bluuebunny/crossref_metadata_2025 huggingface dataset:
A Cohesive-Zone Model for Simulating Hydraulic-Fracture Evolution within a Fully Coupled Flow/Geomechanics-Simulation System
Authors: Alpak Faruk O.
Published: November 2020

A modular multiphysics reservoir-simulation system is developed that has the capability of simulating multiphase/multicomponent/thermal flow, poro-elasto/plastic geomechanics, and hydraulic-fracture evolution. The focus of the work is on the full-physics hydraulic-fracture-evolution-simulation capability of the multiphysics simulation system. Fracture-growth computations use a cohesive-zone model as part of the computation of fracture-propagation criterion. The cohesive-zone concept is developed using energy-release rates and cohesive stresses. They capture the strain-softening behavior of deforming porous material consistent with real-life observations of poro-plastic deformation. Thus, they can be reliably used within both poro-elastic and poro-plastic geomechanics applications, unlike the conventional stress-intensity-factor-based fracture-propagation criterion.

The partial-differential equations (PDEs) that govern the Darcy-scale multiphase/multicomponent/thermal flow, poro-elasto/plastic geomechanics, hydraulic-fracture evolution, and laminar channel flow in the fracture are tightly coupled to each other to give rise to a numerical protocol solvable by the fully implicit method. The ensuing nonlinear system of equations is solved by use of a novel adaptively damped Newton-Raphson method.

Example fully coupled single-phase isothermal-flow, geomechanics, and hydraulic-fracture-growth simulations are analyzed to demonstrate the predictive power of the simulation system. Numerical-model predictions of fracture length/radius and width are validated against analytical solutions for plane-strain and ellipsoid-shaped fractures, respectively. Results indicate that the simulation system is capable of modeling hydraulic-fracture evolution accurately by use of the cohesive-zone model as the propagation criterion. We also simulate and explore the sensitivities around a real-life hydraulic-fracture-growth problem by fully accounting for the thermal-, multiphase-, and compositional-flow effects.
Pumpdown Diagnostics for Plug-and-Perf Treatments
Authors: Cramer David D. , Snyder Jon , Zhang Junjing
Published: November 2020

In this paper, we introduce pumpdown diagnostics, an economical process in which cement sheath integrity, perforation cluster spacing, and fracturing (frac) plug integrity can be assessed for every fracturing stage, potentially leading to improvements in stimulation, completion, cementing, and drilling practices. It is based on analyzing wellbore pressure responses occurring at key segments of the wireline pumpdown and perforating operation and correlating the results among multiple fracturing stages and wells in a field or play. A special requirement is that the ball check is inserted in the frac plug and pumped to seat prior to performing perforating operations. A complementary benefit of this process is that selectively establishing injectivity in the most distant perforation cluster can be used to establish inhibited hydrochloric (HCl) acid coverage across all perforation intervals for uniform reduction in near-wellbore tortuosity.

Reviews of pumpdown diagnostics field cases from several unconventional plays provide the following insights. Pumpdown diagnostics are time efficient and economical, requiring approximately 15 minutes per fracturing stage. Evaluating communication to the previous fracturing stage can serve as a key performance indicator for treatment control or cement sheath integrity. Pumpdown diagnostic results can be more reliable than cement bond log evaluation, and stage isolation characteristics can be strongly affected by cluster spacing.
Magnetic Resonance Multi-Phase Flowmeter & Fluid Analyzer
Authors: Deng Feng , Chen Guanhong , Wang Mengying , Xu Dongping , Chen Shiwen , Zhang Xishun , Xiong Chunming , Zhang Jianjun , Lei Qun , Shi Junfeng , Zhao Ruidong , Wang Cai , Sun Yizhen
Published: November 2020

Quantitative information regarding multiphase flow of oil, gas & water in wells or pipelines are very important in continuous well performance monitoring, production optimization, flow assurance, well testing, production allocation, etc. So far, the multiphase flow measurement methods are usually of low efficiency, low accuracy, high cost and delay delivery, and hard to reflect the real transient fluid production performance at wellheads or pipelines. Therefore, it is urgent to seek accurate and reliable multiphase flow detection devices and methods that can meet the monitoring demands of unconventional oil and gas resources.

Breaking through the technical bottleneck, a novel method and device for online multiphase flow detection based on magnetic resonance (MR) is proposed. The flowing MR data acquisition, processing and interpretation methods are proposed to fill the blank of traditional methods. The full bore, straight tube design does not restrict the flow line and eliminates wear of internal parts and pressure drops, the Halbach magnet structure is designed to provide a uniform magnetic field inside the bore while cancelling the magnetic field outside the housing of the device, the separate antenna structure is designed to eliminate flow effects on MR measurement, and the integration of measurement and control technology realizes unmanned operation.

With the good indoor experiment and field application results, it has achieved accurate, reliable, green, in-situ and online detection of multiphase flow.

As a flowmeter, the device reduces the amount of measuring equipment needed and manages wells more efficiently. And as a ‘fluid analysis laboratory’, it can provide first-hand information on oil and gas properties. It also marks another new application direction of MR technology in the petroleum industry after well logging.
Future Roles for Natural Gas in Decarbonising the Australian Electricity Supply within the NEM: Total System Costs are Key
Authors: Byrom Stephanie , Bongers Geoffrey , Boston Andy , Garnett Andrew
Published: November 2020

Electricity systems around the world are changing, with the Paris Agreement of 2015 a catalyst for much of this current change.1 The Australian Government ratified this agreement, by commiting to 26-28% emissions reductions below 2005 levels by 2030.2 Additionally, State governments are adopting more ambitious targets to increase variable renewable energy and focusing on net emissions reductions, while the Australian Energy Market Operator (AEMO) warns of power instabilities and load shedding3. Given the difficulties in reducing emissions in other sectors, reductions in emissions from electricity generation has become the focus of these targets. Natural gas is often referred to as a ‘transition fuel’ towards a low emissions future, but this requires that it is in abundance and cost competitive. While the majority of the electricity generated in the National Energy Market (NEM) is coal-based, a vast majority of these plants are due to retire gradually between now and 2050, and this generation loss will need to be replaced in the context of low emissions aspirations. How this is done has significant implications for how much the system will cost, with natural gas playing a pivotal role. In order to decarbonise the grid to meet targets, while building firm, dispatchable generation capacity to support the system, a new metric is required to measure success.

The changing generation mix, along with the need to maintain a competent grid, is resulting in previously acceptable cost comparison metrics being used outside of their limited range of applicability. Electricity generation facilities do not only provide energy, they also provide an array of additional services which are fundamental to maintaining a permanent and reliable electricity supply across the system. These services, corresponding costs and operational implications need to be included in the evaluation of technologies in order to ensure the grids emerge transformed, resilient and genuinely sustainable. Total System Cost is the most appropriate economic metric for analysis and decision making in a future, low emissions grid.

This paper explores the outputs of the MEGS model (Model of Energy and Grid Services), showing the outcomes if a single technology group is favoured. High renewables, gas and carbon capture and storage scenarios are discussed. The optimal route to power grid decarbonisation needs to be be viewed as a team sport, not a race. It's an "and" not an "or" solution. There's a range of technologies that have very different, yet important, roles to play in providing the pathway to a low emissions, competent and reliable supply at the lowest total system cost.
Evaluation of Ensemble Smoother with Multiple Data Assimilation and Evolutionary Algorithm for History Matching Process Optimization
Authors: Ghamdi Amell , Ganis Santiago , Hammad Khaled
Published: November 2020

History matching is a challenging reverse problem due to the inherited uncertainty from Earth modeling. History matching process mandates building one or more sets of representative reservoir models with minimum misfit between the reservoir models and observed field data. There are several commercial assisted history matching tools and workflows to facilitate and orchestrate this process. This paper provides performance assessment and process evaluation between two of the commonly used optimization algorithms: the Evolutionary Algorithm (EA) and the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in an automated and closed loop workflows.

The workflow starts with a parametrization step to identify and quantify the static and dynamic uncertainties in the reservoir model. The objective function is then constructed to guide and measure the convergence of the optimization process. The workflow is then executed to start the model calibration and history matching process. The algorithms, EA and ES-MDA, take different routes during the optimization process. EA works by selecting the best cases (i.e. most fitting genes that resulted in a minimum misfit) in each cycle, and generates a number of simulation cases (new generation) based on the predefined gene selection criteria. The iterative ES-MDA algorithm maintains the same number of simulation cases in each iteration (as many as in the initial Ensemble) throughout the assimilation process.

The EA and ES-MDA optimization algorithms have their advantages and disadvantages to the history matching process. For instance, both workflows preserve the geological consistency with the dynamic reservoir models. The automation that both workflows provide incorporates G&G (Geological and Geophysical) workflows to capture, and carryover the static uncertainty to ensure consistency throughout the history matching process. In addition, in both cases, multiple simulation models are generated to cover reservoir static and dynamic uncertainties, which are crucial to the predictive capabilities of the history matched models. Both algorithms, as known, are stochastics in nature (i.e. cannot guarantee one deterministic solution) and, therefore. the results are highly dependent on the initial ensemble. EA is highly effective in searching the solution space, it requires a large number of simulation runs to converge a solution. On the other hand, ES-MDA does not exceed the number of initial ensembles in each iteration regardless of the number of iterations.

This paper presents an automated workflow that leads to multiple reliable history matched simulation models. All of the generated models are geologically consistent and capture all model uncertainties that are needed to be preserved and carried forward in prediction. Moreover, this helps in updating the models seamlessly whenever new data is available by simply re-running the workflow with the model updates.
Downhole Surveillance During the Well Lifetime Using Distributed Temperature Sensing
Authors: Ricard Ludovic Paul , Tertyshnikov Konstantin , Pevzner Roman , Myers Matthew , Avijegon Arsham , Michael Karsten
Published: November 2020

Distributed temperature (DTS) and distributed acoustic (DAS) fibre optic sensing are now commonly used as key reservoir surveillance tools. This work shows the benefit of continuous downhole monitoring during the lifetime of a well. Fibre optic cables were permanently installed in a doublet injector/monitor well system as part of a CO2 controlled released experiment at the In-Situ Laboratory in Western Australia. During the completion and injection operations various planned and unplanned events (mud circulation, cementing, drilling, wireline logging, gas and water flows) occurred. The events were monitored from surface to reservoir with DTS and DAS fibre optic cables. The DTS was recorded continuously data starting during well completion throughout the lifetime of the wells while DAS was recorded at specific points in time, mostly associated with borehole time-lapse seismic acquisitions.

For the well completion stage, the interpretation of the DTS dataset acquired during mud circulation provided information about thief zones above the reservoir. During cementing and cement hydration, DTS highlighted areas of large breakouts and confirmed cement in-fill in those intervals. During the drilling of an unexpected cement infill, it provided a unique insight into downhole progression of the drill bit.

During the CO2 injection stage, DTS enabled monitoring the phase behaviour at the injector using distributed temperature data combined with a permanent pressure downhole gauge. At the monitoring well, the injected gas breakthrough was clearly detected at reservoir level within 1.5 day after the start of injection. Moreover, the accumulation of the CO2 over time was captured accurately at reservoir level for a further 3 days. During an unexpected leak in the casing, gas from the reservoir started entering the monitoring well leading to cyclic release of water and gas at the surface. The DAS dataset enabled to pin-point the exact moment the casing leak occurred while the DTS dataset captured the cyclic nature of water and gas leakage. The sensitivity of the tools and interpretation methods are such that the downhole location of the event can be determined within 1 m and the timing within a few seconds. More importantly, the continuous and distributed recording allowed monitoring the events developing in time over the full length of the well.

This work highlights the unique benefit of permanent distributed monitoring using distributed fibre optic sensing during the lifetime of a well. The continuous and distributed recording allowed monitoring events developing in time over the full length of the well and provides direct observations for the different events while providing near-real-time information about downhole processes.
An Integrated Geomechanics and Corrosion Assessment Approach to Analyze Tubular Degradation in Brownfield X
Authors: Sajjad Farasdaq Muchibbus , Wirawan Alvin , Chandra Steven , Ompusunggu Janico Zaferson , Prawesti Annisa , Suganda Wingky , Muksin M. Gemareksha , Amrizal Amrizal
Published: November 2020

Tubular engineering design is essential for production operation, especially in the mature oil and gas fields. The complex interaction among oil, natural gas, and water, complemented with wax, scale, inorganic compound, and deformation brings complexity in analyzing tubular integrity. This challenging problem will be more severe if the wells are located in offshore environment, therefore finding the cause of tubing deterioration is a challenging.

Field X, which has been in production for 30 years, cannot avoid the possibility of tubular thinning and deformation. The degradation is slowly developed until severe alterations are observed on the tubing body. The current state of the wells is complicated since the deformation inhibits the fluid flow and increases the risk of wellbore collapse and complications during sidetracking, infill drilling, workover, and other production enhancement measures. The risks can be harmful in the long run if not mitigated properly.

The current condition encourages us to conduct more comprehensive study on tubular degradation. It is to model the multiple degradation mechanisms, such as corrosion, scaling, and subsidence, under the flowing formation fluid. The model is then coupled with reservoir simulation in order to provide a better outlook on tubular degradation. We used multiple case studies with actual field data to identify the dominant mechanism on tubular degradation. The case study cover various reservoir and fluid characteristics and also operations problems to develop general equation and matrix for risk analysis and field development considerations.

We present the degree of tubular degradation and its effect to overall field performance and economics. Current field practices do not encourage a thorough tubular assessment during early life of the wells, which create complex problem at later stage. The study indicates that a proper planning and preventive action should be performed gradually before tubular degradation becomes severe. The paper presents a field experience-based model and guideline matrix that is useful in developing new areas from the perspective of well and facilities integrity, so that the degradation-related issues could be recognized earlier.
Effect of Nanoparticles on the Interfacial Tension of CO2-Oil System at High Pressure and Temperature: An Experimental Approach
Authors: Al-Anssari Sarmad , Arain Zain-UL-Abedin , Shanshool Haider Abbas , Ali Muhammad , Keshavarz Alireza , Iglauer Stefan , Sarmadivaleh Mohammad
Published: November 2020

In the recent decade, injection of nanoparticles (NPs) into underground formation as liquid nanodispersions has been suggested as a smart alternative for conventional methods in tertiary oil recovery projects from mature oil reservoirs. Such reservoirs, however, are strong candidates for carbon geo-sequestration (CGS) projects, and the presence of nanoparticles (NPs) after nanofluid-flooding can add more complexity to carbon geo-storage projects. Despite studies investigating CO2 injection and nanofluid-flooding for EOR projects, no information was reported about the potential synergistic effects of CO2 and NPs on enhanced oil recovery (EOR) and CGS concerning the interfacial tension (γ) of CO2-oil system. This study thus extensively investigates the effect of silica NPs on the γ of CO2/decane system at elevated pressure and temperature to recognise the potential impact of NPs-injection on the future CGS projects.

To achieve this, a wide-ranging series of tests have been conducted to reveal the role of hydrophilic and hydrophobic silica NPs on γ of the CO2/oil system. n-decane was utilized as model oil and different amounts of NPs were mixed with the oil phase. Oil-NPs dispersions were formulated using an ultrasonic homogenizer. The γ of the CO2/oil system was measured at different pressures (0.1 to 20 MPa) and temperatures (25 to 70 °C) using a high-pressure temperature optical cell. The γ data were measured using the pendant drop technique via axisymmetric drop shape analysis (ADSA).

The results showed that, generally, CO2/oil γ subjected mainly to pressure, temperature, and with less extent to NPs load in the oil phase. γ decreases with increased pressure until reaching a plateau where no more significant decrease in γ was observed. The γ trend with increased temperature, on the other hand, was more completed. No significant impact of temperature on γ was recorded with low pressure (≤ 5 MPa). Similarly, at relatively high pressure (≥ 25 MPa), only a slight variation of IFT with temperature change was recorded. However, for the pressure range from 5 – 25 MPa, IFT was increased remarkably with temperature. Furthermore, NPs in the oil phase exhibit a remarkable influence on IFT. In this context, the presence of hydrophilic silica NPs in the oil phase can significantly reduce the γ of the CO2/decane system. However, hydrophobic silica NPs showed less influence on IFT reduction.

The outcomes of this work afford good understandings into applications of NP for EOR and CGS applications and help to de-risk CO2-geological storage projects.
Instrumented Docking Perforating Gun System: Field Experience Results in Ecuador
Authors: Vasconez Danilo , Pastrana Wilmar , Benavides Marcia , Vallejo Renato , Dahik Carlos
Published: November 2020

Schlumberger implemented the first instrumented platform integrating advanced measurements in real-time while perforating with plug-in gun connections. This paper presents the results obtained at field level using this technology in Ecuador and a comparison of this technology with perforating operations and techniques in terms of efficiency, reliability, and correlation between the predicted and measured dynamic events to perform a full evaluation of the perforating design process, execution and its impact in well performance

Out of more than 120 jobs performed year to date in Ecuador, the authors selected 5 wells covering operations with dynamic underbalance (simultaneous and standalone) and gas fracturing to illustrate the efficiency gains of a faster and more reliable arming system and to analyze the match between simulated and measured dynamic events (pressure and shock). Among the 5 wells, two of them achieved positive production results, and the real time measurements showed additional information in terms of: reservoir pressure, well response, and perforations cleanup during the job execution. The other three wells provided key information related to well control and pressure buildup after perforations, withal they did not provide comparatively consolidated production results.
Can a Cube Model Mitigate Parent-Child Effects and Improve Oil Recovery in the Permian Basin
Authors: Yan Qiyan , Zheng Wei , Biniwale Shripad , Banerjee Raj , Xu Tao , Rakhimova Yulia
Published: November 2020

As the shale development activity in the Permian continues to be strong and oil prices recover, increasing numbers of infill child wells are being drilled as operators want to improve recovery from each section and continue to meet their production targets. However, production data suggests that both parent and child wells suffer from production losses if they are located too close to one another.

The cube model concept, which is also referred to as supersize fracturing, was first introduced about two years ago and has been piloted in the Permian Basin. In a cube model, multiple wells, usually more than 30 horizontal wells with five to six wells in each different horizontal layer, are drilled and completed in the same section. Operators produce those wells simultaneously with the objective of mitigating the parent-child effect of unconventional reservoirs.

Nevertheless, with all wells producing at the same time and competing for production from the first day, will this benefit ultimate recovery? This question was investigated through comprehensive fracture and reservoir modeling and simulation. A reservoir dataset for the Spraberry Formation in the Permian Basin was used to build a hydraulic fracture and reservoir simulation model.

Different field development strategies were studied. Models representing a traditional parent-child scenario with five parent wells completed and produced one year before four infill child wells and a traditional parent-child scenario with five parent wells completed and produced five years before four infill child wells are compared. In these cases, a geomechanical finite-element model (FEM) was used to quantify the changes to the magnitude and azimuth of the in situ stresses from the various reservoir depletion scenarios. Next, a cube model with nine horizontal wells completed and produced simultaneously was analyzed. These three scenarios were expanded to include 19 horizontal wells with the same methodology.

This study aims to help operators in the Permian Basin, as well as in other unconventional reservoirs to understand how different field development strategies affect ultimate hydrocarbon recovery and net present value.
Use of Big Data and Machine Learning to Optimise Operational Performance and Drill Bit Design
Authors: Cornel Simon , Vazquez Gonzalo
Published: November 2020

Objectives / Scope

Time savings and bit longevity is one of the major challenges in Coal Seam Gas unconventional fields Onshore Queensland. Maximize rate of penetration based on best drilling parameter was the key target to tackle these issues. The dataset was accumulated from more than 80 bits run. A supervised machine-learning algorithm was used to classify drilling parameters that increase ROP and bit endurance. To achieve this goal, our focus was on a) optimizing PDC bit design, drilling hydraulics and b) developing a Drillers Roadmap to ensure optimal parameters were run in the field.

Methods, Procedures, Process

Operator and Service Company worked closely to select two PDC frames and benchmark performance between them. A novel approach was then taken to capture the rig sensors data. These steps were as follows: Capture big data using sensors integrated on the rig to produce drilling parameters data sets. An algorithm to clean large data sets with the use of Python Pandas libraries was created. Using supervised machine learning and clustering the data to identify which drilling parameters gave the best results. Process the data set using visualization tools to develop heat maps vs depth for each of the drilling parameters (WOB, RPM, flow rates and Torque). Create a Drillers Roadmap identify drilling parameter trends by depth and provide a "sweet spot" based on machine learning for fine tuning operational window.

Results, Observations, Conclusions

A formal process for optimizing performance was developed. After 6 wells, optimal operating ranges were identified. This process leads the driller to respond to changes in formation and deliver more consistent drilling performance with excellent results. ROP increased from 50m/hr to 150m/hr, driven by improved ROP in the lower section of the well. Time saving of more than 150hrs for the drilling campiagn. Refine PDC bit designs based on optimized parameters, therefore improving bit life through cutter and hydraulic enhancements.

Novel / Additive information

The innovation of this methodology is to gather drilling information, cleaning the data with Python using Pandas library thus making this process more efficient. After the data files were clean and QAQC, a supervised machine Learning algorithm was used to optimize ROP's by taking drilling datasets and fine-tuning the parameters (WOB, RPM, Flow rate) using drilling dynamics (vibration) and MSE models. Finally, innovative visualizations (heat maps) were produced in order to give a clear insight of the drilling information gathered by the rig sensors to the company men and drillers.
A Digital Core Laboratory Characterising Oil Water Transition Zones
Authors: Morrison Graeme
Published: November 2020

A multi-chamber, finite-difference dynamic model assumed pore-scale reservoir properties and replicated laboratory processes to determine oil-water transition zone characteristics: (1) imbibition residual oil saturation to water flood (distributed as a function of rock properties); (2) imbibition oil-water relative permeability (according to both steady-state and unsteady-state calculations); and, (3) microscopic oil recovery factor as a function of height above free water level. A priori, scaled capillary pressure hysteresis and segregated flow conditions governed pore-scale flow behaviour (initial mobile saturation scaled 0-1) assuming a pipeline network analogue. Although an oil-water system was modelled the process could be extended to other fluid phases.

The dynamic model was divided into five chambers replicating two experiments. Flow between chambers was regulated by transmissibility multipliers (open/shut). Replicating a core displacement experiment, an oil source rock "drained" (flooded) the central core chamber to form the oil-water transition zone; water then imbibed into the core chamber displacing mobile oil into a shallower effluent chamber; capillary pressure hysteresis defined the imbibition residual oil saturation to water flood. In a second experiment, the central core chamber was subdivided into zones, each comprising five model layers, and the imbibition oil-water relative permeability was determined by flooding the quiesced core chamber situated between high/low-pressure chambers (source/sink). Both steady-state and unsteady-state relative permeability were determined across the oil-water transition zone.

The Digital Core Laboratory (DCL) determined the distribution of imbibition residual oil saturation as a function of: wettability assumption; rock properties; and, proximity to the free water level. For a range of wettability assumptions, imbibition oil-water relative permeability was determined across the transition zone, on a zonal basis, from top core chamber (top reservoir) down to the free water level.

Summary of Results: (1) The imbibition residual oil saturation (ISorw) decreased with depth toward the free water level, while the drainage irreducible water saturation (Swir) increased with depth; (2) ISorw was found to be strongly influenced by the wettability assumption, contrasting with the uniformity of Swir; (3) Microscopic oil recovery factor was determined across the transition zone as a function of both proximity to the free water level and the wettability assumption; except for the oil-wet case, microscopic oil recovery factor increased with depth toward the free water level.

The novel approach was the multi-chambered design of the finite difference model forming a Digital Core Laboratory (DCL). Tuning the model to a Special Core Analysis (SCAL) data set, with the objective of filling SCAL data gaps, is expected to be one application.
Edge Computing: A Powerful and Agile Platform for Digital Transformation in Oilfield Management
Authors: Sharma Abhishek , Samuel Prince , Gupta Debashis , Whatley Craig , Agarwal Shubham , Gey Gian-Marcio
Published: November 2020

In Production operations, asset performance depends greatly on maximizing the run life of equipment while reducing the cost of maintenance. Often, E&P operators have a reactive approach to field maintenance resulting in uneccessary downtime in logistics, inventory management, diagnosing the issues, and in taking the recommended actions. This can lead to higher operating costs and non-productive time.

E&P operators are aggressively looking to increase production with operational efficiency gains. In the unconventional fields, a large number of wells have been drilled and put in production with various artificial lift techniques. Proactive well and field production management requires digital enablement of operations, with no data silos and data flowing seamlessly from the subsurface to the hands of the operator. With huge amounts of data being collected, it is imperative to apply data-driven techniques to gain more insights that can be utilized to better manage production. A data-driven approach can provide huge benefits for organisations holding vast amount of reservoir, production, and facilities data. It could provide insights into non-linear multidimensional relationships between parameters so that the field development is better understood and optimized. It could allow companies using a proactive approach towards field operations and equipment maintenance resulting in additional cost savings.

This paper presents case studies in which operators optimized production utilizing edge-driven Industrial Internet of Things (IIoT) solutions. These edge IIoT solutions enable fast-loop control through a combination of physics and data-driven workflows, which empowers the operator to proactively manage their assets and focus attention on potentially problematic wells. The solution’s architectural setup and ability to deliver fast-loop control workflows at the edge enables operators to successfully detect and manage potential issues and ultimately improve well performance. Additionally, this approach reduces the dependency upon domain experts to frequently analyze data. The high-frequency data capturing resulted in predicting equipment performance with confidence and allowing remote well management to reduce health, safety, and environment (HSE) risks while decreasing logistics and maintenance costs.
First Application of New Generation NMR T1-T2 Logging and Interpretation in Unconventional Reservoirs in China
Authors: Zhang Hao , Wang Yue , Fang Tao , Wang Wei , Zhao Xian Ran , Zhao Hai Peng , Wu Jin Long , Wei Guo , Liu Bo
Published: November 2020

The strong domestic need for oil in China requires further exploration in unconventional reservoirs, such as volcanic and shale oil reservoirs. Sweet zone identification is one of the most critical missions in formation evaluation. The complex mineralogy and the low porosity in unconventional reservoirs result in little contrast of resistivities between oil-producing zones and water zones. Reducing uncertainties affecting the type of fluid in the reservoir and its potential movability is key to defining the optimal landing zones for future horizontal drains.

In conventional reservoirs, nuclear magnetic resonance (NMR) logging based on cutoff analysis is the optimum choice for evaluating the porosity and pore geometry of hydrocarbon bearing reservoirs. However, the routine single-dimension T2 measurement is not sufficient for fluid typing in unconventional reservoirs because of the overlap of signals of various fluids in the T2 domain. This contribution presents case studies from a new generation NMR tool providing continuous 2D T1-T2 measurements. The T1-T2 measurements enable separation and quantification of different fluids in the pores. Fluid typing can be done by integration of other wireline logs such as spectroscopy and dielectric.

Case studies are presented from volcanic and shale oil reservoirs in Xinjiang Oilfield of PetroChina. In the volcanic reservoir case, dedicated 2D data analytics technic is used to extract relevant signals from 2D NMR T1-T2 measurements. An integrated workflow combining wireline borehole electrical image, nuclear spectroscopy, dielectric dispersion and NMR T1-T2 gives an insight into the fluid composition in the pore system. Besides accurate measurements of lithology independent porosity and pore geometry from NMR, T1-T2 specific measurement of clay bound water volume and total water volume match well with the same measurements estimated from nuclear spectroscopy and dielectric dispersion logs. It lowers the uncertainties on fluid types in the volcanic reservoir where resistivities fail to differentiate wet zones and oil zones. In the shale oil reservoir case, a reservoir producibility index (RPI) derived from the integration of NMR T1-T2, wireline borehole electrical image, and nuclear spectroscopy is shown to be quite efficient in sweet zone identification and ranking.

This paper discusses a novel application of a new generation NMR T1-T2 logging method in unconventional reservoirs, which helps the operator ascertain the potential of these reservoirs. The oil zones identified by the new method have promising oil productions. The workflow illustrated in the two case studies can be applied and extended to other unconventional plays in China.
Digital Documentation and Data Management for Offshore Drilling
Authors: Cheng Zhong , Xu Rongqiang , Yu Xiaolong , Hao Zhouzheng , Ding Xiangxiang , Li Man , Li Mingming , Li Tiantai , Gao Jiaxuan
Published: November 2020

Upstream Oil & Gas industry recognizes that there are significant gains to be had by the implementation of new digital technologies. For offshore exploration and development, the goal is to bring together all domains, all data, and all engineering requirements in a seamlessly interconnected solution. The industry is putting significant efforts into using instrumentation and software to optimize operations in all domains for exploration and production (E&P) to move towards the digital oil field of the future. an innovative digital solution has been designed and implemented to cover all different aspects of the well planning and engineering workflows, delivering a step change in terms of capabilities and efficiency.

As part of this transformation process, CNOOC have implemented integrated data management project of geological engineering for covering all different aspects of the well engineering workflows, delivering a step change in terms of capabilities and efficiency. The objective is to provide a continuous improvement platform to users for:

Digitalization can reduce the time spent with daily documentation and simultaneously increase the quality by removing an error prone way of work.

Technological solution enabling real-time data transmission from all rigs to CNOOC onshore headquarters and enabling real-time visualizations of the drilling data. This includes workload, number of needed rigs, daily performance, key performance indicators and even operation time forecasts based on real data.

Engineering solution to transform expert experience and accident cases into information to easily identify the areas of operational improvement allowing to implement specific measures to reduce intangible loss time (ILT) and non-productive time (NPT) which can help in reducing costs.

This project has also provided a real geological drilling environment where high frequency real-time drilling data is utilized along with low frequency daily drilling report data to provide better insights for well planning and generate ideas for improving performance and reducing risk.

This paper presents a full description of a new industry standard digital well construction solution that has the potential to transform the well operation process by providing a step change in collaboration, concurrent engineering, automation, and data analytics. Furthermore, the cloud-deployed solution challenges will be briefly discussed.

The learned lessons and gained experiences from this project construction presented here provide valuable guidance for future demands E&P and digital transformation.
Practical Prediction of Asphaltene Mitigation Based on Dynamic Asphaltene Inhibitor Test DAIT
Authors: Takabayashi Katsumo , Iwama Hiroki , Yonebayashi Hideharu
Published: November 2020

Asphaltenes in the production process often cause significant damage to oil production operation through the continuous growth process (precipitation followed by aggregation and finally deposition) of their particles. To mitigate the asphaltene-induced problems, Asphaltene Inhibitor is used to control precipitation and/or deposition of asphaltene particles. In addition to the typical ambient-conditioned Asphaltene Inhibitor-evaluation known as Asphaltene Dispersancy Test, this study applied the Dynamic Asphaltene Inhibitor Test to select candidate Asphaltene Inhibitors at operating conditions. The Dynamic Asphaltene Inhibitor Test used a simple dynamic flow equipment consisting of capillary coil and filter to detect asphaltene precipitation and deposition independently.

Based on Asphaltene Inhibitors experimentally screened through a series of Dynamic Asphaltene Inhibitor Test, four Asphaltene Inhibitor numerical models were reproduced to assess how Asphaltene Inhibitors mitigating asphaltene risks. A numerical model assuming no Asphaltene Inhibitor was built as reference by calibrating with conventional Pressure Volume Temperature (PVT) experimental results and asphaltene onset pressure. Asphaltene Inhibitor models were generated from the reference model on the basis of comprehensively interpreted data containing available Asphaltene Inhibitor characteristic information. In the Asphaltene Inhibitor models, pseudo heavy components of the crude oil used in Dynamic Asphaltene Inhibitor Test were characterized with cubic-plus-association equation of state. The Asphaltene Inhibitor models were validated by comparing with the relative Asphaltene Inhibitor's effectiveness in experimental evaluation. To understand transition from Asphaltene Precipitation Envelopes to Asphaltene Deposition Envelope, the findings of deposition-information from the Dynamic Asphaltene Inhibitor Test were incorporated into the Asphaltene Inhibitor models. To be more specific, the deposition behavior was estimated on thermodynamic plot by assuming the Dynamic Asphaltene Inhibitor Test condition as pseudo onset condition. The Asphaltene Precipitation Envelopes on thermodynamic plots revealed causing asphaltene precipitation possibly in part of the operation conditions even using Asphaltene Inhibitor. On the other hand, Asphaltene Deposition Envelope suggested that Asphaltene Inhibitor application could reduce asphaltene deposition over a wide range of the operation condition. The upper-boundary locations of Asphaltene Precipitation Envelopes and Asphaltene Deposition Envelope were apart from each other. The fact revealed that Asphaltene Inhibitor could delay asphaltene aggregation and deposition after precipitation. This paper describes a more realistic Asphaltene Inhibitor modeling method based on the Dynamic Asphaltene Inhibitor Test measurement results.
Analysing Critical Elements in Openhole Gravel Pack Treatment Design Cycle: Case Study in Indian Ultra-Deepwater
Authors: Gupta Vaibhav , Jeanpert Julie , John Colin , Bose Ramen , Agrawal Vivek , Patowary Markandeya , Banka Raushan
Published: November 2020

Openhole gravel pack is the preferred completion method of sand control in unconsolidated deepwater formations with sanding tendencies. The design phase of an openhole gravel pack treatment spans drilling of the reservoir section, wellbore preparation, sand screen installation, gravel placement, and well flowback. This paper discusses several ultra-deepwater wells of India, where openhole gravel packs were performed by integrating drilling, wellbore cleanout, wellbore displacement and gravel pack methodologies to achieve a holistic approach towards openhole gravel pack completions.

The reservoir sections were drilled with streaks of shale and dolomite sections, the liner shoe often landing in shale, exposing a shaly rat hole. Next, the wells were displaced to dedicated production screen testing (PST) mud prior to running in screens. Screens were then placed across the openhole with memory gauges to capture downhole events during the gravel pack. The displacement pills train was engineered to displace the open hole with water-based fluid prior to starting the gravel-pack treatment. Extensive lab testing and displacement simulations for efficient spotting of pills and optimal sweeping effect were performed. The gravel-pack treatment was executed using Alternate Path technology and a high-temperature viscoelastic surfactant (VES) carrier fluid system.

11 ultra-deepwater wells off the east coast of India were completed. Displacement of open hole from drilling fluid to water-based system was done with direct displacement and reverse displacement methods. Due to the narrow fracturing window of 200-300 psi in all the wells, bottomhole pressures were managed by deploying multiple techniques such as using a lighter VES-based brine, optimizing the rheology of the fluid and taking returns through the riser. Roping behavior of the pre-pad and slurry have also been studied, and results presented to correlate with design. All open-hole gravel packs were executed without losses and achieved complete packs. Finally, flowback and well test data were analyzed by the operator and found to be as expected with no sand ingression. A holistic approach toward open-hole gravel-pack treatments has been developed, analyzing all the interlinked elements and the efficacy of the design process verified against surface and downhole data gathered for each well.

This paper discusses the approach, simulations, lab testing, and evaluation, analyzing captured data from drilling phase of the open hole to completing the well by placing gravel across the screens and flowing back. The design approach, integration strategies, and lessons learned in these wells offshore India can be applied to improve success rates of sand control completion wells around the world.
Numerical Simulation Workflow using Carbonate Digital Rock Built from Integrated Lattice Boltzmann and Kriging Method
Authors: Han Yifu , Liu Kailei
Published: November 2020

Formation evaluation including porosity and directional permeability assessment in complex carbonate formations is challenging due to the anisotropic and heterogeneous nature of the formation. The pore structure can be captured by digital rock technology, which makes the constriction of numerical whole core, and the numerical simulations of chemical enhanced oil recovery on digital rock possible.

We introduce a new workflow to construct the digital rock based on micron-CT scanned sequential images of the carbonate sample. Firstly, we apply the modified Otsu's global thresholding method to convert the grey-scale micron-CT scanned images to binary images, and we systematically divide the whole core 3D segmented image into small blocks or representative elementary volume (totally 10143 blocks including inactive blocks). Then we estimate the porosity of each block, and use Lattice Boltzmann Method (LBM) to simulate fluid flow in randomly selected blocks and accurately compute the directional permeability of these blocks by simulating Darcy flow through them. The permeability tensor of other blocks is estimated by applying Kriging method based on the estimated porosity of all the blocks and the accurately calculated permeability tensor of the selected blocks. Then we simulate the polymer flooding on the constructed digital rock and show that the simulation process is computational efficient.

We calculate the porosity and permeability of the constructed digital whole core and compare them with laboratory measurements. The image analyzed porosity of whole digital rock is comparable with laboratory measurements by only 1% difference. The simulated permeability value with calibration of the whole digital rock is also in good agreement with experimental result by 3.5% difference. Then we simulate the polymer flooding on the constructed digital rock and show that the simulation process is computational efficient. The proposed digital whole core construction workflow integrates the accuracy of LBM simulation and the efficiency of kriging method. The accurately constructed digital whole core only have 10143 blocks including inactive blocks, which greatly reduces the heavy computation time of chemical enhanced oil recovery simulation on digital rock and makes the proposed method more practical in routine analysis and modeling.