bluuebunny/crossref_metadata_2025 huggingface dataset:
Development And Application Of Emulsion-based Conformance Control Method For Enhanced Bitumen Recovery By Steam-assisted Gravity Drainage
Authors: Ding Boxin , Shi Longyang , Dong Mingzhe
Published: October 2020

Bottom water, which is defined as a zone below the base of bitumen pay with high water saturation, has been encountered in many currently operated steam-assisted gravity drainage (SAGD) projects. It has been widely accepted that the bottom water lying under bitumen in SAGD act as a heat sink which is a result of the much higher mobility of water compared to that of the bitumen. This has a detrimental effect on the project economy because of the resulted greater steam-oil ratio (SOR) and less oil recovery. Therefore, it is essential to reduce the mobility of bottom water in SAGD operations to avoid the water coning and steam loss to the bottom water. In this study, emulsion-based conformance control treatment is introduced and investigated in oil sands reservoirs to reduce the mobility of bottom water to avoid the water coning and steam loss to the bottom water during the SAGD operations. To accurately simulate the emulsion plugging performance during the SAGD process, it is essential to optimize the emulsion flow model in water saturated porous media to better describe the flow mechanism. There are three main mathematical models applied to describe the dynamics of emulsion flow in porous media: the bulk viscosity model, the retardation model and the filtration model.

In this paper, the newly proposed emulsion flow model was optimized on the basis of the conventional filtration model with considering the process of emulsion droplets trapped and released by the pore throats. Three different types of emulsion flow tests were collected and analysed from previous publications, including permeability reduction of an emulsion in a specific sandpack, an emulsion slug injection followed by water injection in a specific sandpack and conformance control performance in parallel-sandpack. The optimized emulsion flow model can successfully simulate all of the three different types of emulsion flow process and have a good agreement with the experimentally obtained results. A new method for predicting permeability reduction by emulsion plugging was firstly determined by fully incorporating with the emulsion and sandpack properties. By successfully simulating the emulsion plugging process in a specific sandpack followed by water injection, a desired conformance control performance can be predicted by injection of a prepared emulsion in a specific parallel-sandpack. This may give an instruction on optimal emulsion design in the field application. The optimized emulsion flow model shows a wide application in different types of experiments and flexible control on the parameters, showing a huge potential for the reservoir-scale simulation.
Transient Simulation Study of Well Trajectory Effect on Sand Transport in Multiphase Flow
Authors: Mustafa Ahmed
Published: October 2020

Sand transport in horizontal wells with multiphase flow is a complex phenomenon due to the horizontal well trajectory's having strong impact on multiphase flow and sand transport behaviors. Most of the previously published studies on sand transport centered around pipeline flow applications and less so on sand transport in wells, and particularly, on understanding the effects of well trajectory and the associated sand transport capacity for different well trajectories. Therefore, this study presents qualitative comparison between different well trajectories and their impact on sand transport. This is an important aspect of optimizing the well trajectory where co-sand production strategy is selected.

Five different well trajectories have been selected in this study, which include 90-degree horizontal, toe-up, toe-down, sump and hump trajectories. Multiphase transient simulation has been carried out for each case to evaluate the effect of well trajectories on sand transport in multiphase flow, and the sand deposition profiles including moving bed, stationary bed and suspended particles associated with each horizontal well trajectory.

The study results reveal that 90-degree horizontal, sump or hump well trajectories always have higher capacity to transport sand compared to toe-up and toe-down trajectories. Thus, it helps in delaying the plugging of the horizontal section, sustain well production and reduce sand cleanout requirements. In addition, the toe-up trajectory has higher capacity for sand transport than the toe-down trajectory. Sensitivity analysis has been performed to evaluate the effect of sand particle size, fraction, water cut and GOR on the sand transport capacity for each case of different trajectories. The overall observations showed that as the sand particle size and fraction increases, the sand deposition tendency to form stationary bed increases due to the increase of gravity force. On the other hand, as the water cut, and gas oil ratio increases, the sand deposition tendency to form stationary bed decreases, due to the weak lift force to carry the particles along as the liquid viscosity is low. It is noted however that that the magnitude of effect for each parameter on the sand capacity limits for sand transport is different.

This study presents a novel approach to improve the horizontal well planning where co-sand production strategy is selected. This would help in achieving sustainable production and reducing the number of cleaning operations required during the well life cycle.
Improved Robustness In Long-term Pressure Data Analysis Using Wavelets and Deep Learning
Authors: Orta Alemán Dante , Horne Roland
Published: October 2020

The high frequency and long duration of well production data have high value for inferring reservoir properties, monitoring reservoir conditions and detecting changes in the well flow conditions. Unlike traditional well test data where the well flow-rate and the pressure response are carefully monitored, long-term pressure data is subject to abrupt changes in flow-rate, noise caused by hardware failures, missing data and physical changes in the reservoir. In this work, methodology to interpret long-term flow-rate and pressure data was developed using wavelet multiresolution analysis in combination with deep learning algorithms. The methodology requires little to no data preprocessing, no explicit knowledge of a physical model and has high robustness to noise.

The proposed methodology was found to exploit the ability of wavelets to capture and synthetize the relevant behavior of the reservoir performance history at multiple scales. This was achieved by applying the Maximal Overlap Wavelet Transform (MODWT) to long-term flow-rate data and using the output of the MODWT as the input for a Recurrent Neural Network (RNN) which creates a function map between flow-rate and pressure as a function of time. For the training of the RNN, synthetic well data were used.

The application of wavelet transforms allowed the neural network to perform an automatic noise filtering and simplified the training process while requiring no knowledge of noise levels, little to no manual preprocessing of data and no knowledge of the reservoir's physical model. Moreover, the mathematical formulation of the wavelet transforms allows the neural network to take advantage of the multiresolution properties, opening the door to executing the whole analysis procedure in a simplified way.

The proposed methodology aids in the inference of effects such as changes in skin factor, pressure depletion or changes in the reservoir model from the available production data, even when those data are noisy or incomplete. By using production data, the inference can be done without loss of production and used to support oil production enhancement operations. Moreover, the coupling of a neural network with multiresolution analysis eliminates the need for feature engineering and performs automatic denoising of the data, which were found to be extremely desirable properties for a methodology to be scalable and applicable to real well datasets.
A Pilot-Scale Evaluation of Natural Gas-Based Foam at Elevated Pressure and Temperature Conditions
Authors: Beck Griffin , Bhagwat Swanand , Day Carolyn , Gordon Emilio , Daeffler Chris , Malpani Raj , Verma Sandeep , Chaves Leo , Comeaux Bruce , Chrusch Larry , Naik Sarvesh , Renk Joseph
Published: October 2020

Nitrogen (N2) and Carbon Dioxide (CO2) foams have been used as hydraulic fracturing fluids for several decades to reduce water usage and minimize damage in water-sensitive reservoirs. These foam treatments require gases to be liquefied and transported to site. An alternative approach would be to use natural gas (NG) that is readily available from nearby wells, pipelines, and processing facilities as the internal, gaseous phase to create a NG-based foam. Hydraulic fracturing with NG foam is a relatively inexpensive option, makes use of an abundant and often wasted resource, and may even provide production benefits in certain reservoirs. As part of an ongoing development project sponsored by the Department of Energy (DOE), the surface process to create NG foam is being developed and the properties of NG foam are being explored. This paper presents recent results from a rigorous pilot-scale demonstration of NG foam over a range of operating scenarios relevant to surface and bottomhole conditions with a variety of base-fluid mixtures.

The Pilot-scale Foam Test Facility (PFTF) used in these investigations is first described. The PFTF is capable of generating foamed fluids at pressures up to 7,500 psig and at temperatures in excess of 300°F. Then, results from several investigations aimed at proving NG foam at conditions relevant to the field are presented. NG foam was characterized using rheology measurements and flow visualization techniques. Experiments were performed to investigate the texture and stability of NG foam generated by two different mixing methods: one using a custom designed tee to match mixing velocities in the field where the gas phase is jetted into the aqueous stream, and another to ensure comprehensive mixing for laboratory analysis. Parametric studies were conducted to explore the effects of flow rate, foam quality, and temperature on the stability of NG foam. Moreover, different fluid preparations were used to investigate the effect of base fluid and additive concentrations on the stability of NG foams. Additional laboratory studies that investigated foam stability with produced water and multicomponent NG mixtures are also reported.

The NG foams explored in these investigations exhibited typical, shear-thinning behavior observed in rheological studies of N2- and CO2-based foams. The measured viscosity and observed stability indicate that NG foams are well suited for fracturing applications. Like other foams, NG foam exhibits sensitivity to operating temperature characterized by a decrease in apparent viscosity as temperature increases. Rapid foam breakdown was observed at significantly elevated temperatures exceeding 290°F. In addition to fluid characterization, these investigations also yielded several key lessons that should be applied to future field demonstrations of NG foam.
Development of an Integrated Well Planning Platform in a NOC
Authors: Esteves Aranha Pedro , Abensur Gandelman Roni , Henrique Martins dos Santos Pedro , Borella Hougaz Augusto , Gomes Clemente Ricardo , Dumlao Vincent , Henrique Santos Teixeira Pedro , Mazzi Vitor
Published: October 2020

The act of planning a well is a collaborative effort taking disparate data from various groups and synthesizing it into one overarching program. Each operator follows its own well design process, but common to each operator is the fact that wells not designed wholly in series, but rather have many parallel sections, with a set of decision gates and countless data interdependencies. The goal of this paper is to detail the successful development and implementation of an integrated planning platform within a National Oil Company.

The individual components of well design process are not performed in a vacuum. The various inputs / outputs from one application affect the inputs/ outputs of applications both upstream and downstream of it, along the design process. This is addressed by facilitating integration and analysis of data input / output from each component of the planning process, synthesizing it, and performing automated system-integrity and overall conformity checks between the interdependent components. Any changes to an individual design component that affect other areas must be flagged, notifying the appropriate parties. Defining and automating workflows, mapping data interdependencies within the workflows, and creating a system of data governance are also keys to building an efficient planning platform. Similar to a navigation program which is able to determine an optimal driving route by considering various dynamic and static data points, the implemented platform uses a data-adaptive approach to well planning. Using this method, it takes unstructured data processes, decision trees, data integration protocols, and automates them while also permitting users to collaborate on well planning and design, allowing the standardization of processes. Operators are able to embed their particular methods of well design into the software platform and thus ensure that all of their company meet the specific requirements. Key to the effectiveness and longevity of this type of platform is a neutral data repository. Allowing data to be free of any singular data protocol ensures that although individual applications may revised, replaced, or put into competition with one another, the format of the data produced as well as ingested by these applications stays constant. Data will remain mapped, both in terms of overall process workflow and interdependencies. This paper is beneficial to any operator wishing to gain insight in developing a forward thinking of a digital strategy for well planning and design. However, the use of these types of methods and development of a similarly integrated platform an operator can standardize processes, enforce and ensure data governance, as well as gain efficiency in overall planning time.
Integrated Fracture Characterization and Flow Profiling with Distributed Temperature Sensing
Authors: Sun He , Yu Wei , Sepehrnoori Kamy
Published: October 2020

Distributed temperature sensing (DTS) is an enabling technology for fracture diagnosis and multiphase flow measurement in unconventional areas. DTS data analysis includes the warm-back stage and production stage analysis. The warm-back stage analysis can provide the slurry flow and proppant allocation. The production stage analysis can be applied to flow profiling and fracture characterization. The objective of our DTS data analysis approach is to provide an integrated quantitative diagnosis of effectiveness of staged fracturing, and hydraulic and natural fractures with the full-physics model, which will benefit the fracturing operation design and decision-making process in the unconventional reservoir. In this work, we developed a comprehensive numerical forward model for DTS data analysis. Our model includes reservoir and wellbore models. Also, the flow and thermal models are fully coupled. A thermal embedded discrete fracture model (Thermal EDFM) is developed to handle the thermal modeling of complex fracture networks. The DTS analysis with our model provides a high-resolution solution since the fracture diagnosis and flow profiling are performed for each fracture. With this analysis, we obtain a deeper understanding of the effectiveness of the field hydraulic fracturing operation. Although numerous simulators are developed for DTS data analysis, relatively few existing models can handle the full-physics such as complex fracture geometry and multiphase flow. Our inverse model provides an improved DTS data match result. Our model is more rigorous than the prior models to simulate and match the field DTS data.
Quantifying Resilient E&P Portfolio Strategies
Authors: Allan Paul , Amadi Ugo
Published: October 2020

E&P companies frequently mention portfolio resilience when describing their strategic plans or the composition of their opportunity inventories. As a general concept, the ability to recover from adversity quickly and deliver performance objectives under multiple scenarios is a critical characteristic in any strategic plan. While there is little disagreement in the value of ‘resilience’ as a portfolio characteristic, there is significant variance in the ways this attribute is described or measured as related to E&P portfolios. In many cases, available planning software or valuation tools drive the ways that resilience is considered, resulting in measures that may or may not actually reflect the ability to adapt and still deliver desired performance objectives. This paper provides a structured and repeatable method for quantifying portfolio resilience, with examples provided to clarify the value in applying these techniques.

To demonstrate the methodology, a simple portfolio model reflecting a typical E&P asset portfolio was developed. Resilience as measured using this approach is the ability for a given strategy to deliver set performance expectations when exposed to a broad range of uncertainties or adverse scenarios (above and below ground). These adverse scenarios or ‘torpedoes’ are applied to the portfolio and the cumulative performance difference is computed across multiple objectives and used as a measure of aggregate downside impact. Measuring ‘resilience’ as the ability of a portfolio to recover from selected ‘torpedoes’ in delivering performance is quantified, and portfolio selections are then optimized using linear programming to maximize the resilience measure. The resulting performance of this portfolio is then compared to other optimized portfolios to quantify the trade-offs between performance, resilience, value, and risk. The examples provided clearly depict the value in quantifying a resilience measure and introducing this into the decision-making process. Some of the alternative methods of assessing resilience will also be assessed in this same frame, allowing a direct comparison of these techniques.

While the concepts and techniques outlined in this paper are not entirely new, the combination of aggregate variance computation, linear programming, and rapid assessment of alternative scenarios (torpedoes) provides a practical and replicable measure of E&P portfolio resilience. As objectives and key performance measures are typically fluid, these methods may be adapted quickly for real-time insights into portfolio decisions and value versus risk trade-offs between competing strategic plans.
Improved Stacked Permian Development by Integrating DNA Diagnostics with Traditional Reservoir Analysis
Authors: Schlecht Mathias , Sawadogo Jordan , Sadeghi Simin , Reeve Nico , Haggerty Matthew , Liu Joanne , Ursell Luke
Published: October 2020

Permian operators have dramatically increased the number of multi-stage fractured horizontal wells over the past 5 years and face challenges associated with maximizing production of existing wells while developing new acreage and benches, all the while meeting capital return requirements. Over that time, DNA diagnostics have been applied successfully to more than 1000 wells throughout the Permian Basin to help operators reduce uncertainties ranging from drained rock volume, well-well communication, and sources of water production.

When subsurface conditions change, microbes change, and the DNA from microbes can be used to profile total fluid flow (water + oil phases) from benches and between wells. It therefore serves as a powerful tool to provide a range of answers, using advanced analytics and integration with various data sets. In this study, we will provide the background of DNA diagnostics and related analytics, along with the latest insights into viable operating environments. We also highlight recent Permian basin projects that have used DNA in conjunction with operator data to reduce uncertainty about subsurface conditions.

We will show Total Fluid Logs, which are based on comparing DNA signatures from produced fluids with a DNA stratigraphy log. Total Fluid Logs are utilized to 1) constrain interpreted fracture heights, and 2) work in combination with pressure and production data for Rate Transient Analysis (RTA) for significantly improved estimation of the half-length. The case histories will illustrate the differences between production rates and confirmed fracture height and half-length, and a discussion of microseismic is included.

We show how produced fluid collection during pad completions can elucidate well-well communication and demonstrate the impact of completion size and completion order on effective drainage heights. DNA changes in produced fluids can be compared to production data to reveal the timing and impact of frac hits between wells during zipper completions.

Finally, we provide a suggested workflow for analyzing water contributions out of target in the diagnosis of problem wells. Petrophysical logs can be compared to drainage height assessments to help reveal from which depths water may be producing and can be integrated with production data for a more complete subsurface understanding.

DNA diagnostics represent a complementary, cost effective, minimum environmental footprint and low risk tool for operators to easily integrate into existing production and engineering workflows for monitoring well health and subsurface conditions across time.
A Well Flux Surveillance and Ramp-Up Method for Openhole Stand-Alone Screen Completion
Authors: Karaaslan Mehmet , Wong George K. , Soter Kevin L. , Hicking Stephen H. , Yousif Majeed H.
Published: October 2020

Production and surveillance engineers need practical models to help balance out the reward of maximizing production and the risk of ramping-up the well too much that damages the completion. This paper presents a well flux surveillance method to monitor and ramp-up production for openhole stand-alone screen (OH-SAS) completion that optimizes production by considering risks of production impairment and screen erosion failure.

The flux surveillance model for OH-SAS assumes the filter cake from drilling operations does not cleanup uniformly during fluids unloading and production. This leaves pinholes on the filter cake that cause concentrated flows. The flux model contains three components: (a) capture and describe input properties of pinhole, (b) link completion pressure drop of flows across filter cake region and through pinholes to surveillance results from pressure transient analyses, and (c) distribute fluxes in the SAS wellbore. The model uses three input parameters to describe the pinhole diameter and internal filter cake properties. They are determined as a system utilizing the laboratory return permeability test, computerized tomography scans of test samples, and computational fluid dynamics simulations. The completion pressure drop incorporates radial and hemispherical flows and captures the compounding skin effects of pinholes with internal filter cake. Flux distributions are modeled as a network system. This contains branches and nodes that incorporate radial and vertical flow resistances in the annular region of SAS.

Application of filter cake properties and pressure transient analysis data showed the completion pressure drop as a function of flow rate is non-linear and higher with pinholes than without pinholes. By not incorporating pinholes in surveillance, operations can potentially limit ramp up when the completion pressure drop in the field is higher than predicted. Results from the network model showed the largest radial screen impingement velocity is at the top section of screen. The axial annular flow velocity or scouring velocity is two orders of magnitude larger than the screen impingement velocity. This warrants further considerations of wellbore enlargement and screen scouring erosion mechanisms due to the high axial annular flow.
Completion Sand Control Evolution in the Peng Lai Field, Bohai Bay, Offshore China
Authors: Zhou Leon , Xiu Haimei , Ma Yingwen , Chen Zenghai , Gong Yonggang , Xi Jiangjun , Tan Caiyun , Burton Robert , Gou Susan , Yu Xiang , Lyu Shi , Cao Jun , Du Fuyun , Tang Wenbo
Published: October 2020

This paper presents the evolution of completion sand control designs for the Peng Lai field in Bohai Bay, offshore China. A range of sand control designs from open hole gravel packs to open hole standalone screens to cased hole high rate water packs and cased hole frac-packs have been tried over time. Evaluation of these various designs indicates that single-trip multi-zone cased hole frac-pack designs provide the most effective method for developing the thick, low net-to-gross, unconsolidated sandstone reservoirs in this waterflood field. The paper describes the completion design and evaluation process used in the field and shows how field challenges led to innovative frac-pack downhole equipment designs.

This paper highlights completion practices and results from a range of well types used to develop the large Peng Lai field, offshore China. Development challenges and productivity and reliability results for alternative completion designs are discussed on the basis of field performance. The resulting preferred design, consisting of cased hole frac-packs using large-bore and small-bore single-trip multi-zone equipment are then discussed in further detail with emphasis on development of an innovative, fit-for-purpose, small-bore design. The installation challenges, solutions and successes experienced during the design and installation process and the actual performance and benefits of the new system are also discussed in this paper.

Over the course of Peng Lai field development, a range of alternative completion sand control designs have been tried and evaluated. Cased hole frac-packs provide the most effective completion method for the thick, low net-to-gross, sandstone reservoirs. To enable effective completions in sidetracked wells, a 7" single-trip multi-zone frac pack tool has been developed. This small-bore system has been used to complete 13 wells with 67 zones in the field to date, saving over 43 rig days and $8MM.

This paper presents reliability and productivity comparisons for a range of alternative completion sand control designs in a single, large field environment. A new 7" single-trip, fit-for-purpose design has been developed for the field and successfully implemented in multiple well campaigns. Installation time data and cost savings are provided.
Conformance Control in Fractured Tight Formations using Functional Nanocellulosic Materials Reinforced CO2 Foam Systems
Authors: Wei Bing , Chen Shengen , Tian Qingtao , Lu Jun
Published: October 2020

Foam stability primarily determines the efficiency of foam induced conformance control especially when fractures exist in formations. In this work, a well-defined nanocellulose fibrils (NCF) strengthened CO2 foam (NCF-st-CO2 foam) was proposed aiming to improve the conformance of tight formations with fractures. The bulk characteristics of NCF-st-CO2 foam including foamability, foam stability and texture were thoroughly investigated in a high-pressure and high-pressure (HT-HP) cell. Afterward, foam flow in fracture models was simulated intending to correlate the generated differential pressure with the parameters of foam quality, fracture aperture and fluid velocities, which helped to understand the foam generation, propagation and mobility in fractures. The capacity of this foam in controlling conformance and improving oil recovery in a fractured core was finally evaluated. Conventional CO2 foam (without NCF) was used as the reference throughout this work. The results indicated that the addition of NCF into CO2 foam considerably retarded the liquid drainage and bubble coalescence, which consequently stabilized the CO2 foam especially in the presence of crude oil. NCF-st-CO2 foam presented dense bubbles and a thick framework as observed visually from the cell. The flow behaviors of CO2 foam in fractures were largely determined by the bulk foam stability. NCF-st-CO2 foam could be generated in-situ (aperture from 0.5 mm to 5.0 mm) and then properly propagate throughout the fractures at a low mobility without any plugging issues. In core flooding tests conducted on the model with fracture aperture of 0.5 mm, the NCF-st-CO2 foam injection exhibited a delayed breakthrough, which finally led to an incremental oil recovery of 8.6% OOIP after conventional CO2 foam treatment. These results demonstrated the promising potential of NCF-st-CO2 foam in conformance control in tight formations.
Automatic Carbonate Rock Facies Identification with Deep Learning
Authors: Pattnaik Sonali , Chen Songhua , Helba Adly , Ma Shouxiang
Published: October 2020

Linking depositional properties and post-depositional diagenetic modifications of a rock with its petrophysical attributes remains a greatest challenge for carbonate rock characterization, formation evaluation and petrophysical rock typing. Generally, characterization of carbonate rock facies is labor intensive which requires an experienced geologist to interpret and integrate core, petrographic thin-sections and borehole image logs. In this approach, the carbonate lithofacies are identified with an emphasis on the diagenetic features, such as grain packing, micritization, cementation and dolomitization as well as diagenetic/karstic dissolution, and related connected or partial connected interparticle pores, intraparticle pores, separate and oversized vugs and micrite micro-porosity, etc. Here, we focused on developing deep learning based technique for automatizing manual facies identification process, a powerful tool to provide consistent and faster turnaround interpretations of geological facies for applications such as petrophysical parameter prediction.

In this paper, an architecture for unsupervised multi-class semantic segmentation of carbonate facies that incorporates deep U-Net based architecture is presented. The advantages of using such a network comes from adding skip connections which allows better flow of information in the network. This in return ensures comparable performances along with better feature representation for semantic segmentation tasks. Although many machine learning techniques have been previously applied for facies image analysis automation, the foundation is always the effectiveness of segmentation of multiple overlapping objects in the image. In case of carbonate rocks, diagenesis multiplies the heterogeneity complication. Therefore, in order to deal with this heterogeneity of carbonates we focused on unsupervised approaches because supervised learning methods can become very impractical due to the daunting task of manual feature labeling.

Multiple experiments are conducted on representative images of three types of carbonate facies (grainstone, rudstone, and packstone) to evaluate the performance of our segmentation algorithm and provide quantitative metrics useful for geological and petrophysical applications. Additionally, the segmentation algorithm is also used to detect primary resistive features from resistivity based borehole images. The consistent segmentation results have proved both the effectiveness and validity of the algorithm.
Depletion Effect Quantification and Mitigation of the Offset Wells
Authors: Dashti Ghazal , Liang Baosheng
Published: October 2020

Depletion effects occurs in unconventional reservoirs when the hydraulic fractured wells are completed within the drainage volume of existing producers. Field production and monitoring data show that the existing (parent) wells negatively affect the new (child) wells' productivity, making the new wells produce less than if all the wells are drilled and produced at the same time. This paper presents a systematic study on quantifying the offset well depletion effect in the Permian basin through advanced reservoir modeling and data analytics from field pilots.

The workflow starts with building an Earth model for parent wells in the area of interest, generating a hydraulic fracture network, and performing reservoir simulation for production forecast. The depletion effect, including changes in geomechanical properties on child wells, is properly updated in the Earth model and further captured through fracture geometry distortion and production decrease in the depleted environment. The results from the simulation models are further validated with actual field data. Simulations and pilot studies in both Midland and Delaware basins demonstrate that offset well depletion effect can have significant impact on production performance.

The ability to quantify depletion effect has significant business impacts on development sequence, facility design, completion design, and overall project economics. Analysis on various development scenarios was performed to evaluate the economic impact of depletion effect and determine how to mitigate this effect. Net present value (NPV) economic analysis of different simulation results indicates that time duration of production in the parent well ahead of child well has more impact on depletion effect than the distances between parent and child wells. As examples, depletion quantifications are applied to optimize well production near lease boundary with parent well depletion on the competitor land and guide section development strategy by evaluating pad sequence scenarios.
Experimental Study of Cuttings Transport with Non-Newtonian Fluid in an Inclined Well Using Visualization and ERT Techniques
Authors: Huque Mohammad Mojammel , Imtiaz Syed , Zendehboudi Sohrab , Butt Stephen , Rahman Mohammad Azizur , Maheshwari Priyank
Published: October 2020

Hole cleaning is a concern in directional or horizontal well drilling operation where drill cuttings tend to settle in the lower annulus section. A lab-scale experiments were performed with different non-Newtonian fluids in a 6.16-meter-long, 4.5-inch × 2.5-inch transparent annulus test section to investigate cuttings transport behaviour. This experimental study focused on understanding the cuttings transport mechanism in the annulus section with high-speed imaging technology. The movement of cuttings in the inclined annular section was captured with a high-speed camera at 2000 frames per second. Also, cuttings bed movement patterns at different fluid velocity and inner pipe rotation were captured with a DSLR video camera. The electrical resistance tomography (ERT) system was used to quantify the cuttings volume fraction in the annulus. Different solid bed height and cuttings movement were observed based on fluid rheology, fluid velocity and inner pipe rotation. The mechanistic three layers cuttings transport model was visualized with the experimental procedure. This study showed that solid bed height is significantly reduced with an increase in the inner pipe rotation. This study also identified that cutting bed thickness largely depends on fluid rheology and wellbore inclination. The image from the high-speed camera identified a downward trend of some rolling particles in the annulus due to gravitational force at a low mud velocity. Visual observation from high-speed camera identified a helical motion of solid particles when drill pipe is in contact with solid particles and rotating at higher RPM. Different cuttings movement patterns such as: rolling, sliding, suspension, helical movement, downward movement were identified from the visualization of a high-speed camera.
Oilfield Virtual Twin
Authors: Konchenko Andrey , Chatar Crispin , Doronichev Sergey , Saetern Danny , Bruns Jared
Published: October 2020

Man has always been fascinated by a virtual world outside our reality. From prehistoric drawings to stories told around campfires. In 1838, Charles Wheatstone understood and described binocular vision which demonstrated that the human brain combines two separate images to infer vision. In 1935, seemingly completely separately, Stanley Weinbaum wrote a fictional story in which a person wears a pair of goggles to transport him to a fake world which stimulates the senses. Many of the technology leaders believe that humans will one day create a virtual world indistinguishable from our own reality eventually. Today we already recreate visuals, sounds, touch and even smell. Some companies are working on ways to merge 2-Dimentional(2D) interactions with 3-Dimentional(3D) experiences. Our industry is no different. Many planning and operational experiences have long been done in one way or another but with recent advances in digital technology, there is no reason not to recreate processes and procedures to ensure they are more efficient, bring more value and incorporate richer experiences. As technology advances, reduction in cost are finally making some of what we dreamed about a reality. From the way a geologist reads logs, the way we participate in morning calls while drilling or how we visualize field production and manage facilities in real time, collaborative platforms are evolving with fit for purpose technology.

The concept of a "virtual twin" presumes real-time software—a 2D, 3D, or virtual reality environment where the physical location is being dynamically copied to a necessary degree of details to create a synchronized wellsite representation. Virtual twin is a new concept for the industry. The authors demonstrate the power of visualization, cloud, and virtual reality technology to achieve operational efficiency. Examples demonstrated in the paper are unique and belong to rapidly growing and hugely hyped digital workflows to enable the rethinking of operations and change the status quo for the way of working in the industry. The authors describe three tiers of the virtual twin concept: tier one—planning virtual twin, tier two — dynamic virtual twin, and tier three—prognostic virtual twin. Several examples of concept implementation are demonstrated within oil and gas domains of field development, unconventional completions, drilling, and production gas processing.
Automated Completion System Optimizes Completion Efficiency in Delaware Basin
Authors: Blatt Sam , Douget Phillip , Mast Michael , Wilhoit James , Wiesner Brian , Marvel Tim
Published: October 2020

A new, fully automated completion system contributed to improved safety and efficiency of plug and perforate completions on 34 horizontal wells completed in west Texas Delaware Basin between 2018 and 2020. By enabling nearly continuous frac operations and wireline transitions in three minutes or less, the new system helped reduce the average time-to-complete a well by 38% compared to the average performance on the operator's nearby wells completed using a traditional latch.
Utilizing Artificial Intelligence for Determining Threshold Sand Rates from Acoustic Monitors
Authors: Kolla Srinivas Swaroop , Xu Bohan , Nadeem Ahmed , Luo Qing , Shirazi Siamack A , Sen Sandip
Published: October 2020

Acoustic sand monitors are non-intrusive passive devices and they can be used to provide a warning when sand is being produced in pipelines and/or determine sand rates when calibrated. These devices are attractive to be installed on individual wells or manifolds to detect sand production in offshore production units. One drawback of such devices is the noise created by flow impacting the pipe walls. Thus, if the impact speed or the rate of sand is low, the monitor will not be able to recognize/differentiate the noise that is generated by solid particles impacting pipe walls. The minimum sand rate that can be recognized by the monitor is called a threshold sand rate (TSR). A significant amount of data has been compiled over the years for TSR for various flow conditions, pipe sizes, and sand sizes with stainless steel pipe material. However, it has become challenging to use this data to develop a theoretical model for different flow conditions, pipe, and sand sizes. The goal of this paper is to present an artificial intelligence (AI) approach to determine TSR.

The acoustic sand monitor data utilized in this work has been generated mostly in the lab for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters 2, 3, and 4 inches in vertical and horizontal directions downstream of stainless-steel elbows. The TSR data, pipe sizes, gas, and liquid rates, and calculated erosion obtained by a mechanistic model (characterizing other parameters that are not directly included) have been used to train and create an AI model that can predict TSR for different flow conditions. The parameters of the AI models, including elastic net, random forest (RF), and support vector machine (SVM), are optimized using nested cross-validation and the model performance is evaluated by the R-squared score.

The AI model was used to predict TSR for several new test conditions collected on a 4-in flow loop. Random samples of past data were also compared to predicted values for further validation.

Additionally, this relationship revealed by the AI model was used to predict field data for TSR data available in the literature. The model agreement with published data is encouraging and suggests that this model can be extended for a variety of flow conditions and pipe sizes not tested before.

This work provides significant TSR data, a framework describing a novel methodology to utilize Artificial Intelligence to correlate the TSR with pipe size, erosion model indicating the severity of impact speed of particles, sand size, and superficial liquid and gas velocities.
Computing Pressure Front Propagation Using the Diffusive Time of Flight in Structured and Unstructured Grid Systems Via the Fast Marching Method
Authors: Hongquan Chen , Tsubasa Onishi , Jaeyoung Park , Akhil Datta-Gupta
Published: October 2020

Diffusive-Time-of-Flight (DTOF), representing the travel time of pressure front propagation, has found many applications in unconventional reservoir performance analysis. The computation of DTOF typically involves upwind finite difference of the Eikonal equation and solution using the fast marching method (FMM). However, application of the finite difference based FMM to irregular grid systems remains a challenge. In this paper, we present a novel and robust method for solving the Eikonal equation using finite volume discretization and the FMM.

The finite volume form of the Eikonal equation is derived by differential manipulation, volume integration and the divergence theorem. Using product rule, the differential term is first converted to the divergence form. Then volume integrals that contain divergence terms are converted to surface integrals using the divergence theorem. Consequently, the spatial coordinates are replaced by cell volumes and transmissibilities which are universal for both structured and unstructured grids in finite volume simulators. When applied with the upstream scheme, the finite volume form evolves into a set of quadratic equations, and fast marching method is implemented to solve these equations.

The implementation is first validated with analytical solutions using isotropic and anisotropic models with homogeneous reservoir properties. Consistent DTOF distributions are obtained between the proposed approach and the analytical solutions. Next, the implementation is applied to unconventional reservoirs with hydraulic and natural fractures. Our approach relies on cell volumes and connections (transmissibilities) rather than the grid geometry, and thus can be easily applied to complex grid systems. For illustrative purposes, we present applications of the proposed method to embedded discrete fracture models (EDFM), dual-porosity dual-permeability models, and unstructured PEBI grids with heterogeneous reservoir properties. Visualization of the DTOF provides flow diagnostics such as evolution of the drainage volume of the wells and well interactions.

The novelty of the proposed approach is its broad applicability to arbitrary grid systems and ease of implementation in commercial reservoir simulators. This makes the approach well-suited for field applications with complex grid geometry and complex well architecture.
3D Visualization of Oil Displacement in Porous Media by the Injection of Microcapsule Suspension using Confocal Microscopy
Authors: Chalhub O. S. Ribeiro Raphael
Published: October 2020

In the past decades, oil companies have shown growing interest in increasing oil recovery efficiency. Commonly, after primary and secondary recovery phases, a large amount of oil remains trapped inside the reservoir. Thus, the number of studies focused on enhanced oil recovery is growing, aiming to increase the recovery factor.

The focus of this work is to study the fundamentals of oil displacement in porous media using a confocal laser scanning microscope, which enables 3D visualization of dynamic phenomena with a good spacial and time resolution. The analysis was on oil displacement that results from the use of a suspension of gellan gum microcapsules in water injected after water injection. These microcapsules moving along with the water, blocked some of the preferential paths and forced the water to displace trapped oil ganglia. The result achieved was a collection of 3D images from artificial porous media, in which it was possible to distinguish the distribution of phases (microcapsules, oil, and aqueous phases) inside the porous media, before and after the microcapsules injection.

These images showed that indeed the gellan gum microcapsules blocked preferential water paths and that, after the blockage, some oil ganglia were displaced from their original positions. This work applied modern techniques of microscopy to investigate the concept behind enhanced oil recovery using microcapsules.
Seismic-Wave Traveltime Computation by Supervised and Unsupervised Training of Artificial Neural Networks
Authors: Grubas Serafim
Published: October 2020

Computation of seismic-wave traveltimes is used in seismic imaging procedures such as Kirchhoff migration. For realistic applications, one has to precompute large traveltime tables (for all sources, receivers, and imaging points). This implies massive computations as well as storage of large files with these traveltime tables. One of the popular traveltime computation methods is a numerical solution of the eikonal equation. In this paper, I addressed the idea of using artificial neural networks for optimizing traveltime computations and using traveltimes in Kirchhoff migration. First, I used supervised learning for approximating and compressing the traveltime tables by artificial neural networks. Second, I used unsupervised learning for solving the eikonal equation.

I used fully-connected neural networks for solving both problems. For the first problem, I used traveltimes precomputed on a coarse for supervised training of a neural network. Synthetic tests show that this neural-network approximation provides great compression of the traveltime tables (102−105 times) with reasonable accuracy of predicting traveltimes on a fine imaging grid. Overall, the use of artificial neural networks results in a speed-up of the Kirchhoff migration operator in two applications: microseismic event localization (by three times) and reflection-seismic migration (by four times).

The second problem was to use artificial neural networks for solving the eikonal equation. The main result was a special design of a loss function that ensures solution of the eikonal equation and allows for neural-network unsupervised training. In the synthetic test, the neural network was successfully used for solving the eikonal equation (forward problem) with slightly better accuracy compared to the first-order Fast Sweeping Method. I also demonstrated that neural networks could also solve the inverse problem – back propagate traveltimes from the observation surface into the subsurface. Such inversion was illustrated by successfully solving the problem of microseismic event localization.