Connecting Data, Model and Process to Streamline and Enhance Single-Point Production Well Test Validation

In this paper we will present a process streamlined for well-test validation that involves data integration between different database systems, incorporated with well models, and how the process can leverage real-time data to present a full scope of well-test analysis to enhance the capability for assessing well-test performance. The workflow process demonstrates an intuitive and effective way for analyzing and validating a production well test via an interactive digital visualization. This approach has elevated the quality and integrity of the well-test data, as well as improved the process cycle efficiency that complements the field surveillance engineers to keep track of well-test compliance guidelines through efficient well-test tracking in the digital interface. The workflow process involves five primary steps, which all are conducted via a digital platform: Well Test Compliance: Planning and executing the well test Data management and integration Well Test Analysis and Validation: Verification of the well test through historical trending, stability period checks, and well model analysis Model validation: Correcting the well test and calibrating the well model before finalizing the validity of the well test Well Test Re-testing: Submitting the rejected well test for retesting and final step Integrating with corporate database system for production allocation This business process brings improvement to the quality of the well test, which subsequently lifts the petroleum engineers’ confidence level to analyze well performance and deliver accurate well-production forecasting. A well-test validation workflow in a digital ecosystem helps to streamline the flow of data and system integration, as well as the way engineers assess and validate well-test data, which results in minimizing errors and increases overall work efficiency.

Intelligent Operating Envelope Integrated with Automated Well Models Improves Asset Wide PCP Surveillance and Optimization

Petroleum Development Oman (PDO) has been a pioneer in improving Well management processes utilizing its valuable human resources, continuous improvement and digitalization. Managing several PCP wells through Exception Based Surveillance (EBS) methodology had already improved PCP surveillance and optimization across assets. The key to trigger EBS was to keep Operating Envelope (OE), Design Limits updated in Well Management Visualization System (WMVS) after every change in operating speed (RPM), workover and new completion. The sustainable solution was required for automatic update of OEs, having well inflow potential and oil gain opportunities available for quicker optimization decisions for further improvements. PDO has completed a project automating PCP well modeling process where models are built and sustained automatically in Well Management System (WMS) for all active PCP wells, with huge impact on day-to-day operational activities. The paper discusses utilization of physics based well models from WMS to automatically update OE, identify oil gain potential daily and enable real time PCP performance visualization in WMVS. The integration of WMS and WMVS was completed to share data between two systems and automatically update well's OE daily. A tuned well model from WMS was utilized to provide well performance data and sensitivity analysis results for various RPMs. Among the various data obtained from WMS, live OE of torque and fluid above pump (FAP) for various speeds, operating limits, design limits, locked in potential (LIP) for optimization and pump upsize were utilized to process PCP well EBS and create live OE visualization. The visualization is created on a torque-speed chart where a live OE and FAP can be observed in provided picture with current RPM and torque with optimum operating condition. The project is completed after conducting successful change management across PDO assets and after thorough analysis of implementation following benefits were observed: 5% net gain of total PCP production is being executed with zero CAPEX using LIP reports. 50% of engineer's time was saved by updating OEs in WMVS automatically, reduction of false EBS and EBS rationalization. 200% improvement in PCP well performance diagnostics capabilities of Engineers. 15% CAPEX free optimization and pump upsize cases were identified based on well inflow potential. 100% visibility to PCP well's performance was achieved using well model. The visualization has supported engineers monitoring well performance in real time and easily identifying ongoing changes in well and pump performance. PCP well models have supported engineers in new PCP well design and pump upsize. The current efforts in utilizing real time well models, inferred production, automating processes to update OE is one more step toward Digitalization of PCP Surveillance and optimization and to achieve self well optimization for further improving operational efficiency.

Surface Runoff Management Model of Bandung Regency Through Remote Sensing Analysis

Flood is caused by surface runoff, therefore controlling the surface runoff is necessary especially on built areas. The aim of this research is to analyze the width, calculating the volume of surface runoff and analyze the model of infiltration wells on built areas in Bandung Regency. The methods implemented in this research is experimental method. This method was carried while analyzing rainfall on built areas samples, which is house building. The land use was analyzed through Landsat 8 imagery in the year of 2019. Rainfall volume was calculated by equation V = h x l. Meanwhile the volume of infiltration well was calculated by equation V = h x k. The result of 2019 Landsat imagery analysis shows that 19.01% of total watersheds in Bandung Regency or as much as 1382.13 km2 is the built areas. The highest rainfall in total of 0.02431 m occurred in October on the area of 197.67 m2 and became a surface runoff of 377,534 m3. In a house building, as built area example, as wide as 90 m3, the amount of 2.19 m3 rainwater needed to be infiltrated. Infiltration well model is a management model on each built areas, so that rainwater on built areas would not be turned into a surface runoff.

Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations

The paper examines new horizontal drift-flux correlations for their ability to accurately model phase flow rates and pressure drops in horizontal and undulating wells that are part of a Steam-Assisted Gravity Drainage (SAGD) field operation. Pressure profiles within each well correlate to the overall performance of the pair. SAGD is a low-pressure process that is sensitive to reservoir heterogeneity and other factors, hence accurate simulation of in situ wellbore pressures is critical for both mitigating uneven steam chamber evolution and optimizing wellbore design and operation. Recently published horizontal drift-flux correlations have been implemented in a commercial thermal reservoir simulator with a multi-segment well model. Valid for horizontally drilled wells with undulations, they complement previously reported drift-flux models developed for vertical and inclined wells down to approximately 5 degrees from horizontal. The formulation of these correlations has a high degree of nonlinearity. These models are tested in simulations of SAGD field operations. First, an overview of drift-flux models is discussed. This differentiates those based on vertical flow with gravity segregation to those that model horizontal flow with stratified and slug flow regimes. Second, the most recent and significant drift-flux correlation by Bailey et al. (2018, and hereafter referred to as Bailey-Tang-Stone) was robustly designed to be used in the well model of a reservoir simulator, can handle all inclination angles and was optimized to experimental data from the largest available databases to date. This and earlier drift-flux models are reviewed as to their strengths and weaknesses. Third, governing equations and implementation details are given of the Bailey-Tang-Stone model. Fourth, six case studies are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios.

A Multi-Level Non-Linear Solver for Complex Well Modelling

Complex well model has proved to be important for capturing the full physics in wellbore, including pressure losses, multiphase effects, and advanced device modelling. Numerical instability may be observed especially when the well is produced at a low rate from a highly productive multi-phase zone. In this paper, a new multi-level nonlinear solver is presented in a state-of-the-art parallel complex wellbore model for addressing some difficult numerical convergence problems. A sequential two-level nonlinear solver is implemented, where the inner solver is used to address the convergence in the constraint rate equation, and then the entire complex network is solved using an outer solver. Finally, the wellbore model is coupled with the grid solution explicitly, sequentially, or implicitly. This novel formulation is robust enough to greatly improve the numerical stability due to the lagging in the computation of mixture density in wellbore constraint rate equation and the variation in the fluid composition over Newton iterations in network nonlinear solver. The numerical challenge in the complex well model and the improvement of performance with the new nonlinear solver are demonstrated using reservoir simulation. Models with complex wells running into convergence problems are constructed and simulated. With this novel nonlinear solver, simulation gives much more reliable results on well productions without numerical oscillations and computational cost is much less.

An Integrated IR4.0 Software Enables Autonomous Casing Crossflow Rate Calculation

This paper will present an alternative calculation technique to predict wellbore crossflow rate in a water injection well resulting from a casing leak. The method provides a self-governing process for wellbore related calculations inspired by the fourth industrial revolution technologies. In an earlier work, calculations techniques were presented which do not require the conventional use of downhole flowmeter (spinner) to obtain the flow rate. Rather, continuous surface injection data prior to crossflow development and shut-in well are used to estimate the rate. In this alternative methodology, surface injection data post crossflow development are factored in to calculate the rate with the same accuracy. To illustrate the process an example water injector well is used. To quantify the casing leak crossflow rate, the following calculation methodology was applied:Generate a well performance model using pre-crossflow injection data. Normal modeling techniques are applied in this step to obtain an accurate model for the injection well as a baseline case.Generate an imaginary injection well model: An injection well mimicking the flow characteristics and properties of the water injector is envisioned to simulate crossflow at flowing (injecting) conditions. In this step, we simulate an injector that has total depth up to the crossflow location only and not the total depth of the example water well.Generate the performance model for the secondary formation using post crossflow data: The total injection rate measured at surface has two portions: one portion goes into the shallower secondary formation and another goes into the deeper (primary) formation. The modeling inputs from the first two steps will be used here to obtain the rate for the downhole formation at crossflow conditions.Generate an imaginary production well model: The normal model for the water injector will be inversed to obtain a production model instead. The inputs from previous steps will be incorporated in the inverse modeling.Obtaining the crossflow rate at shut-in conditions: Performance curves generated from step 3 & 4 will be plotted together to obtain an intersection that corresponds to the crossflow rate at shut-in conditions. This numerical methodology was analytically derived and the prediction results were verified on syntactic field data with very high accuracy. The application of this model will benefit oil operators by avoiding wireline logging costs and associated safety risks with mechanical intervention.

Production Optimiser Pilot for the Large Artificially-Lifted and Mature Samotlor Oil Field

This paper describes a production optimiser Pilot, developed by Rosneft/Samotlorneftegaz, with support from bp and deployed in JSC Samotlorneftegaz - a vast, mature, water-flooded, high water-cut and artificially-lifted oil field. Objectives include creating a digital twin for a sub-system of 600 wells and ~180 km of pipeline network, applying discrete, continuous and constrained optimisation techniques to maximise production, developing sustainable deployment workflows, implementing optimiser recommendations in the field and tracking incremental value realisation. This proof-of-concept Pilot and field trial approach was adopted to understand the optimisation technology capability and work-flow sustainability, prior to a field-wide roll-out. The periodic optimisation activity workflows include the creation of a "Digital Twin", a validated surface infrastructure model that is fully calibrated to mimic field performance, followed by performing optimisation that includes all the relevant constraints. Optimisation was trialled using two different classes of algorithms – based on sequential-modular and equation-oriented techniques. This strategy minimises optimisation failure risks and highlights potential performance issues for such large-scale systems. Optimiser recommendations were consolidated, field-implemented and values tracked. The optimiser Pilot development was undertaken during the fourth quarter of 2019. The delivered minimum viable product and workflows were used for field trials during 2019-20 and continuously improved based on the learnings. Specialists from both bp and Rosneft, along with three consulting organisations (1 in Russia and 2 in the UK) collaborated and worked as one-team to deliver the Pilot. Optimiser recommendations for maximising production include continuous and discrete decisions such as ESP frequency changes, high water-cut well shut-ins and prioritised ESP lists for installing variable speed drives. Field production increase of 1% was achieved in 2020 and tracked. Enduring capabilities were built, and sustainable work-flows developed. Field-wide optimisation for Samotlorneftegaz is non-trivial due to the sheer size, with over 9,000 active wells and due to continuously transient operations arising from frequent well-work, well shut-in's, new well delivery, pipeline modifications and cyclic mode of operations in some wells. This Pilot has provided assurance for the optimisation technical feasibility and workflow sustainability. A second Pilot of similar complexity but with different pressure-flow system response is planned. The combined results will help to decide about the full-field roll-out for this vast field, which is anticipated to deliver around 1% of additional production. This Pilot has demonstrated the applicability of discrete and continuous variable constrained optimisation techniques to large-scale production networks, with very high well-count. Furthermore, the developed workflows for configuring and calibrating the digital twin have several unique components including automation of hydraulic network model generation from static data, well model build automation and fit-for-purpose automated well model calibration. Overall, the results of this approach demonstrate a viable and sustainable methodology to optimise large-scale oil production systems.

Automation & Digitalization Implementation – A New Normal in Oil & Gas Industry: A Success Story

Nowadays automation and digitalization in oil & gas industry have become a new normal practice to replace traditional workflows. The implementation of automation and digitalization is driven by the need to automate the repetitive and low-cognitive tasks, so it allows engineers to spend more time on high-cognitive and high-level analytical evaluations or studies, and to finally lead up to smarter decisions. One of the solutions is by developing and implementing "fit for purpose" automation tools which consist of various data analytics inside the tools. Saka Energi Indonesia, as the operator of Pangkah PSC, has developed and implemented automation and digitalization in Ujung Pangkah field. Located in northern side of East Java, the field's reservoir consists of multi-layered carbonate oil and gas zone, which is being produced through horizontal and directional wells. Solutions of automation and digitalization have been developed for the Ujung Pangkah field to minimize loss opportunity, increase oil production and reduce the field decline rate. With some collaboration efforts from Subsurface, Operation and IT Department Team, some automation tools have been developed and implemented in Ujung Pangkah Field, which are as follows: Exception Based Surveillance (EBS) tool: An automation tool to identify real-time well problems & opportunities. Auto Gas Lift Rate Allocation (GALAA) tool: An optimization tool to automate gas lift rate allocation. SAKA Well Opportunity, Register, Define and Select (SWORDS): An automation tool to evaluate well opportunity portfolio. Well Model Update Automation: A tool to update well model automatically for every individual well. By implementing the automation solutions, various repetitive tasks can be completed significantly faster and more efficiently. Saka engineers have more time to perform high-cognitive analytical evaluations on other technical areas. Ujung Pangkah field oil production decline rate has been successfully decreased from 21%/year to 8.6%/year after the automation solutions have been implemented in 2018. Ujung Pangkah success story of automation & digitalization implementation will be used as a reference for managing other Saka assets in different fields. The new automation solutions are a faster and a more efficient way of optimizing existing field production and it will give positive impacts exponentially with increasing well numbers.

INFLUENCE THE RIBS IN NEW MODEL OF CHANNEL PATH ON HYDRO-THERMAL PERFORMANCE IN SERPENTINE MINI- CHANNEL HEAT SINK

The aim of the present study is to enhance of heat transfer in mini channel heat sink. To reach the target of this study a new models of mini channel heat sink is proposed the traditional model, model (A) and model (B). Both model (A, B) has straight serpentine mini channel heat sink with inlet at the center, the difference between them is that model(B) has ribs within channel, the diameter of ribs is 1 mm with distance between ribs 5mm. A 3D (ANSYS Fluent program) 2019R3 is used to obtain the numerical outcomes. A good agreement is found when compared the experimental outcomes from the literature review and the numerical results of the traditional model in the current study. Furthermore, the findings demonstrate that the formation of flow has a great impact on heat transfer, Though the model with serpentine straight channel improve the pressure drop, thermal resistance, Nusselt number and the distribution of temperature in the base of heat sink. The overall performance factor (OPF) for new models under study are superior than conventional model. In addition, the average OPF for model (A) is [1.43] and for model (B) is [1.33] as compared with traditional model. As well, model (A) is superior as compared with model (B) by 6.99 % due to the effectual uniformity in the base of heat sink and efficient OPF.

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.