bluuebunny/crossref_metadata_2025 huggingface dataset:
Casing Twist Insight Through Fiber Cable
Authors: Samuel Robello , Wood Stuart , Olin Greg
Published: September 2021

During perforating operations, identifying the orientation of fiber cable accurately is critical for maintaining the integrity of permanently installed fiber.Beyond completions,it alsoprovides insights into how the casings get twisted and how the mechanical stability of the casing is altered as the string is run in the hole. The drilling and completion system is as unique as the aspect ratio and length/diameter is very high. This puzzles the researchers in modeling forces, stresses, stretch, and twists.

To aid the accurate prediction in the position of the casing, radial orientation of downhole fiber optic cables canbe used. The clear images obtained by mapping the equipmentoutside thecasing provides not only how the casings get twisted after running in but also provide improved risk mitigation for perforating operations.The orientation angle of the casing versus depthis then analyzed to get the finaltwist and pitch of the twist of the casing. Several wells datawere analyzed to get a comprehensive view of the casingtwist as the casings were run and versus the model prediction.

The raw data obtained using the pulsed-eddy current time-domain decay at each station are used for the analysis. Each installed cable detection clamp (CDC) is placed above a casing centralizer located 2' above each joint of casing that had a clamp installed.This simplifies the process of locating the depth of each CDC. A casing collar locator easily identifies the casingjoints.Further, the data are used to find the casing rotation. Several wells showed normal casing rotation of 2–3 wraps along the lateral and onewell showed more than 12 wraps. Several reasons were considered and analyzed including the wellbore spiraling, borehole torsion,and additional mechanical forces applied duringrunning the casing. The coupling of the geometrical and mechanical twist and mechanical stability of the string are discussed in the paper withmathematical underpinnings. In thecase of abnormal prediction, additional mechanical forcesandgeometrical considerations were overlapped and comparedagainst the torque and drag model prediction.It has also beenfound that in some wells where the wellbore torsion washigh,it resulted in a complete twist of 360° atthe heel and in some cases negative trend.
Severe Casing Failure in Multistage Hydraulic Fracturing Using Dual-Scale Modelling Approach
Authors: Yu Hao , Dahi Taleghani Arash , Lian Zhanghua , Mou Yisheng
Published: September 2021

Field evidence of production logs after fracturing have documented the existence of abundant natural fractures in Weiyuan shale plays, which is widely acknowledged to have a positive impact on fracture network complexity. On the other hand, cases of severe casing failures have been frequently reported in this field during multistage fracturing jobs. Stress interference between two adjacent stages may intensify non-uniform loading on the casing string and accommodate failure. To better understand this problem, we establish a coupled 3D reservoir-scale model with complex well trajectory and tie it to a single well-scale model consisting of casing and the surrounding cement sheath. Using this model, we investigate the potential impacts of cement deficiency, clustered perforations, fracture geometry as well as spacing strategy on casing integrity. Our simulation results indicate that cement deficiency could intensify the load nonuniformity around the borehole which escalates the potential threats for casing failure. When cement deficiency reaches 45° along the minimum horizontal stress, it has the largest influence on the stress level in the casing. In addition, perforations could lower the casing strength, but the reduction may not change furthermore when the perforation diameter reaches a certain value. Moreover, impacts of fracture geometry and spacing on casing deformation are investigated. We conclude that the lower ratio of fracture length to its width and reasonable spacing strategy could help reduce the load non-uniformity on casing which avoid the casing deformation. The described workflow may be adopted in other areas to predict the possible casing failure problems induced by multistage hydraulic fracturing with cheap computational costs, to anticipate the challenges and avoid them by revisiting pumping schedule or spacing strategy.
Casing Wear: Prediction, Monitoring, Analysis and Management in the Culzean Field
Authors: Aichinger Florian , Brillaud Loic , Nobbs Ben , Couliou Florent , Oyovwevotu Joy , Mathieson Graeme , Vavasseur David , Hardie Jamie
Published: September 2021

Objectives/Scope

This paper will present predicted vs. measured wear for six wells that were analysed in the Culzean field, which is a high-pressure, high-temperature (HPHT) gas condensate field located in the central North Sea.

The focus rests on the casing wear prediction, monitoring and analysing process and within that, especially on how to make use of offset well data to improve the accuracy of casing wear predictions.

Methods

The three major inputs to successfully predict casing wear are: Trajectory & tortuosity, wear factor and required rotating operations. All those were calibrated based on field measurements (High-resolution gyro, MFCL (Multi-Finger-Caliper-Log) and automatically recorded rig mechanics data), to improve the prediction quality for the next section and/or well.

The simulations were done using an advanced stiff-string model featuring a 3D mesh that distinguishes the influence of different contact type and geometry on the wear groove shape.

The "single MFCL interpretation method", in which the wear is measured against the most probable elliptical casing shape and herby allowing wear interpretation with only one MFCL log and avoiding bias error, was applied. (Aichinger, 2016)

Results, Observations, Conclusions

For the six wells that were analysed the prediction of the largest wear peak per well section was compared to the measurement. In the planning phase (before any survey data was available) the mean absolute error on the wear groove depth was +/- 0.018 [in] (+/- 0.46 [mm]), the maximum error was + 0.045 [in] (+ 1.1 [mm]). The error of the results is summarized in Figure 10 and laid out in detail in Figure 9. Generally, the predictions are accurate enough to be able to manage casing wear effectively.

In this case, the maximum allowable wear on the intermediate casing was extremely limited to ensure proper well integrity in case of a well full of gas event while drilling an HTHP reservoir.

Novel/Additive Information

This paper should provide help to engineers who seek to improve the accuracy of casing wear prediction and hence improve casing wear management. It presents a new way of anticipating tortuosity based on offset well data and it offers a suggestion on how to deal with MFCL measurement error during wear factor calibration and wear prediction.
Practical Applications of Diagnostic Data Science in Drilling and Completions
Authors: Senters Chad , Jayakumar Swathika , Warren Mark , Wells Mike , Harper Rachel , Leonard Richard , Woodroof Robert
Published: September 2021

The application of data science remains relatively new to the oil and gas industry but continues to gain traction on many projects due to its potential to assist in solving complex problems. The amount and quality of the right type of data can be as much of a limitation as the complex algorithms and programing required. The scope of any data science project should look for easy wins early on and not attempt an all-encompassing solution with the click of a button (although that would be amazing). This paper focuses on several specific applications of data applied to a sizable database to extract useful solutions and provide an approach for data science on future projects.

The first step when applying data analytics is to build a suitable database. This might appear rudimentary at first glance, but historical data is seldom catalogued optimally for future projects. This is especially true if specific portions of the recorded data were not known to be of use in solving future problems. The approach to improving the quality of the database for this paper is to establish requirements for the data science objectives and apply this to past, present and future data. Once the data are in the right "format", the extensive process of quality control can begin. Although this part of the paper is not the most exciting, it might be the most important, as most programing yields the same "garbage in = garbage out" equation. After the data have found a home and are quality checked, the data science can be applied.

Case studies are presented based on the application of diagnostic data from an extensive project/well database. To leverage historical data in new projects, metrics are created as a benchmarking tool. The case studies in this paper include metrics such as the Known Lateral Contribution (KLC), Heel-to-Toe Ratio (HTR), Communication Intensity (CI), Proppant Efficiency (PE) and stage level performance. These results are compared to additional stimulation and geological information.

This paper includes case studies that apply data science to diagnostics on a large scale to deliver actionable results. The results discussed will allow for the utilization of this approach in future projects and provide a roadmap to better understand diagnostic results as they relate to drilling and completion activity.
Autonomous Control of Well Downtime to Optimize Production and Cycling in Sucker Rod Pump Artificially Lifted Wells
Authors: Nickell Ian , Treiberg Terry
Published: September 2021

For decades sucker rod pump artificially lifted wells have used devices called pump off controllers (POC) to match the pumping unit's runtime to the available reservoir production by idling the well for a set time where variable frequencies drives are not available. In doing this the POC allows the well to enter a set period of downtime when the downhole pump fillage is incomplete to avoid premature failures, and then brings the well back online to operate before production is lost. Although this method has been successful for several years, autonomous control algorithms can be utilized to reduce failures or increase production in cases where the downtime is not already optimized. Optimizing the idle time for a sucker rod pump artificially lifted well involves understanding the amount of time required to fill the near wellbore storage area before generating a fluid column above the pump intake that will begin to hinder inflow from the reservoir into the wellbore. By varying the idle time and observing the impact on production and cycles the program hunts for the optimal idle time. By constantly hunting for the optimal idle time the optimization process can adjust the idle time when operating conditions change. This gives the advantage of always meeting the current well bore and reservoir conditions without having to have a user make these changes and determine what the downtime for the well is. Autonomously modulating the idle time for a well, if done properly will either reduces incomplete fillage pump strokes, in cases where the idle time is too short, or will increase the wells production in cases where the idle time is too long. Overall this will result in the optimization of wells by reducing failures and/or increasing production, generating a huge value to the end user by automating the entire process of downtime optimization.
A Graph Network Based Approach for Reservoir Modeling
Authors: Sun Wenyue , Sankaran Sathish
Published: September 2021

Reservoir management routinely requires assimilating historical data and predicting field performance against multiple production strategies before implementing them in the field. However, traditional numerical methods are often cumbersome to characterize, build and calibrate at a timescale that can be used reliably for such short-term decision cycles such as production forecasting, IOR optimization and production rate control. Simpler analytical models make assumptions and lack the rigor needed to adequately model these systems. Pure data-driven methods may lack physical insights or have limited range of applicability. Model fidelity, speed, interpretability, suitability to automate and ease-of-use are some key modeling traits that are desired for reservoir management purposes.

In this work, we propose to use a reservoir graph-network modeling approach (RGNet), based on the concept of diffusive time of flight, to forecast well performance using routinely measured field measurements (e.g. bottomhole pressure and rates). We propose a novel, model order reduction method based on discretized time of flight for multiple wells with interference. It simplifies the 3D reservoir flow problem into a flow network representation that can be solved as a 2D simulation model with any general-purpose reservoir simulator. Parameters in RGNet model cover well productivity index, grid pore volume and transmissibility, which are estimated through a history-matching process. After history matching, multiple posterior RGNet models are generated to quantify subsurface uncertainties. The RGNet modeling approach allows various fluid-flow physics to be modeled within the grids and boundary conditions, and is applicable to a range of conventional and unconventional reservoirs with different flow mechanisms.

We applied the proposed approach on a field case reservoir models for multiple wells with interference. By virtue of the reduced complexity, the modeling methodology is highly scalable and still retains physical interpretability. The calibration method produces parsimonious models and provides uncertainty estimates in history matching parameters with range of outcomes. In addition, the RGNet models are much faster to simulate, over 1000x speed up, compared with full-physics models. We then used RGNet models for well-control and flood optimization and achieved significant improvement over field net-present-values.

Parameterization of the proposed reservoir graph-network modeling approach provides a unique and sustainable way to reduce model complexity needed for reservoir management purposes. The method is rooted in physical principles and provides an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.
Well Completion Optimization in Unconventional Reservoirs Using Machine Learning Methods
Authors: Baki Sohrat , Temizel Cenk , Dursun Serkan
Published: September 2021

Unconventional reservoirs, mainly shale oil and natural gas, will continue to significantly help meet the ever-growing energy demands of global markets. Being complex in nature and having ultra-tight producing zones, unconventionals depends on effective well completion and stimulation treatments in order to be successful and economical. Within the last decade, thousands of unconventional wells have been drilled, completed and produced in North America. The scope of this work is exploring the primary impact of completion parameters such as lateral length, frac type, number of stages, proppant and fluid volume effect on the production performance of the wells in unconventional fields.

The key attributes in completion, stimulation, and production for the wells were considered in machine learning workflow for building predictive models. Predictive models based on Neural Networks, Support Vector Machines or Decision Tree Based ensemble models, serves as mapping function from completion parameters to production in each well in the field. The completion parameters were analyzed in the workflow with respect to feature engineering and interpretation.

This analysis resulted in key performance indicators for the region. Then the optimum values for the best production performing completions were identified for each well. Predictive models in the workflow were analyzed in accuracy and best model is used to understand the impact of completion parameters on the production rates. This study outlines an overall machine learning workflow, from feature engineering to interpretation of the machine learning models to quantify the effects of completion parameters on the production rate of the wells in unconventional fields
Advanced Modeling Capability to Enhance Near-Wellbore and Far-Field Bridging in Acid Fracturing Field Treatments
Authors: Khan Abdul Muqtadir , Emelyanov Denis , Romanovskii Rostislav , Nevvonen Olga
Published: September 2021

Different applications of fracture bridging and diversion are used regularly in carbonate acid fracturing without an in-depth understanding of the physical phenomena that dominate the processes involved in the bridging and diversion process. The extension of modeling capabilities in conjunction with yard-scale and field-scale experiences will increase our understanding of these processes.

A robust multimodal diversion pill and polylactic acid fiber-laden viscous acid were utilized for near-wellbore and far-field bridging, respectively. Numerous field treatments demonstrated the uncertainty of achieving effective diversion. An existing multiphysics model was extended to develop functionalities to model diversions at different scale. Extensive laboratory testing was conducted to understand the scale of bridging and diversion mechanisms. Finally, a bridging yard test was designed, and field case studies were used to integrate all the branches.

Field cases showed a diversion pressure up to 4,000 psi depending on perforation strategy, pill volume, and pill seating rate. Correlations showed the interdependence of multiple parameters in diversion processes. The field studies motivated modeling capabilities to simulate the critical diversion processes at high resolution and quality. The model simulates diverting agents that reduce leakoff in the fracture area and their effects on fracture geometry. The approach considers the acid reaction kinetics coupled with geomechanics and fluid transport. Different diverting agent concentrations required for bridging can be modeled effectively. A yard test was designed to confirm the integrity of the pill material through completion valves (minimum inside diameter 9.5 mm) and analyzed with high-resolution imaging. All the theoretical, mathematical, and numerical findings from modeling were integrated with laboratory- and yard-scale experimentation results to develop and validate near-wellbore and far-field diversion modeling. Analytical correlations were formulated from injection rate, particulate material concentration, pill volumes, fracture width, etc., to incorporate and validate the model.

This study enhances understanding of the different diversion mechanisms from high-fidelity theoretical modeling approach integrated with a practical experimental view at laboratory and field scale. Current comprehensive research has significant potential to make the modeling approach a reliable method to develop tight carbonate formations around the globe.
Digitization of Drill Bit Inspections; User-Centered Design Methods to Automate Robotic Inspections
Authors: Ma Marrie , Murphy Jeremy , Salman Nader , Li Zhen , Chatar Crispin , Chatagnier Justin
Published: September 2021

One unique facet of digital technology is the merging of separate technologies for new workflows and products. Like other industries, energy is also doing this. This project will automate the bit inspection process and this system will reduce labor costs, increase product quality, and improve bit performance. The innovation center is working on various aspects of the project, which aims to join automation technologies with robotic capabilities.

Industrial robots are used extensively in traditional high-volume manufacturing applications. The high-mix, low-volume nature of oil and gas manufacturing operations has impeded deployment of automation solutions. Recent advances in sensors, computers, and machine learning now enable integrating robotics and automation technologies into these flexible manufacturing workflows. Driven by digital transformation, an automated inspection system for polycrystalline diamond compact (PDC) drill bits has been developed. The system uses high-resolution robotic 3D scanning, 2D imaging, and artificial intelligence to improve inspection efficiency and product quality. In our user-experience- (UX-) focused approach, we streamlined the user interface (UI) research methods to develop the robotic inspection UI and successfully tested the design with end users. This paper introduces the inspection system and improved workflows for the PDC bits, illustrates the innovative UX/UI development process, and targeted evaluation with the end users, which is crucial before deploying the system in production. We also concluded with some recommended improvements to guide future work.
Gas Lift Design Optimization Using Intelligent Gas Lift Valves a KJO Case Study
Authors: Al Qahtani Abdullah , Al-Aklubi Sultan , BenAmara Abdel , Faux Stephen
Published: September 2021

Gas lift is becoming a big consideration in most of oil field as an economic, sustainable means of artificially lifting weak/dead oil wells. This is especially considered in high volume wells. Gas lift is employed, by injecting gas into the well tubing through gas lift valves, to reduce the hydrostatic pressure of the produced fluid column in oil wells, leading to a lower flowing bottom-hole pressure. The increased pressure differential induced across the sand face from the in situ reservoir pressure, assists in lifting the produced fluid to the surface.

Optimizing the level of injected gas is important in maximizing the production, and hence the financial performance of the well. The challenge for most oil and gas producers is that they do not effectively maximize production with the most efficient use of gas lift resources. The challenge is that there is a lack of accurate and timely production data from the well tests. The optimal inject rate for a well is based on a ratio of injected gas rate to the liquid production rate. Under injecting the gas decreases the well production rate. The objective of optimization in gas-lifted wells is to achieve optimal production rate with minimal gas injection volume to spare gas for other wells, when the compression capacity is limited. Optimally allocated injection gas helps reduce unnecessary strain on your facility and maximize performance, this in turn enhances the life of production assets significantly.

This paper presents a case study from Khafji Joint Operation fields, utilizing the intelligent digital gas lift valve to optimize the design and performance of the gas lift wells. The case study demonstrates the value proposition by using the digital intelligent gas lift system to maximize well performance whilst reducing injected gas, in addition to acquired real-time data that help assess the process. That optimization was achieved on well level by optimizing the well parameters such as point of injection, injection rate, and injection pressure. All these aspects have been investigated and presented in this study by using field data and flow simulations. Results showed the potential added value of the system.
Toe Initiation Sleeve With Time-Delay Functionality Improves Operational Efficiency of Offshore NCS Wells
Authors: Mehus Cathrine , Keerthivasan Vijay Kumar , Koløy Tom Rune , Young Dustin , Sørheim Tore
Published: September 2021

A toe initiation sleeve is a tool installed in the toe of a completion liner and is used to establish a flowpath to the reservoir without the use of intervention. Conventional toe initiation sleeves require either intervention or increasing pressure to higher than the liner test pressure to activate. These methods have inherent cost and operational risks. This paper will present the development, qualification, and deployment of a multicycle, time-delay cementable toe initiation sleeve that allows for interventionless activation without exceeding the liner test pressure. This development greatly improves operational efficiency and eliminates risk associated with conventional toe initiation sleeves.

A major operator in the North Sea required an ISO V0 rated toe initiation sleeve to be developed and qualified. Design criteria for the tool was identified, and the design was developed based on field-qualified seal technology. Individual component and full-scale validation testing was performed to complete the product qualification, followed by field trials in 2019. With its unique time-delay feature, the newly developed ATS (Advanced Toe Sleeve) allows for an unlimited number of pressure cycles to be performed while also keeping the well V0 barrier in place, and activates at below liner test pressure.

This paper will discuss the technology development and implementation project, resulting in ISO 14998 V0-qualified cemented ATS being installed in nearly 40 wells in the same field. This paper will also provide insight into how the ATS provides unique benefits to the operator during various phases of the well's life.

Cementing: One moving part and opening sleeve isolated from the inside diameter (ID) allow for pumping darts through the ATS without the risk of opening Setting liner/testing liner: Time-delay features allow for setting liner and testing the liner at higher pressures than ATS opening pressure. Well cleanup/displacing to lower density fluid: Time-delay function allows for opening the ATS at lower pressure than the well has seen during previous operations. Completion: ATS design and qualification grade reduce completion steps and costs for the operator. Stimulation: ATS eliminates the need for intervention, reducing the operational steps and costs for the operator.

The advanced toe sleeve with built-in time-delay features maintains the liner integrity throughout the various well operations. The number of available pressure cycles can be predetermined, and the activation of the various cycles can be precisely controlled thereby also controlling when the tool is activated to achieve injectivity. This paper will present the development and field-wide implementation of the ATS technology, which has rapidly gained operator acceptance and resulted in significant time and cost savings.
Impact of High Resolution 3D Geomechanics on Well Optimization in the Southern Gulf of Suez, Egypt
Authors: Elkhawaga Mohamed , A. Elghaney Wael , Naidu Rajarajan , Hussen Assef , Rafaat Ramy , Baker Kareem , Radwan Ahmed , Heiland Julian
Published: September 2021

Optimizing the number of casing strings has a direct impact on cost of drilling a well. The objective of the case study presented in this paper is the demonstration of reducing cost through integration of data. This paper shows the impact of high-resolution 3D geomechanical modeling on well cost optimization for the GS327 Oil field. The field is located in the Sothern Gulf of Suez basin and has been developed by 20 wells

The conventional casing design in the field included three sections. In this mature field, especially with the challenge of reducing production cost, it is imperative to look for opportunites to optimize cost in drilling new wells to sustain ptoduction. 3D geomechanics is crucial for such cases in order to optimize the cost per barrel at the same time help to drill new wells safely.

An old wellbore stability study did not support the decision-maker to merge any hole sections. However, there was not geomechanics-related problems recorded during the drilling the drilling of different mud weights.

In this study, a 3D geomechanical model was developed and the new mud weight calculations positively affected the casing design for two new wells. The cost optimization will be useful for any future wells to be drilled in this area.

This study documents how a 3D geomechanical model helped in the successful delivery of objectives (guided by an understanding of pore pressure and rock properties) through revision of mud weight window calculations that helped in optimizing the casing design and eliminate the need for an intermediate casing.

This study reveals that the new calculated pore pressure in the GS327 field is predominantly hydrostatic with a minor decline in the reservoir pressure. In addition, rock strength of the shale is moderately high and nearly homogeneous, which helped in achieving a new casing design for the last two drilled wells in the field.
Bulk Modulus of Hydrocarbon Fluids After Injection with Supercritical CO2 at Reservoir Conditions
Authors: Kandil Mohamed Eid
Published: September 2021

The mechanical properties of hydrocarbon reservoirs significantly depend on the elastic properties of the fluids occupying the pore space in the rock frame. Accurate data and models for the mechanical properties of fluid mixtures in a petroleum reservoir containing supercritical CO2 should be available at the same reservoir conditions for reliable design of well-completion, maximizing reservoir productivity, and minimizing risk in drilling operations. This work investigates the change in the bulk modulus of the higher hydrocarbon fluid (decane C10H22) after the injection with supercritical CO2 at reservoir conditions. The isothermal bulk modulus βT of liquids under pressure, simply defined as the first-order derivative of pressure with respect to volume, is determined in this study from the derivative of pressure with respect to density. The density data were obtained from experimental measurements of mixtures of supercritical CO2 + C10H22 for a range of CO2 mole fractions from 0 to 0.73, at temperatures from 40 to 137 °C and pressures up to 12000 psi. The isothermal derivative coefficients of the pressure as a function of density are reported for each CO2 concentration measured in this work. Common fluid-substitution models, including the Gassmann model, which is only valid for the isothermal regime, have limited predictive power because most fluids are treated as simple fluids, with their mechanical properties only characterized by their densities. However, under different environments, such as when supercritical CO2 is injected into the geological formation, the fluid phase and its mechanical properties can vary dramatically. At high pressure, the density of CO2 can equal to that of the hydrocarbon phase ρ(CO2)/ρ(C10H22) ≈ 1, while the bulk modulus of CO2 remains as low as only βT(CO2)/βT(C10H22) ≈ 7 %. Excessive decrease in the bulk modulus can easily cause subsidence, although the pore pressure and the fluid mixture density remain unchanged, even at pressures up to 4000 psi.
Multiphase Flow Meter Health Monitoring Strategy: Maximizing the Value of Real-Time Sensors and Automation for Industrial Revolution 4.0
Authors: Al-Kadem Mohammad , Gomaa Mohammad , Al Yateem Karam , Al Maghlouth Abdulmonam
Published: September 2021

Metering with Multiphase Flow Meters (MPFMs) is a well proven Intelligent Field technology, which utilizes complex flow models and a radioactive detection mechanism to accurately measure multiphase flow (oil, gas and water) on a real-time basis. The major realized benefits of utilizing MPFM real-time sensor data technology are reduced maintenance costs, optimized maintenance schedules, and improved safety via minimizing visits and predicting well performance.

MPFMs also have the capability to perform comprehensive real-time test validation in a short period of time making them a valuable tool. Leveraging their capabilities, engineers can perform various production analysis scenarios remotely using continuous data streaming to reduce and ultimately eliminate reliance on operators and vendors in remote fields. Nevertheless, such MPFMs require frequent maintenance, which could be quite difficult to manage when having a large quantity of meters.

An in-house application was developed to support those objectives and to remotely monitor the health of all MPFMs on a real-time basis. The in-house analytics system comprises four modules, namely, Field Overview, MPFM Overview, Well Test Management, and Alarm Management with the steps used to avail this kind of solution to cover infrastructure requirements, visualization requirements, optimization and prediction. The solution was designed as the major interface for engineers to detect and prevent major MPFM component failures proactively and to highlight all types of projected MPFM component failures. It proactively harvests preset alarms, which leads to preventing internal MPFM parts from failing. These alarms are generated based on critical parameters measured by the MPFM such as rates, differential pressure, and radioactive measurements. Annual savings are realized due to utilizing the monitoring solution by minimizing costly offshore visits and optimizing the visits to be based on problematic MPFMs.

The authors will discuss a newly developed MPFM health monitoring strategy based on real-time solutions, driving a migration from a preventative to a predictive maintenance concept to reduce costs and optimize manpower time through eliminating unnecessary visits.
Forecasting Reserves Replacement Ratio RRR: A Method for Benchmarking the Ability of the Company to Mature Projects and Reduce Uncertainty
Authors: Salacz Dominique , Allam Farid , Mubarak Al Araimi Wadha , Al Mansoori Yousof
Published: September 2021

Reserves Replacement Ratio (RRR) has been used as a Key Performance Indicator (KPI) for monitoring the ability of IOCs to replace their reserves through time. It provides valuable information about the expected long-term viability of the company and its ability to move forward with new projects. If this remains an important metric for investors and operators alike, it is worth understanding the key parameters behind this KPI. How should we realistically expect the RRR to behave in the future? What does it tell about the reserves and resources of the operator? The objective of this document is to investigate how a widely underused KPI can be analyzed and forecasted based on previous reserves and business plan submissions. We suggest a method to provide teams with relevant and realistic RRR, that can be expected based on the company portfolio.

This method was developed by reviewing the Reserves & Resources in hundreds of reservoirs for all projects, including No Further Activity (NFA) cases and all future development projects. Based on the future profiles, the evaluation of the RRR was forecasted for 10 years. The forecasts are being reviewed year on year to ensure that teams are provided with realistic KPIs based on hard data rather than biased expectations. The RRR is computed using (i) the amount of reserves added during the year, divided by (ii) the production of the company during the same year. This paper will focus on the reserves changes from one year to another, considering that reserves addition (or reduction), comes from two main sources:

Project maturation: as projects are maturing on the SPE PRMS vertical axis, they will eventually become commercial according to the PRMS definition and move from contingent resources to reserves. Project maturation is driven by the asset team and by the company's willingness to move forward with the development. Technical review: as more data becomes available, the reserves of the asset will change, up or down, within (hopefully) the range of uncertainty defined in PRMS. This is driven by the inherent uncertainty in the reservoir. The control that the company has on this parameter is very limited. This document emphasizes on the impact of the uncertainty captured by the asset team during the reserves evaluation exercise, and how it will (should) affect the KPI calculations. Finally, we will discuss what should be a reasonable KPI depending on the PRMS submissions, 1P, 2P or 3P.

Eventually, this method should provide a healthy middle ground between asset teams being too pessimistic in setting their KPIs, and managers who are keen to set challenging, and sometime unrealistic, targets
Designing Tools To Improve Rod Pumping Performance In Hostile Production Conditions
Authors: Fu Zhijun , Zhu kuanliang , Wang Lei , Xu Jingjing , Wang Qian , Wang Jinzhong , Wang Lingling , Liu Yufei
Published: September 2021

In oil and gas industry, it is inevitable that the developed reserve will gradually become exhausted. Under such circumstance, in order to stabilize oil production and meet increasing energy demand, we have no choice but to improve oil recovery from matured field as much as possible, since finding new large reservoir is quite hard in the future. For Jidong Oilfield in China, a lot of method can be used for improving oil production, one of which is deep pumping method by increasing pump setting depth, especially for depleted reservoir.

Deep pumping method can be helpful to lower bottom hole pressure and enlarge drawdown pressure between producing layer and downhole. Not only can this method generate more power to displace oil from reservoir to well and subsequently increase oil drainage area, leading to higher oil recovery, but also can boost pump fillage and finally obtain high production efficiency. Even though, this method still brings many disadvantages. In Jidong Oilfield, we sometimes set the 1.5in pump at over 3000m depth (in this paper, all well related are rod pumping wells), where varied problems happened as followed:
Novel Nano and Bio-Based Surfactant Formulation for Hybrid Enhanced Oil Recovery Technologies
Authors: Ma Qisheng , Xia Wenjie , Tang Yongchun , Haroun Mohamed , Motiur Rahman Md , Gibrata Muhammad , Rouis Lamia , Djanuar Yanfidra , Abdelhadi Eldali Magdi , Ijaz Rabbani Aneesa , Abubacker Ponnambathayil Jassim , Suboyin Abhijith
Published: September 2021

This investigation presents laboratory and field deployment results that demonstrate the potential candidacy utilizing Nano and bio-technologies to create superior chemicals for novel applications to increase oil recovery from both onshore and offshore reservoirs. Nano-technology is gaining momentum as a tool to improve performance in multiple industries, and has shown significant potential to enhance hydrocarbon production. The laboratory analysis and specifically designed coreflood results indicate there are beneficial interactions at liquid-nano solid interface that increase oil mobility. This will increase the surface activity of chemical surfactants and thereby make them the dominant agents to mobilize and recover oil from oil-bearing reservoirs. Advances in biotechnology offer another rich resource of knowledge for surface active materials that are renewable and more environmental-friendly. In addition, our studies also demonstrate that bio-surfactants are well-suited to provide superior performances in enhancing oil recovery.

Nano-particles and biosurfactants may be included with synthetic surfactants to create novel and more efficient surface active agents for enhanced oil recovery. These formulations can promote better flow back of the injected stimulation fluids and additional mobilization to extract more oil from the matrix and micro-fractures. Laboratory experiments demonstrate that the specialized surfactant formulations created, interact with mixed or oil-wet low permeability formations to produce additional oil. Furthermore, this investigation also compares the total production on a candidate field with respect to typical water flood and the novel formulated surfactant approach. For each surfactant treatment, the overall designed injected fluid volume is 1500 m3 (~ 396,000 gallons) with 4 gpt (gallon per thousand unit) of surfactant concentration. Results indicate improved oil production with longer exposure time of the key surfactants within the reservoir. Enhanced surface wetting and super-low interfacial tension (IFT) at lower chemical concentrations are recognized to be the main mechanisms. The novel surfactant also shows stronger sustainability and endurance in keeping rock surface wettability over traditional surfactant system up to 5 times for an 8 PV wash. Furthermore, this can assist to identify and initiate the optimization of the identified mechanisms for potential applications within other compatible reservoirs.

A number of successful field applications of EOR with special formulated nano and bio-based surfactant formulation are discussed in this paper. This unique study bridges the gap between the field realized results and lab optimization to enhance feasibility as a function of time and cost.
Fault Rock Property Prediction On Jurassic Clastics Of The Barents Sea/Norway
Authors: Schuller Volker , Zamolyi Andras , Stueland Eirik , Dunkl István , Kettermann Michael , Schleder Zsolt
Published: September 2021

We analysed the fault rocks of a compartmentalized field in the Barents Sea, in an area with several tectonic elements, which formed at different tectonic events. Standard Fault Seal Analysis (FSA) was conducted to predict the shale content of the fault rock (SGR). A static cellular model based on well data, seismic data and geological concepts served as input. The fault rock calibration workflow required various data acquired by different methods. We analysed the Mid-Triassic to Upper Jurassic clastic deposits to reconstruct the tectonic history. Apatite fission track and (U-Th)/He thermochronology were used to determine the maximum burial depths and exhumation history. The results of high-resolution shale ductility analysis (BIB-SEM), a compaction trend study, kinematic analysis and structural modelling (section balancing) served as additional input constraints for fault rock calibration.

The evaluation of the results helped to reconstruct the following tectonic evolution: The orthogonal faults of the analysed area developed at an early stage, during Late Triassic to Early Jurassic times at relatively shallow depth, below 1000 m. Ongoing subsidence created accommodation space for Upper Jurassic to Cenozoic deposits with a maximum burial depth of 2000 m for the analysed Mid-Jurassic rocks. Exhumation of the area started around 10 Ma and continued through to Quaternary times.

The calculated values for fault rock permeability show a wide range when using poorly constrained input for fault rock calibration: 10 to 1000 mD for SGR values around 0.08 at reservoir/reservoir juxtaposition. Fault rock calibration using above described results concluded in reliable values for fault rock permeability and ultimately, for transmissibility multipliers. The reason for the sensitivity of the fault rock calibration is a combination of multiple factors: highly permeable reservoir sandstone, shallow depth of initial faulting, maximum burial depth and low shale content at the primary reservoir level.

The study shows that an accurate reconstruction of the geohistory provides essential parameters for fault rock calibration and fault rock permeability calculation. The range of values can widely scatter if preconditions are not acknowledged. Well-constrained fault rock calibration reduces the uncertainty on possible flow scenarios, increases the reliability on production forecasts and helps to determine the most efficient drainage strategy.
Characterizing Downhole Fluid Analysis Sensors As Digital Twins: Lessons of the Machine Learning Approach, The Physics Approach and the Integrated Hybrid Approach
Authors: Price Jimmy , Jones Chris , Dai Bin , Gascooke Darren , Myrick Michael
Published: September 2021

Digital fluid sampling is a technique utilizing downhole sensors to measure formation fluid properties without collecting a physical sample. Unfortunately, sensors are prone to drift over time due to the harsh downhole environmental conditions. Therefore, constant sensor evaluation and calibration is required to ensure the quality of analysis. A new technique utilizes a virtual sensor as a digital twin which provides a calibration that can be utilized by the physical twin. Digital twin technology enables the end-user to operate and collaborate remotely, rapidly simulate different scenarios, and provide improved accuracy via enhanced up-to-date calibrations. With respect to downhole fluid identification, the contribution of harsh environmental conditions and sensor drift can also be mitigated by realizing a virtual implementation of the fluid behavior and the individual sensor components. Historically, the virtual behavior of a digital twin has been constructed by a combination of complex multi-physics and empirical modeling. More recently, access to large datasets and historical results has enabled the use of machine learning neural networks to successfully create digital twin sensors. In this paper, we explore the efficacy of constructing a digital twin on a single downhole optical fluid identification sensor using both the machine learning nonlinear neural network and the complex, multi-physics' based modeling approaches. Advantages and lessons to be learned from each individual method will be discussed in detail. In doing so, we have found a hybrid approach to be most effective in constraining the problem and preventing over-fitting while also yielding a more accurate calibration. In addition, the new hybrid digital twin evaluation and calibration method is extended to encompass an entire fleet of similar downhole sensors simultaneously. The introduction of digital twin technology is not new to the petroleum industry. Yet there is significant room for improvement in order to identify how the technology can be implemented best in order to decrease costs and improve reliability. This paper looks at two separate methods that scientists and engineers employ to enable digital twin technology and ultimately identify that a hybrid approach between machine learning and empirical physics'-based modeling prevails.