-time visualizations of the drilling data. This includes workload, number of needed rigs, daily performance, key performance indicators and even operation time forecasts based on real data. Engineering solution to transform expert experience and accident cases into information to easily identify the areas of operational improvement allowing to implement specific measures to reduce intangible loss time (ILT) and non-productive time (NPT) which can help in reducing costs. This project has also provided a real geological drilling environment where high frequency real-time drilling data is utilized along with low frequency daily drilling report data to provide better insights for well planning and generate ideas for improving performance and reducing risk. This paper presents a full description of a new industry standard digital well construction solution that has the potential to transform the well operation process by providing a step change in collaboration, concurrent engineering, automation, and data analytics. Furthermore, the cloud-deployed solution challenges will be briefly discussed. The learned lessons and gained experiences from this project construction presented here provide valuable guidance for future demands E&P and digital transformation.

Formulating a robust and efficient production optimization and forecasting tool for unconventional oil and gas assets is a challenging task. This is mainly due to the high number of wells involved and the limited predictability of detailed individual well performance until there is sufficient production history. Current methods to optimize and forecast production rely on type curves and surface network models, neither of which account for changes in the wellbore condition. Generally, these activities are conducted separately and are rarely fully integrated. A solution that can automatically match real-time performance of an unconventional asset well is presented in this paper. The implemented solution replicates performance of unconventional wells using transient models. Input parameters for all models honour a given range of realistic operational conditions and are automatically regressed to match the historical performance. At a specified interval, historical production is updated in the models and regression is performed automatically to keep the multiple models updated. This solution can address challenges related to historical well allocation given by lack of remote sensing, by coupling the well model to a network model. The use of automated regression tools to identify outliers reduces the requirement for routine engineering review, thus making forecasting for thousands of wells manageable. Having accurate well models coupled with a network model allows automatic allocation matching at separator levels and hence a calibrated network model leading to production optimization and better field management. Consequently, a robust optimization and forecasting tool for unconventional assets is achieved.

An operator and a service company parternered in successfully drilling the longest extended-reach drilling (ERD) wells in the Sultanate of Oman. The project consisted of four wells to be drilled in the G field. These wells will be the first aquifer pumpoff wells drilled in the field with a goal of lowering the reservoir water cut (oil/water contact) for all nearby oil producers, and to minimize water production and maximize oil production. The buildup section required drilling through the risky gas cap and zones having possible total fluid losses. The risk of stuck pipe and the likelihood of gas kick, as well drilling through fractures on the horizontal section and experiencing potential total fluid losses, made successfully drilling the wells to TD even more difficult and challenging. The buildup section was divided into two string designs based on a previously used single-string design. The drilling objectives were to reduce and separate the risks in the different sections and to drill the gas cap formation in the 12.25-in. shale section, plus landing the well with possible fluid losses while entering the reservoir with the 8.5-in. bottomhole assembly (BHA). This drilling plan eliminated known risks, using BHAs for drilling the 12.25-in. section with a push-the-bit system, and drilling the 8.5-in. section with a high-buildup rate rotary steerable system (RSS) for dogleg severity assurance. Drilling both sections was performed with a near-bit gamma ray adapter for geostopping. The 6-in. lateral section was the main challenge in the drilling operations due to the anticipated highly fractured reservoir with possible total fluid losses and high-equivalent circulating density management. Poor borehole cleaning was expected due to the high-critical transport rate, as well severe shock and vibrations. The BHA was designed to be able to drill to TD using the following techniques: A finite element analysis (FEA) model was developed to determine the most excellent drillpipe to be used in terms of shock and vibration and buckling moments. The model recommended 4-in. drillpipe to reduce the high-surface pressure expected at TD. The FEA model helped in selecting the best driving system (motorized BHA) because the simulations indicated that the BHA would have lower shock and vibration and be able to handle the expected high-surface torque. Due to the expected faults and possible sidetrack, the final BHA selection was a motorized high-buildup rate RSS. The real-time parameter management and plan helped in selecting the best drilling parameters to minimize real-time shocks and vibrations. Drilling the buildup section with the two-casing string design solved the risk reduction and allowed for landing the four wells successfully. For the lateral section, the BHA design met the expectation of completing the drilling operation on one run to TD. Both companies teamed to successfully drill the two longest ERD wells in the Sultanate of Oman. All of the wells are in the very extended-reach group by ERD definition. Well G-55 is the longest ERD ratio well drilled to date by the operator with a 4.01 ratio.

The upstream E&P industry encompasses diverse range of activities. The core activities may be broadly classified as G&G, Drilling, Production and Transportation and non-core activities supporting efficient execution of core activities and the business as a whole may be classified as Finance, Contracts and Purchase Management, HRM and Engineering services. E&P industry is one of the earliest adopters of digital technologies in its operations to efficiently transform complex reservoirs into revenues. Oil India Limited (OIL), the 2 nd largest national E&P Company of India too adopted digital technology in its G&G activities encompassing Seismic data acquisition, processing, interpretation and logging in 1970s. It was followed by use of high end workstations for reservoir studies & modelling in 80's. Over the years, OIL deployed number of digital interventions in both core and non-core activities. However, like other E&P companies, induction of digital technology is slow in core activities like Drilling and Production. As far as non-core activities are concerned, advanced digitisation methodology adopted in the area helped OIL in enhancing efficiency and ease of data access in B2B, B2C relationship. Crude oil and Natural Gas price volatility demands that E&P organizations to be more agile and competitive. Thus there is need of collaborative workplace with real time data analysis and efficient decision making facility. As a solution, number of intelligent IT technologies in the form of IoT, Data Analytics, Machine learning, Artificial Intelligence, Augmented Reality etc. are emerging at an affordable cost. Adopting these technologies in a holistic way integrating all core and non-core activities of an E&P industry can fulfil the need of above desired solution enhancing productivity, flexibility, reliability and RoI. Believing "Holistic Digital Transformation" is the success mantra to become more agile and competitive, OIL has embarked in the process of implementing new digital technologies integrating all its core and non-core activities. The paper describes OIL's methodology towards holistic digital transformation.

One goal for oil fields of the future is acquiring continuous and on-demand data as required for field and reservoir management. Synthetic time-lapse production methods are becoming a way of providing this information at and away from wells. Time-lapse production log data acquired over oil fields is used to monitor water sweep in the reservoir. Production logs provide a direct measure of the fluid flowing downhole and detect the unwanted fluid entries. In field applications, this advanced scanning of fluid profiling successfully derisked several infill well locations and identified new workover candidates and drilling opportunities in the fields. Synthetic time-lapse production logging is a useful complement to understanding reservoir heterogeneity and complexity through tailored synthetic and real data integration. A computer-based workflow has been developed to automate the downhole production flow profile. Production performance of the well is assessed, considering the dynamic time-lapse logging data. A synthetic flow profile is constructed to show the change in water production signature, and the well is further examined if it undergoes remedial actions. Reservoir characterization is a continuous process during the life of the oil field. As new data are available, the model is updated and contains more details. The incorporation of all data allows increased accuracy and reduced uncertainty in characterizing the reservoir. The proposed methodology requires the acquisition of dynamic production logging data to establish a solid workflow and validate the model. Uncertainty can be eliminated with the acquisition of additional production logs. Recommendations for improvement of the current well condition can be made to reduce the well water cut and improve oil production from the well. Consequently, well classification and candidate selection for workover can be achieved. The results of this work demonstrate the strength of applying multidisciplinary team efforts to develop automated workflows that are relevant to reservoir and production engineers who deal with complex reservoirs with numerous wells.

This paper discusses case histories, where Distributed Temperature Survey (DTS) with Fiber Optic Enabled Coiled Tubing (FOECT) was used during stimulation treatment and well testing by the Operator in Southern Iraq oilfield. The field was discovered in 1984 with hydrocarbon accumulation in four (4) major reservoirs, first oil production was achieved from the top carbonate reservoir in 2013. The wells were drilled to penetrate a thick carbonate reservoir which ranges from 80m to 100m thickness, subsequently completed as single selective producer with cemented casing and perforated across the targeted zone. Permeability and porosity varies drastically within same carbonate reservoir thus effective treatment fluid diversion is required to ensure the largest possible surface area of the reservoir is contacted and exposed to stimulation fluid to achieve uniform stimulation and sustainable production. Main challenge for stimulation treatment in heterogeneous carbonate reservoirs is to achieve optimum placement of the stimulation fluid without some knowledge of, and control over where the treatment fluids flow. Traditional way to record production and injection fluid profile is via running a production logs with wireline equipment, however this require additional equipment and time. This was addressed by using CT equipped with fiber-optic real-time telemetry, that offer 3D temperature profiling which significantly improved the stimulation treatment by providing downhole monitoring throughout the entire treatment and thus a new level of control of fluid placement. In addition, the FOECT allow real-time interpretation of Pressure Transient Analysis (PTA) to evaluate the well deliverability, production performance of the stimulation treatment and basic reservoir description (e.g. permeability and total skin). This technique helped in reducing time and the number of well intervention runs, while improving fluid placement to achieve faster, more informed stimulation treatment. Lessons learned from these case histories enhanced Operator understanding of the reservoir and led to recommendations for future treatments and potential replication. Steps and considerations in decision making, reaching the most advantageous solution for the stimulation treatment as well as the detailed engineering evaluation for the two (2) wells candidates will be addresses in this paper.

Drilling operations are considered a major cost in the development phase of oil and gas wells, which places huge emphasis on drilling efficiency as a leading factor in cost reduction and optimization. Nonproductive time (NPT) incidents such as stuck pipes must be minimized by careful planning and close well monitoring to enhance drilling efficiency. One frequent and global cause of stuck pipes is the inefficient removal of formation cuttings from the wellbore while drilling, which is the focus of the developed real-time model. Poor hole cleaning is also a major contributor to other NPTs such as loss of circulation and formation fracturing, which can be induced due to the high equivalent circulating density (ECD) caused by the presence of excess cuttings. Insufficient hole cleaning, if not tackled properly and in a timely manner, can lead to NPT incidents and consequently increases the drilling cost significantly. In addition to NPT reduction, proper hole cleaning can increase the rate of penetration (ROP) and reduce torque and drag. A tool that provides a real-time indication of the hole cleaning efficiency is therefore very valuable to have better control of the hole conditions, especially in critical wells. Such indicators will provide continuous monitoring of the wellbore and will allow immediate intervention for detected abnormalities. There are several indexes that evaluate hole cleaning efficiency while drilling. The index that will be the core of the developed model is the Carrying Capacity Index (CCI), which is defined as the ability of a mud system to circulate the cuttings to the surface. The index is influenced mainly by the drilling fluid properties and flow hydraulics, which are both controllable factors that allow the rig crew to adjust on location to ensure sufficient cleaning of the cuttings. The developed system automatically calculates the CCI in real-time. Thousands of raw values are generated from rig sensors continuously, which makes the calculations nearly impossible for a human to perform. The model is developed to take in the raw data and use it as an input in conjunction with some well details, such as hole size and casing size, to generate the CCI. This system takes us one step closer toward the ultimate goal of having an integrated and fully automated hole cleaning evaluation and intervention tool that does not require any human involvement.

This paper aims to describe an integrated data analysis and validation application for well testing. This is a computer based application set to work with the different types of wells (water injection, oil producers and gas producers). In addition, it features data capturing, validation, analysis and several techniques for data visualizations. The conventional well testing process goes through: data capturing, loading, calculations, validation and data archiving. This process was handled by multiple isolated systems that required different workflows based on the data sources. In addition, these systems were designed to handle conventional testing methods such as High Pressure Test Trap (HPTT) separator. With the introduction of continuous real time measurement technology, such as Multi Phase Flow Meter (MPFM), the volume of the data expands tremendously. This imposes the challenge of big data analytics while ensuring the quality and the consistency of the results. This resulted in a demand for a system to streamline, integrate, standardize and automate the process of well testing. The Integrated Well Rate Analysis System (IWRAS), was developed and deployed to overcome the encountered challenges. This paper describes well test handling in IWRAS with an example of how the application unified the process workflow for oil producers equipped with MPFM. It will also shows the benefits of utilizing this application in handling real time data.

Because of hydrocarbon oil reservoirs aging and oil production declining, and due to continuous change of oil characteristics and deterioration of production flowlines and shipping pipelines, oil congealing is becoming an increasingly major challenge in maintaining continuous production for both light and heavy oil operations. Thousands of oil barrels are lost on daily basis across the oil industry due to oil congealing indicating that there is a significant gap in industry knowledge to understand the real root causes and how to address or mitigate them. Hydrocarbon oil has a very complex liquid characteristic specially when is comingled with other crude oils (with different properties), and other deposits which are produced from reservoir and/or formed during production and/or transportation. The oil flow regime could yet be even more complex with varying weather conditions. With aging-reservoirs’ temperature and flowrate declining, and, increasing asset integrity issues minimizing the pressure envelop of production and transportation pipelines, maintaining a healthy flow is becoming a major challenge specially during low temperature and rainy seasons. Such conditions reduce the oil temperature and thus viscosity, and if the condition persists, then cause the development and formation of oily wax and subsequently oil congealing. It is thus critical to significantly improve the knowledge of (mixed) oil characteristics and properties and their rheology behavior with varying temperatures, pressures and flowrates by performing complex and non-linear modelling and simulation to better predict their flow regime, and address and prevent any issues ahead of the time using appropriate preventing measures in the form of mechanical, thermal and chemical solutions. PT. Chevron Pacific Indonesia has made a major step change and development in the industry, and significantly progressed to mitigate oil congealing in her light oil operations by developing a comprehensive real-time and integrated prediction and prevention tool to help eliminate or minimize lost production due to oil congealing. The development included two stages: oil rheology and non- Newtonian modeling, and prediction algorithm and digital tool development. In this paper the sequence of both stages is presented, and the results are discussed in detail. Stage 1 development process was implemented using comprehensive laboratory and field tests of various oils/oil mixtures for different shear rates and temperatures, categorization of 19 different oils/oil mixtures with wide-varying wax appearance temperatures and the development of oil rheology models and simulations. The pipeline network which included 11 gathering stations with feeder lines from 6" to 12" and the main 30" shipping line extending about 80 km, indicated the complexity of the assessment requiring significant simplification. The results were comprehensively checked against norm trends and tested using several commercial modelling and simulation tools. Significant iterations were applied, and non-Newtonian models were built to finally converge to meaningful data and in the development of a visual basic algorithm representing the oil and oil mixture for the logic sequence. Stage 2 development process was implemented by thorough running of visual basic tool for various temperatures followed by comprehensively and iteratively checking the outputs against collected historical event data until the data converged with an acceptable bias. The inclusive algorithms were developed in two stages to include a wide-ranging prediction tool for various production scenarios, development of a significant library for thousands of varying temperature, pressure and flowrate scenarios and the associated prevention and combating solutions. It was then linked through a sequence of intelligent logics to an operations intelligence software and an existing Human-Machine-Interface to minimize human interface and maximize digital innovation in prediction and subsequent prevention actions. The tool included real time "Day 0" data as well as predicted pressures and temperatures for the following 8 days based on a paid weather service data which were also calculated based on crude oil travelling time. This tool, which was developed in 2018 as part of a critical digital flow assurance project, has been put in operation mode for some time and has already made considerable contribution to the operation to prevent oil congealing and lost production, and, also helped build substantial confidence that oil congealing could be mitigated entirely

The paper will discuss the advancement of recent technology high definition resistivity imager convey with drilling assembly (Logging While Drilling) to quantify fracture geometry and secondary porosity (vuggs) in highly fractured carbonate. Oligo- Miocene carbonate Kujung formation in East Java Basin will be penetrated with high angle well and completed with Inflow Control Device (ICD) to manage water breakthrough from the presence of the fracture. High definition borehole images typically required to quantify the fracture and vuggs and locate the potential water breakthrough. High definition resistivity images conveyed with electrical cable has been used for decades to quantify fracture geometry and vuggs. However, the well's risk such as potential stuck and mud losses will be high when the well has been depleted and has high deviation well. Recent development technology of logging while drilling high definition resistivity images has pixel resolution 0.25". The borehole image was acquired through an array of eight buttons that has the pixel sizes to 0.25" for each button. LWD high definition imaging tool has an advantage of scanning 360 deg along the borehole's circumference without any gap along the borehole. The images acquired while drilling then processed with advance processing to quantify fracture geometry and classification of the porosity in the carbonate system for further optimization on inflow control device position and geometry.

Brunei Shell Petroleum (BSP) has a complex production gathering network that comprises of multiple onshore and offshore Assets. Gas and oil is produced under different pressure regimes, and with varying compositions. The production from each asset can be routed in multiple ways to different customers, each with their own compositional requirements. Crude oil and gas is routed to Brunei Liquified Natural Gas (BLNG) and Seria Crude Oil terminal (SCOT) for further processing before export to external customers. Apart from export, other customers include BMC (Brunei Methanol Company), Brunei Shell Refinery (BSR) and Department of Electrical Services (DES), fueling Brunei's vehicles, homes and local industries. Currently this complex production network is managed by a dedicated Operations Control Centre (OCC). The central OCC team coordinates, monitors and controls the hydrocarbon activities to meet customers' quality and quantities demand by engaging with the 4 individual Asset Operation teams, as well as customers, to optimize production at the Pan-BSP system level. With the introduction of the Integrated Production Monitoring and Optimisation System (IPMOS) tool, the Pan-BSP production optimisation can be automated, in real-time. The IPMOS tool performs a high-level production optimisation built on top of existing real-time well production surveillance models, and it dynamically adjusts the system capacity in the event of upsets. In this way, the tool provides day to day advisory of production optimisation during both normal operations as well as during different production upset scenarios, and it also eliminates the need for OCC team to maintain multiple MS Excel sheets/macros to cater for those scenarios. This is a step forward in production system optimisation through digitalisation.