Output is the core index of oil and gas enterprises, and the management of output is very important to the operation of oil and gas enterprises. Each year, oil and gas companies will develop a production allocation plan according to well productivity. Under the guidance of the allocation plan, the annual production target would finally be achieved. In the digital transforming stage of development and production management of oil and gas fields, the traditional offline manual production allocation can no longer meet the needs of delicacy management. For this reason, based on the development and production management platform, a system of production allocation and management was established and deployed in an international oil and gas company. In this paper, through the information platform concept model, introduced the development and production management platform construction method. Based on the platform architecture, taking production allocation and management as an example, the system construction process is introduced from the aspects of business process splitting, system function design, data and software integration call, etc. Furthermore, through the integration of intelligent oil and gas project results based on the platform architecture, the integrated application of intelligent workflow to core business activities in the production allocation and management process is realized. The platform has been fully applied by a large natural gas production company. Through this platform, a management mode with "business standardization, process standardization, and execution standardization" was adopted to realize the comprehensive transformation of production allocation and management from the traditional mode to the digital one. The application of the platform shows that: the process of production allocation and management optimized in process-driven way is traceable and can form a business closed loop; through process reorganization and integrated application, the efficient business collaboration across specialities and departments has been achieved, improving the scientific decision-making ability and the level of business management;

In the event of offshore oilfield blow-out, real-time quantification of both spilled volume, recovered oil and environmental damage is essential. It is due to costly recovery and restoration process. In order to develop a robust and accurate quantification, we need to consider numerous parameters, which are sometimes tricky to be identified and captured. In this work, we present a new modeling technique under uncertainty, which accommodates numerous parameters and interaction among them. We begin the model by identifying possible parameters that contributes to the process: grouped into (1) subsurface, (2) surface and (3) operations. Subsurface e.g. well and reservoir characteristics. Surface e.g. ocean, wind, soil. (3) Operations e.g. oil spill treatment blow-out rate, oil characteristics, reservoir characteristics, ocean current speed, meteorological aspects, soil properties, and oil-spill treatment (oil booms and skimmers). We assign prior distribution for each parameter based on available data to capture the uncertainties. Before progressing to uncertainty propagation, we construct objective response (amount of recovered oil) through mass conservation equation in data-driven and non-intrusive way, using design of experiment and regression-based method. We propagate uncertainties using Monte Carlo simulation approach, where the result is presented in a distribution form, summarized by P10, P50, and P90 values. This work shows how to robustly calculate the amount of recovered oil under uncertainty in the event of offshore blow out. There are several notable challenges within the approach: 1) determining the uncertainty range in blow-out rate in case of rupture occurs in the well, 2) obtaining data for wind and ocean current speed since there is an interplay between local and global climate, and 3) accuracy of capturing the shoreline geometry. Despite the challenges, the results are in-line with the physics and several recorded blow-out cases. Through sensitivity analysis with Sobol decomposition, we can define the heavy hitters. These heavy hitters give us knowledge on which parameters should be aware of. In real-time quantification, this analysis can provide an insight on what treatment method should be performed to efficiently recover the spill. We also highlight about the sufficiency of the model to obtain several parameters’ range, for example blow-out rate. The model should at least capture the physics in high details and incorporate multiple scenarios. In the case of blow-out rate, we extensively model the well completion and consider leaking due to unprecedented fractures or crater formation around the wellbore. We introduce a new framework of modeling to perform real-time quantification of offshore oil spills. This framework allows inferring the causality of the process and illustrating the risk level.

The advancement of technology in this era has long profited the oil and gas industry by means of shrinking non-productive time (NPT) events and reducing drilling operational costs via real-time monitoring and intervention. Nevertheless, stuck pipe incidents have been a big concern and pain point for any drilling operations. Real-time monitoring with the aid of dynamic roadmaps of drilling parameters is useful in recognizing potential downhole issues but the initial stuck pipe symptoms are often minuscule in a short time frame hence it is a challenge to identify it in time. Wells Augmented Stuck Pipe Indicator (WASP) is a data-driven method leveraging historical drilling data and auxiliary engineering information to provide an impartial trend detection of impending stuck pipe incidents. WASP is a solution set to tackle the challenge. The solution is anchored on Machine Learning (ML) models which assess real-time drilling data and compute the risk of potential stuck pipe based on drilling activities, probable stuck pipe mechanisms, and operation time. The output of the analysis is built on a warning and alarm system that can be utilized by the engineers to refine and optimize the well construction activities; tackling the stuck pipe issue before it manifests. This solution is evaluated by comparing historical and real-time drilling parameters with the prediction data to generate an error analysis. On top of that, a confusion matrix is tabulated based on the analysis of warnings and alarms raised by the solution to rule out Type 1 and Type 2 errors. The WASP solution has demonstrated tolerably accurate predictions of drilling parameters with minimal warnings and alarms error. With the solution, the stuck pipe issue can be identified hours earlier before the actual stuck pipe was reported in the historical well. It is a powerful tool with the capability to pinpoint possible stuck pipe mechanisms for engineer's immediate analysis and intervention. Value creation from the WASP solution has been massive with a reduction in manhours of analysis, potential NPT events, and unexpected operational costs. Data-driven techniques are effective in preventing stuck pipe incidents and will be scalable to tackle other downhole issues such as loss of circulation, well control, and borehole instability.

This paper highlights the efforts to mitigate the unprecedented situation of truncated gas demand via network modeling development and harmoniously harvesting value generation by its implementation. Global Pandemic in year 2020 resulted into unique situation of steep truncated gas demand due to economic slowdown worldwide. Hence as a prudent operator its deemed necessary to pursue strategic ideas and innovative concepts to manage offshore complex gas network to handle reduced supply demand balance, whilst protecting fulfill technical and contractual obligations also by optimizing value generation. It therefore demands the development and implementation of robust integrated system (end to end value chain system for hydrocarbon molecule) that would be leveraged on for agile response for deploying appropriate resolution considering dynamics of supply/demand balance and system equilibrium. This study focuses on a state of art that was commenced to develop an End to End Holistic Network Model from well head (fields) to product delivery terminal to scrutinize the complex offshore facilities to decipher appropriate pain points in terms of capabilities, risks, uncertainty, opportunity and exposures by performing robust analysis for trouble shooting, root cause analysis, gap analysis and expansion strategies for required scenario(s). A novel approach was influenced to create simulation model for complex network with building components i.e. source (100+ fields), sinks (multiple terminals), connectors (120+ export pipelines and ∼8 gas highways) along with pressure boosters (pump/compressor) etc. embedded in model. Major hubs, sub-hub, spill-over pipelines/loop lines including main gas transporting facilities with dedicated receiving terminal which formed integral part of network were also modeled in single platform. Flow co-relations for hydraulic estimations and material balance calculations along with engineering thermodynamics formulae for seamless data transfer in collaboration with operations were inbuilt for representative and resilient results. Simulation model was further validated with actual plant data as history matching and that precise forecasting analysis output. Multiple scenarios utilizing system ullage/ pipeline hydraulics (adhering to first principles) were studied and suitable alteration in operating philosophy e.g. were proposed to cater the truncated demand and to shape development strategies for future portfolios. Multi-level diagnostic was conducted to assure that system parameters such as operating pressure, velocity limits and required quality specifications are within operating envelope for the entire landscape. Lookahead analysis (what-if scenarios) were performed to evaluate to root cause analysis and troubleshoot at various intensities of the network to cater for equilibrium balance. Multiple contemplating scenarios were accomplished to analyze complex network parameters such as ullage opportunity, pressure variations, hydraulic fluxes, potential choking of low-pressure wells/fields & prospective blending specifications with variations in the supply/demand outlook. Gap analysis was executed in addition to arrive at necessary alterations for operating philosophy, partial segregation of system for pressure balancing due to low flow volume and product quality adherence. Model output assisted to gauge the potential for operating network by implementing appropriate reforms to optimize truncated flow in system and ensuring system is above its minimum turndown rate flow regime and could also propose to have the necessary mitigations to be in place for vigorous liquid management system due to low flow in network. Above methodology describes how by developing an end to end network model that summarize the granularity of a complex offshore network has facilitated to steer the agile response for operating envelope to cater the fluctuations in the demand/supply balance and optimize offshore allocations by network balancing for value maximization and to form a vision of portfolio strategy for future developments.

This paper describes a methodology that can be used to estimate the potential value of implementing digital and automation technologies in the well construction process in the context of a complex deepwater environment during the drilling conceptualization phase. This serves as a guideline for those interested in quantifying the value of applying digitization and automation processes, not only to make informed decisions related to investment in drillship or systems hardware and software but as well as performance improvement.

Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recognize equipment failure; avoid unplanned downtime; repair costs and potential environmental damage; maintaining reliable production, and identifying equipment failures. Machine learning-artificial intelligence application is being studied to develop predictive maintenance (PdM) models as innovative analytics solution based on real-data streaming to get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. This paper proposes novel machine learning predictive models based on extreme learning/support vector machines (ELM-SVM) to predict the time to failure (TTF) and when a plant equipment(s) will fail; so maintenance can be planned well ahead of time to minimize disruption. Proper visualization with deep-insights (training and validation) processes of the available mountains of historian and real-time data are carried out. Comparative studies of ELM-SVM techniques versus the most common physical-statistical regression techniques using available rotating equipment-compressors and time-failure mode data. Results are presented and it is promising to show that the new machine learning (ELM-SVM) techniques outperforms physical-statistics techniques with reliable and high accurate predictions; which have a high impact on the future ROI of oil and gas industry.

Natural gas is the noble fuel of 21st century. Consumption increased nearly 30% in last decade. Exploitation of conventional, unconventional, and contaminated gas resources are in focus to meet the demand. There are number of giant gas fields discovered worldwide and some of them with higher degree of contaminants viz. CO 2 , H 2 S and Hg. Additionally, they have operating challenges of high pressure and temperature. It becomes more complex when discovery is in offshore environment. This study presents the development and production, separation, transportation and identification & evaluation of storage sites and sequestration and MMV plan of a giant carbonate gas field in offshore Malaysia. Geological, Geophysical and petrophysical data used to describe the reservoir architecture, property distribution and spatial variation in more than 1000m thick gas bearing formation. Laboratory studies carried out to generate the rock and fluid representative SCAL (G-W), EOS and Supercritical CO 2 -brine relative permeability, geomechanics and geochemical data for recovery and storage estimates in simulation model and evaluating the post storage scenario. These data are critical in hydrocarbon gas prediction and firming up the number of development wells and in the simulation of CO 2 storage depleted carbonate gas field. Important is to understand the mechanism in the target field for storage capacity, types of storage- structural and stratigraphic trapping, solubility trapping, residual trapping and mineral trapping. Study covers methodologies developed for minimization of hydrocarbon loss during contaminants separation and utilization of CO 2 in usable products. Uncertainty and risk analysis have been carried out to have range of solution for production prediction and CO 2 storage. Coupled Simulation studies predict the production plateau rate and 5 Tscf recovery separated contaminants profile and volume > one Tscf in order to have suitable geological structure for storage safely forever. Major uncertainties in the dynamic and coupled geomechanical-geochemical dynamic model has been captured and P90, P50, P10 forecast and storage rates and volumes have been calculated. Results includes advance methodologies of separation of hydrocarbon gas and CO 2 like membrane and cryogenics for bulk separation of CO 2 from raw gas and its transportation in liquid and supercritical form for storage. Study estimates components of sequestration mechanism, effect of heterogeneity on transport in porous media and height of stored CO 2 in depleted reservoir and migration of plume vertically and horizontally. Generation of chemical product using separated CO 2 for industrial use is highlighted.

This study presents a hybrid approach that combines data-driven and physics models for worn and sharp drilling simulation of polycrystalline diamond compact (PDC) bit designs and field learning from limited downhole drilling data, worn state measurements, formation properties, and operating environment. The physics models include a drilling response model for cutting forces, worn or rubbing elements in the bit design. Decades of pressurized drilling and cutting experiments validated these models and constrained the physical behaviour while some coefficients are open for field model learning. This hybrid approach of drilling physics with data learning extends the laboratory results to application in the field. The field learning process included selecting runs in a well for which rock properties model was built. Downhole drilling measurements, known sharp bit design, and measured wear geometry were used for verification. The models derived from this collaborative study resulted in improved worn bit drilling response understanding, and quantitative prediction models, which are foundational frameworks for drilling and economics optimization.

The Coalbed Methane in Australia Surat Basin is main gas source for LNG project in east coast of Australia [ 1 ] . Traditionally, Coalbed Methane are drilled by vertical wells. But there are big intensively farmed land coverage in the Surat Basin, the multiple wells on single, larger pad from the surface, the bores slant away at around 70 degrees to intersect multiple, thin coal seams are applied to avoid the extra "footprint" and decrease the environment affect. Many pad wells production failure because of poor interburden isolation. Excessive solids production in pad wells resulted in new failures of holes in tubing due to accelerated erosion, which bring big challenges for the Coalbed methane production in deviated pad wells. The gas production in pad wells are analysised and the new pad wells optimization are proposed. First, the complete wire log (at least include GR and density log curves) need to acquire for correct thin coal seams correlation and locate the interburden sandstone position for future good sandstone isolation. Second the customized completion strategy and placement (swell packer) are applied in the pad wells and specialized tubing with enhanced erosion resistance to extend the run life. Thirdly ESP pumps and optimized tubing are installed in new deviated wells for good gas production. After the pad wells were put into production, the gas production was kept well for long time without pump problems. Swell able packer completion significantly eliminates sand problem by isolating in excess of or close to 80% of interburden sand. The above well completion and artificial lift optimization methods bring good production performance for the new pad wells and contribute much production for the producing gas field. The swellable packer completion also can be used in vertical wells and will be standard well completion methods for future gas development wells.

The concept of minimum economic field size (MEFS) has been used by explorationists for almost four decades. MEFS is often the only filter to distinguish between a commercial and a non-commercial discovery—far before a wildcat well is drilled—to test a prospect for a working petroleum systems hypothesis. As simple as it gets, the concept started to lose traction in the 21 st century as subsurface targets became more and more challenging. In the case of tight hydrocarbons, it is fairly common to observe a P90 case net present value (NPV) to be negative, a P50 case to be positive, and a P10 case to be negative again. The reason for this outcome is that a whole set of full-cycle factors, in addition to the field size, affects prospect commerciality. Their uncertainty ranges can match or exceed resource estimate uncertainty. These factors include, but are not limited to, initial productivity of development wells, estimated eltimate recovery (EUR) per well, decline curve parameters, capital investments, operating costs, and the project phases’ durations. A new way of handling the full universe of risks and uncertainties faced by modern explorers is already available in the new generation of industry-leading integrated prospect risk, resource and value assessment software. Innovators and thought leaders can already substitute MEFS with a commerciality threshold (CT) that neatly mimics board considerations at the final investment decision (FID) stage gate. Others can consider the economic chance of success (ECOS) estimated with a probabilistic full-cycle mindset, as an additional metric valuable for risk management purposes. Using fictional case studies inspired by real-life assessment situations, we discuss the additional value creation by a CT-powered workflow as compared to an MEFS-based one and explain the reasons for the key differences. The discussed workflow does not eliminate nature-specific uncertainties; neither does it reduce the geological risk. However, it helps to better understand human-controlled risks and prepare management exploration decisions with a greater degree of confidence.

In the past few years, over hundreds of wells were drilled in Gulf of Thailand, had faced with the depletion and lost circulation issues resulted from a lack of pressure data. A prior research of reservoir depletion pressure ( Fangming, 2009 ) in oil field, China was obtained from multivariate statistic and regression by using density and neutron porosity log curves in logging-while-drilling data. However, the relative errors are 7.5% from the actual formation pressure. Thus, there are several latent variables in the model like drilling parameters ( Rehm, 1971 ) which part of formation pressure. From 2018 initiative model in Satun-Funan, the classification model was obtained by using mud gas, porosity, water saturation, net sand thickness, net-hydrocarbon-pore thickness and neutron-density separation. However, the limitation is drilling parameters could not account by classifier, and accurate only original pressure category. So, this study has expanded scope to include other reservoir properties and drilling parameters then applied with machine learning on offset well dataset by using three regressors such quantile, ridge and XGBoost regressors. The pore pressure estimation model aims to improve efficiency for making decision in execution phase, increasing confidence in perforation strategy. The model parameters, pay thickness, porosity, water saturation, original pressure from local pressure profile and total gas show are accounted into this model. As of regressor assumption, some facts are conducted to logarithm and perform 2nd polynomial feature for model flexibility. There are three steps for building model such as data manipulation, analysis and deployment. Two purposes of pressure prediction impact algorithm selection, for operational phase, quantile regressor is implemented to provide conservative prediction while Ridge or XGBoost regressors are alternatives for perforation strategy, provide mid case result of pressure prediction. Overall model performance was measured using root mean square error (RMSE) on train & test dataset which show approximately 1.2 and 1.5 ppg range of accuracy respectively from total 12 drilling projects in Pattani basin. Overall model fitting is within reasonable range of generalization capacity to apply with unknown data point (test set). The future model will continue to improve accuracy and manage imbalanced dataset between original pressure and depleted sands.

As a resourced based economy, Malaysia relies heavily on the energy oil, and gas industry - a critical sector contributing to the economic growth of the Malaysian economy; which makes up in the range of 20% - 25% of the total gross domestic product (GDP) of Malaysia as of 2017. No analysts can properly predict prices of the future, with the highs and lows of crude and natural gas and renewables as the fuel of the future and are perhaps new way of things. This "new normal" in which countries, including Malaysia, must learn to adapt in a more agile manner to the "new way of work" of increased productivity and efficiency ( de Graauw, McCreery, & Murphy, 2015 ). In adapting to the new normal, measures of increased productivity must continue to be pushed forward and implemented. Energy companies and services provider still need to continue with exploration and development (E&P) operations and activities to meet long term strategic objectives and demands of the nation, in line with the aspirations of the national oil company, however, it needs to add more value to every dollar spent as margins have continued to shrink and reduce profit margins of energy producers. This is where Digital Transformation comes into play and the urgency for implementation has gone from novelty solutions to critical business survival. Changing industry trends such as Industrial Revolution 4.0 have made it more prevalent than ever to make better use of capital at a time when productivity is essential. At the same time, the industry needs to continue to explore and develop to meet long-term demands, which continues to grow albeit slower than before.

This paper discusses the workflow and method of selecting optimum number of infill and injection wells based on incremental recovery. Normally, for infill wells study, a ‘creaming curve’ method is used to evaluate the optimum number of wells against incremental recovery from the field. However, in the case of determining number of infill wells together with water injection wells, a more comprehensive approach is needed. One needs to evaluate the pressure depletion rate from existing and infill wells together with the dynamic of the producer-injector pairings as well as the sweeping factor. The paper is based on infill and water injection development plan for a brown field in Sabah basin which located in Malaysia. To maintain operatability of the field in the future, several new infill and water injection wells options are evaluated for optimum field life oil production. Unlike infill or producer-only assessment, the same ‘creaming curve’ approach for combination of infill and water injection wells is less effective as large number of simulation runs are needed to sample the combination of these wells that generate optimum oil recovery. This has proven to be challenging especially when the models are large which is normally the case for brown fields and it requires extensive computational hours. In the first part, a modified approach bringing some pre-analytic assessment of producer-injector pairing is being used. The pairings are first ranked based on streamlines visualization, drainage tables and their respective contributions towards oil recovery. The ‘creaming curve’ is then built based on the highest contribution as well as the sequencing of the pairings. The second method mentioned in this paper is the numerical approach through multi-objective optimization using assisted history matching and uncertainty tool. With the help of optimizer, the number of simulation runs can be drastically reduced when only best combination of infills and injectors for each total number of wells are considered. Both alternative methods will be compared with the full computational runs, sampling every single combination of wells. Finally, the optimum number of wells with the combination of infill and water injection wells are analysed based on cumulative oil recovery against the Net Present Value (NPV). This case study therefore demonstrates how alternative methods can be used to resolve the optimum number of infill and water injection wells to avoid lengthy and very large numbers of simulation runs.

This paper demonstrate a unique combination of techniques and equipment that enabled dynamic reservoir evaluation process using simplified Drill Stem Test (DST) string and completion accessories. The well testing was conducted on a shallow slanted offshore well, drilled into faulted reservoirs with multilayer and complex fluids environment. Key technical challenges to perform well testing includes designing a custom DST string to cater for the multilayer reservoir and articulating a surface well testing equipment that capable of efficient separation to ensure safe and environmental friendly disposal while having accurate flowrate measurements, to deliver good interpretable data given that the uncertainty and complexity of the formation and the well itself. During drilling campaign, contingency plan to mitigate against losses was implemented which had a significant impact on the well testing program. As such, uncertainty-based well test design and interpretation methodology was used to address this and to achieve well objectives. This involved numerical model analysis considering reservoir uncertainties and their interaction with each other, to identify which parameters can be interpret confidently and to indicate the test duration for the well testing program. Since the area is nearby to producing fields, several cases model based on reservoir pressure regime was also constructed during the design stage to tolerate flexibilities for the decision tree. The well testing was successfully conducted result from integrated approach to well test design and realtime data support throughout the operation along with innovative DST string design, customize completion accessories for multiple zones testing and adaptive intervention tools for highly deviated well. Matching with nearby wells were also conducted during monitoring to predict future pressure behaviour which allow for the duration of final build-up to be optimized. Given that Health, Safety and Environment (HSE) is the top of priority, an important aspect of the surface well testing package was the water treatment equipment to treat the produced water from reservoir before being discharge in order to guarantee safe environmental disposal. The well was successfully test at maximum flowrate 2,000bpd of oil and 20MMscf/d of gas with traces of produced water. Data gathered thru the Tubing Stem Test (TST) can used to interpret reservoir parameters and all the well testing objectives were successfully achieved despite the many challenges encountered during the drilling campaign and design stage. The end results may contradict traditional testing methods for pressure transient analysis, but hopefully this paper might create the opportunity to replicate TST as quick and effective reservoir evaluation in other parts of the world.

Unconventional reservoirs such as shale and tight sandstones that with ultra-low permeability, are becoming increasingly significant in global energy structures ( Pejman T, et al., 2017 ). For these reservoirs, successful hydraulic fracturing is the key to extract the hydrocarbon resources efficiently and economically. However, the intrinsic mechanisms of fracturing growth in the tight formations are still unclear. In practice, fracturing design mainly depends on hypothetical models and previous experience, which leads to difficulties in evaluating the performance of the fracturing jobs. Therefore, an improved method to optimize parameters for fracturing is necessary and beneficial to the industry. In this paper, a data-driven approach is used to evaluate the factors that dominate the production rate from tight sandstone formation in Changqing Field which is the largest oil field in China. In the model, the input parameters are classified into two categories: controllable parameters (e.g. stage numbers, fracturing fluid volume) and uncontrollable parameters (e.g. formation properties), and the output parameter is the accumulated oil production of the wells. Data for more than 100 wells from different formations and zones in Changqing Field are collected for this study. First, a stepwise data mining method is used to identify the correlations between the target parameter and all the available input parameters. Then, a machine learning model is developed to predict the well productivity for a given set of input parameters accurately. The model is validated by using separate data-sets from the same field. An optimize algorithm is combined with the data-driven model to maximize the cumulative oil production for wells by tuning the controllable parameters, which provides the optimized fracturing design. By using the developed model, low productivity wells are identified and new fracturing designs are recommended to improve the well productivity. This paper is useful for understanding the effects of designed fracturing parameters on well productivity in Changqing Oilfield. Furthermore, it can be extended to other unconventional oil fields by training the model with according data sets. The method helps operators to select more effective parameters for fracturing design, and therefore reduce the operation costs for fracturing and improve the oil and gas production.

One of the greatest challenges for wells using an artificial lift system (ALS) is the ability to optimize production while minimizing expenditure. This process involves a perfect balance between effective oil production management, extending ALS longevity, and ensuring energy efficiency, to produce the lowest operational cost. In oil fields under secondary recovery and mature or depleting conditions, production optimization involves continuous monitoring and surveillance of operational variables affecting changes in flow rates caused by surface and subsurface conditions, such as water injection, pressures, obstructions, etc. To achieve the desired goal of a properly optimized process, strong technical expertise is required almost continuously. Based on years of experience in ALS monitoring, surveillance and optimization, various tools and software have been developed to assist in parameter monitoring. Traditionally, the well optimization of progressive cavity pumps (PCP's) is completed on a daily or weekly basis through the analysis of continuous monitoring and surveillance data by a technical expert. As an alternative, downhole (DH) gauges have been installed to directly measure the exploitation conditions, however, such gauges can be costly relative to the production lifting method. To improve the process, well optimization control units have been produced to maximize oil production using algorithms. These systems take advantage of surface variables such as velocity, current, torque, flow rate, and wellhead and casing pressures to respond to changes in operational conditions, ensuring maximum system uptime and reducing intervention costs by increasing the mean runtime between failures. This paper will focus on the well monitoring and optimization system implemented in a YPF well in Neuquen, Argentina. The theoretical bases, concerns, benefits and results will be discussed throughout. The evaluated well used a surface flow meter to allow the system to converge to an optimal flow rate by changing velocity while considering system performance in terms of submergence, torque, current, and wear between the tubing and rod string at high operating speeds. Post-implementation, by optimizing the flow rate, the oil production doubled while reducing the manhours required, ultimately increasing profit while reducing operational costs. The results of implementing this well monitoring and optimization system have been validated through field trial, creating a trusted method to extend throughout Argentina fields and aiding production engineers in their optimization targets.

The completion of a highly deviated well involves overcoming significant deployment challenges during the drilling operations that require precise and effective conveyance and intervention. The conventional slickline intervention is unsuitable for wells with more than 60° deviation. The operator has sought to implement efficient, reliable and cost-effective deployment methods in delivering injector well. Thus, the operator decided on the e-line tubing tractor conveyed with e-line key and an e-line stroking tool. A tubing tractor and mechanical key and stroker were used to convey the wireline key in highly deviated wells. The key and stroker tools are latched into the sliding side doors (SSDs). They will activate open or close SSDs by down-strokes or up-strokes. In particular, the SSDs are closed when it is required to pressure up the tubing to set the packers. After the packers are set, an integrity test is conducted to confirm zonal isolation. Finally, the SSD is shifted open by the tubing tractor and a low rate injection test is performed to confirm the status of the SSD before handover the well. The operation had successfully installed multiple zones injection completions (MZC) in a highly deviated well and complemented the new completion design for the sand control in water injection well. The e-line tubing tractor and well key/stroker tools have met all operational and budgetary expectations. The traditional intervention methods in highly deviated wells, such as coil tubing, can be costly and potentially infeasible due to a footprint constraint on the drilling rig. The completion was successfully installed without any HSSE issues and the lesson learnt was recorded for future interventions when a change of injection zones is required. For a water injector completion design, equipment was selected based on reservoir requirements i.e. sand control, injection rates and pressure, etc. The goal was to prevent sand from flowing into the tubing when water injection is temporarily paused. To address this concern, the team designed and implemented a cost- effective Autonomous Inflow Control Device (AICD) with bypass valves equipped with SSDs for injection zone selectivity. This first well has been on injection for more than two years with no sand observed in the tubing or declines in the injection rate. The e-line tubing tractor and well key/stroker tools enabled the success of this operations and should be an option for completions in highly deviated wells. Additionally, this is the first time an AICD with bypass valves has been installed for a water injection well in the Gulf of Thailand. The success achieved with this operation in the Nong Yao field provides operators with a new solution for dealing with the water injection in the unconsolidated reservoirs.

For any given Exploration oil and gas portfolio and associated opportunities, successful business decisions can only be made on the basis of technically robust estimates of the subsurface risk versus the resource potential and estimate of the associated upside(s). Ideally, these estimates should incorporate the entire spectrum of opportunities for the complete portfolio and they should be made in a consistent and comparable way. However, as Explorers, we are often faced with data that is incomplete, limited, of variable quality, and/or inconsistent. As a result, subsurface evaluation may be seen more as educated guessing rather than a robust science of evaluation grounded in facts and defensible logic. In their January 2020 paper "Randomness, serendipity, and luck in petroleum exploration" authors Milkov and Navidi (Ref 1 .) go quite a bit further and demonstrate that luck is a significant factor in the exploration success equation. They also showed a general lack in long-term consistency in exploration results of individual companies. Indeed, after we looked at PETRONAS’ own historical POSg versus actual technical success rates and observed only a fair to poor relation between actual technical success rate and the pre-drill POSg estimate. A similar—albeit less worrisome—observation was made for volume ranges and fluid phase predictions. Clearly, there is, and always has been, a phenomenal challenge for the Petroleum Geoscientists to provide the sought after estimates as accurately as possible, and we were no exception. In a bid to improve on this PETRONAS set out on a more disciplined approach to characterizing subsurface uncertainties on its conventional exploration efforts. Over time, more accurate characterizations of such exploration risk and resource potential have followed from a series of procedural guidelines, enhanced capability training program and careful governance of exploration workflows. With improved capabilities and workflow consistency, our evaluation teams have delivered substantially better subsurface evaluations. This in turn has led to more confident decision-making on e.g., individual drilling decisions and new play entries. While our newly implemented workflows are not groundbreaking in isolation, in combination they have delivered notable success and an improved ability to shape the future growth for PETRONAS. In this article, we will highlight the main contributing changes and demonstrate that the overall improvements are indeed impactful. We are not directly challenging the article of Milkov et. al., but are convinced that professionalism and scientific discipline is the deciding factor in the Exploration success equation .

Advances in digital technologies have the potential to enhance model predictive capability and redefine its boundaries at various scale. Digital oil with accurate representation of atomistic components is a powerful tool to analyze both macroscopic properties and microscopic phenomena of crude oil under any thermodynamic conditions. Digital oil model presented in this paper is the key input in molecular chemistry modeling for designing chemical enhanced oil recovery formulation. Hence, it is constructed based on a fit-for purpose strategy focusing in oil components that have large contribution to microemulsion stability. Complete crude oil composition could comprise over 100,000 components. Lengthy simulation time is required to simulate all crude oil components which is impratical, despite the challenges to identify all crude oil components experimentally. Therefore, we established a practical experimental strategy to identify key crude oil components and constructed the digital oil model based on surrogate components. The surrogate components are representative molecules of the volatiles, saturates, aromatics and resins. Two-dimensional digital oil model, with aromaticity on one axis, and the size of the molecules on the other axis was constructed. We developed algorithm to integrate nuclear magnetic resonance response with architecture of the molecular structure. A group contribution method was implemented to ensure reliable representation of the molecular structure. We constructed the digital oil models for a field in Malaysia Basin. We validated the physical properties of the digital oil model with properties measured from experiment, predicted from molecular dynamics simulation and calculated from quantitative property-property relationship method. Good agreement was obtained from the validation, with less than 5% and 13% variance in crude density and Equivalent Alkane Carbon Number respectively, indicating that the molecular characteristic of the digital oil model was captured correctly. We adopted the digital oil model in molecular chemistry modeling to gain insights into microemulsion formation in chemical enhanced oil recovery formulation design. Digital oil is a robust tool to make predictions when information cannot be extracted from experimental data alone. It can be extended for engineering applications involving processing, safety, hazard, and environmental considerations.

The Daman marginal field is a prolific gas-producing clastic field with highly unconsolidated Paleo-Miocene sandstone formations and a wide variety of lithologies across multistack sand layers. As such, high-rate water packs (HRWPs) are the ideal completion method in many Mumbai fields. Because multistack reservoirs require good zonal isolation, and to prevent crossflow between reservoirs with different pressure regimes, multistack sand exclusion (MSSE) methodology was selected for primary well completions with minimum rig time and a high degree of treatment placement accuracy. From an operational standpoint, exploiting these layers using this method means more control points can be achieved across these heterogeneous layers, and the MSSE completion is ideal for multiple applications in a shorter period, helping sustain sand-circumscribed gas production from these unconsolidated layers. During the design phase, grain-size distributions and core study defined the sand range from generally clean, coarse, and sorted to poorly sorted, with high-fines content and clay rich. To address the unique challenges of deep offshore operations, formation technical difficulties, high-stakes economics, and the significant untapped potential from these Daman sands, the MSSE approach was designed and implemented in this field. Historically, for multistack wells, an HRWP is performed zone by zone whereby the process of sump packer installation, perforation run, deburr run, screen assembly installation, and pumping is repeated for each zone. In Well A, the MSSE system was applied without any repetition and all in one phase. All layers were perforated and positively isolated. Each interval was individually opened for the HRWP treatment using a low-friction low-residue carrier fluid. Using a high-packing-factor proppant at a higher rate, the well was treated sequentially from the bottom of the interval to the top. Many marginal fields in this basin have become uneconomical because of the high cost and complexity of sand control methodology. Therefore, reducing costs and time becomes vital to help ensure economic viability, as well as achieving significant operational efficiencies. Additionally, reducing near-wellbore (NWB) mechanical skin and ensuring good productivity from the reservoir are among the major solutions when implementing an MSSE completion. The methodology adopted significantly helped reduce expenditures by standardizing completion design, simplifying the core complexity, and enhancing overall reliability and operational efficiency. The optimized engineering workflow was fit for purpose, rather than the conventional “cookie-cutter” method to address sanding propensity in this field. This paper discusses the cutting-edge MSSE completion systems that focused on downhole completion and modifications for pumping operations. Additionally, the paper reviews challenges addressed during this campaign, workflow adapted, detailed strategy success factors, and positive results obtained during evaluation. This has helped reduce potential risks and improve reliability and performance, which can act as best practices and can be applied within similar fields.