bluuebunny/crossref_metadata_2025 huggingface dataset:
Scaling Formulae for the Wellbore Hydraulics Similitude with Drill Pipe Rotation and Eccentricity
Authors: Nosar Thad , Khodaparast Pooya , Zhang Wei , Mehrabian Amin
Published: December 2021

Equivalent circulation density of the fluid circulation system in drilling rigs is determined by the frictional pressure losses in the wellbore annulus. Flow loop experiments are commonly used to simulate the annular wellbore hydraulics in the laboratory. However, proper scaling of the experiment design parameters including the drill pipe rotation and eccentricity has been a weak link in the literature. Our study uses the similarity laws and dimensional analysis to obtain a complete set of scaling formulae that would relate the pressure loss gradients of annular flows at the laboratory and wellbore scales while considering the effects of inner pipe rotation and eccentricity.

Dimensional analysis is conducted for commonly encountered types of drilling fluid rheology, namely, Newtonian, power-law, and yield power-law. Appropriate dimensionless groups of the involved variables are developed to characterize fluid flow in an eccentric annulus with a rotating inner pipe. Characteristic shear strain rate at the pipe walls is obtained from the characteristic velocity and length scale of the considered annular flow. The relation between lab-scale and wellbore scale variables are obtained by imposing the geometric, kinematic, and dynamic similarities between the laboratory flow loop and wellbore annular flows.

The outcomes of the considered scaling scheme is expressed in terms of closed-form formulae that would determine the flow rate and inner pipe rotation speed of the laboratory experiments in terms of the wellbore flow rate and drill pipe rotation speed, as well as other parameters of the problem, in such a way that the resulting Fanning friction factors of the laboratory and wellbore-scale annular flows become identical. Findings suggest that the appropriate value for lab flow rate and pipe rotation speed are linearly related to those of the field condition for all fluid types. The length ratio, density ratio, consistency index ratio, and power index determine the proportionality constant. Attaining complete similarity between the similitude and wellbore-scale annular flow may require the fluid rheology of the lab experiments to be different from the drilling fluid. The expressions of lab flow rate and rotational speed for the yield power-law fluid are identical to those of the power-law fluid case, provided that the yield stress of the lab fluid is constrained to a proper value.
Production Performance Analysis of Western Siberia Mature Waterflood with Prime Diagnostic Metrics
Authors: Aslanyan Arthur , Margarit Andrey , Popov Arkadiy , Zhdanov Ivan , Pakhomov Evgeniy , Garnyshev Marat , Gulyaev Danila , Farakhova Rushana
Published: December 2021

The paper shares a practical case of production analysis of mature field in Western Siberia with a large stock of wells (> 1,000) and ongoing waterflood project.

The main production complications of this field are the thief water production, thief water injection and non-uniform vertical sweep profile.

The objective of the study was to analyse the 30-year history of development using conventional production and surveillance data, identify the suspects of thief water production and thief water injection and check the uniformity of the vertical flow profile.

Performing such an analysis on well-by-well basis is a big challenge and requires a systematic approach and substantial automation.

The majority of conventional diagnostic metrics fail to identify the origin of production complications. The choice was made in favour of production analysis workflow based on PRIME metrics, which automatically generates numerous conventional production performance metrics (including the reallocated production maps and cross-sections) and additionally generates advanced metrics based on automated 3D micro-modelling.

This allowed to zoom on the wells with potential complications and understand their production/recovery potential.

The PRIME analysis has also helped to identify the wells and areas which potentially may hold recoverable reserves and may benefit from additional well and cross-well surveillance.
Quadrant Mapping Artificial Lift Concept
Authors: Rohman A. F. , Febriana C. , Sany S. , Hanggoro R. E.
Published: December 2021

This paper outlines a concept for monitoring performance of artificial lift performance such as electrical submersible pump (ESP), hydraulic pumping unit (HPU), sucker rod pump (SRP) and progressive cavity pump (PCP), for a large number of wells. The objective is to generate simplified monitoring performance of artificial lift with a huge number of wells on one page by creating quadrant mapping consisting of two coordinates with x axis representing pump efficiency and y axis showing pump submergence. We made a four-quadrant limit by pump efficiency (50%) and submergence (200 m). Optimum wells will show on range pump efficiency above 50% and submergence below 200 m, and 3 other quadrants are classified as artificial lift problems, well potential and sizing/design problems.

By using the quadrant mapping concept, we can generate performance of artificial lift for 1500++ wells in one page, and this mapping consists of four quadrants (quadrant 1, quadrant 2, quadrant 3 and quadrant 4), quadrant 1 (Submergence above 200 meter and lifting efficiency below 50%) showing wells which have artificial lift problem, quadrant 2 (Submergence is above 200 meters and efficiency is above 50%) showing well which have potential to increased production, quadrant 3 (Submergence is below 200 meters and efficiency is above 50%) showing the optimum wells operation and quadrant 4 (Submergence is below 200 meters and efficiency is below 50%) showing the wells which required to re-sizing/re-design artificial lift.

This quadrant mapping can be shown to Engineers, manager's and shareholder to show overall performance and classification detailed problems to create a troubleshooting, optimization program to increased oil production, run life artificial and result in better production performance. This mapping also helps petroleum engineers to get a better field view and create priorities and program optimization based on the quadrant mapping result and classification.
A Leap into Automation for Advanced Fracture Characterization
Authors: Patro Radhika , Mishra Manas , Chawla Hemlata , Devkar Sambhaji , Sinha Mrinal , Mukherjee Nistha
Published: December 2021

Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately.

While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques.

This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs.

Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs.

Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation.

The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.
Development and Implementation of Two Compression Set Tools in a Wellbore Clean Out String
Authors: Maher Peter , Nelson Carl , Dockweiler David
Published: December 2021

Running two compression set tools in a single wellbore clean out string, typically a bypass tool and negative test packer, has been a significant industry challenge to operate reliably. The need for running these types of tools is generally driven by the need to perform a negative test on a liner top and achieve high flow rates necessary to hydraulically remove debris from the well. Combining these operations into a single run is an increasingly common method to reduce rig time and cost for the operator. Tools to perform this type of operation are generally available from many service providers, however difficulties and challenges arise when trying to manipulate two different tools in the same string that function by the same compression set method. These operations do have a history that is partially successful, however on a long term basis reliability is generally considered poor by most operators, as a failure to manipulate the tools correctly can result in a failed run and a trip out of the hole. This paper discusses the development and successful field deployment of a system of two compression set tools to address this specific challenge while improving reliability over existing solutions.
Geomechanical Properties Estimation Utilizing Artificial Intelligence Prediction Tool
Authors: Alabbad Mohammed , Alqam Mohammad , Aljeshi Hussain
Published: December 2021

Drilling and fracturing are considered to be one of the major costs in the oil and gas industry. Cost may reach tens of millions of dollars and improper design may lead to significant loss of money and time. Reliable fracturing and drilling designs are governed with decent and representative rock mechanical properties. Such properties are measured mainly by analyzing multiple previously cored wells in the same formation. The nature of the conducted tests on the collected plugs are destructive and samples cannot be restored after performing the rock mechanical testing. This may disable further evaluation on the same plugs.

This study aims to build an artificial neural network (ANN) model that is capable of predicting the main rock mechanical properties, such as Poisson's ratio and compressive strength from already available lab and field measurements. The log data will be combined together with preliminary lab rock properties to build a smart model capable of predicting advance rock mechanical properties. Hence, the model will provide initial rock mechanical properties that are estimated almost immediately and without undergoing costly and timely rock mechanical laboratory tests. The study will also give an advantage to performing preliminary estimates of such parameters without the need for destructive mechanical core testing.

The ultimate goal is to draw a full field geomechanical mapping with this tool rather than having localized scattered data. The AI tool will be trained utilizing representative sets of rock mechanical data with multiple feed-forward backpropagation learning techniques. The study will help in localizing future well location and optimizing multi-stage fracturing designs. These produced data are needed for upstream applications such as wellbore stability, sanding tendency, hydraulic fracturing, and horizontal/multi-lateral drilling.
Combined Proppant Placement and Early Production Modeling Achieve Increased Fracture Performance in Ecuador's Oriente Basin
Authors: Chertov Maxim , Salazar Suarez Franck Ivan , Kaznacheev Mikhail , Belyakova Ludmila
Published: December 2021

In the paper, we document one iteration of the continuous improvement of well performance undertaken in the Oriente Basin in Ecuador. In the past, it had been observed that well economics was sometimes degraded by the issues related to proppant flowback from hydraulic fractures. Proppant flowback resulted in extra costs from well cleanouts, pump replacement, and damage to fracture conductivity. After evaluation of proppant flowback cases using the combined modeling workflow that simulates fracture growth, proppant placement, and early production of solids and fluids, it had been proposed to modify fracture designs and well startup strategy. In this paper, we review the first results of implementation of these modifications in the field and evaluate the significance of improvements.
Applying Boundary Element Simulation to Generate Stress-Based Fracture Drivers: A Case Study for the Montney Unconventional Oil/Gas Play in Western Canada Basin
Authors: Cai Zhong , Smith Craig , Cole John , Tan Chee Phuat
Published: December 2021

Natural fracture distribution is critical to the hydrocarbon production from the Early Triassic Montney unconventional oil and gas play. The formation underwent several tectonic events, creating a unique natural fracture system. Identifying tectonic events and their stress field evolution is an import component in fracture system modeling and prediction. The objective of this paper is to identify the evolution of paleo-stress domains, to establish related tectonic models, and to generate the drivers for fracture network modeling which will aid in reservoir understanding and overall play development.

Compared with other geomechanical approaches, the boundary element method (BEM) is better suited for the structural characteristics in the study area. Hence, the corresponding boundary element simulation (BES) was applied for the evolution of the paleo-stress domains. The methodology is a combination of 3D BEM and Monte Carlo simulations. The inputs include seismic interpreted faults and natural fractures from Formation Microimager logs. After applying the methodology, several best fit realizations were calculated, and the admissible paleo-stress domains were characterized by the tectonic models which are consistent with the regional tectonic evolution of the formation.

The study area is about 400 km2 located at northeast British Columbia in the Western Canada Basin. The main structural features are the thrust and back-thrust faults, forming different fault blocks without any significant deformation structures. The Montney formation within the study area underwent several tectonic events: (1) regime of terrane collision, indentation and lateral escape during end of Middle Jurassic to Middle Cretaceous; (2) regime of left-lateral transpression dominated by strike-slip during end of Late Cretaceous and Paleocene; and (3) regime of right-lateral transtension dominated by strike-slip during end of Early and Middle Eocene which is maintained till present day. Three major stress domains were identified in the study area by the application of the BES method, one reverse event and two strike-slip events, representing paleo and present-day stress domains. These stress domains are consistent with the regional tectonic evolution history of the foreland basin. The stress field parameters, such as stress ratio and maximum horizontal stress azimuth, are consistent. The derived tectonic models are shown to be reliable drivers for the subsequent fracture modeling and geomechanics study.
First Oman Implementation of Pressurized Mud-Cap Drilling Improves Drilling Efficiency and Sustainability
Authors: Orta Cesar , Al Faqih Mohanad , Al Gharibi Bader , Al Shabibi Mohammed , El Khouly Ali , Matin Yasir , Hadj-Moussa Ayoub , Saleh Mujahed
Published: December 2021

Drilling with a gas cap over the Natih formation in Oman often results in excessive flat time. Using the current dynamic fill equipment to deal with kick and loss scenarios leads to extensive nonproductive time on the rig. Managed pressure drilling (MPD) is a well-established drilling technology, and diverse variants exist to suit different requirements. All those variants use the rotating control device (RCD) as a common piece of equipment, but their procedures are different. The pressurized mud-cap drilling (PMCD) technique in the Natih formation replaces the need for traditional dynamic filling technology. The PMCD application enhances the drilling and completion processes by reducing flat time when total downhole losses are experienced.

This paper elaborates on PMCD as a proven drilling technique in total loss scenarios when drilling with it for the first time in the Natih formation in Oman. It describes the PMCD process, the associated equipment, and the results of the inaugural application in the Qalah field.
Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray
Authors: Martinelli Mattia , Colombo Ivo , Russo Eliana Rosa
Published: December 2021

The aim of this work is the development of a fast and reliable method for geomechanical parameters evaluation while drilling using surface logging data. Geomechanical parameters are usually evaluated from cores or sonic logs, which are typically expensive and sometimes difficult to obtain. A novel approach is here proposed, where machine learning algorithms are used to calculate the Young's Modulus from drilling parameters and the gamma ray log. The proposed method combines typical mud logging drilling data (ROP, RPM, Torque, Flow measurements, WOB and SPP), XRF data and well log data (Sonic logs, Bulk Density, Gamma Ray) with several machine learning techniques. The models were trained and tested on data coming from three wells drilled in the same basin, in the same geological units but in different reservoirs. Sonic logs and bulk density are used to evaluate the geomechanical parameters (e.g. Young's Modulus) and to train the model. The training phase and the hyperparameter tuning were performed using data coming from a single well. The model was then tested against previously unseen data coming from the other two wells.

The trained model is able to predict the Young's modulus in the test wells with a root mean squared error around 12 GPa. The example here provided demonstrates that a model trained with drilling parameters and gamma ray coming from one well is able to predict the Young Modulus of different wells in the same basin. These outcomes highlight the potentiality of this procedure and point out several implications for the reservoir characterization. Indeed, once the model has been trained, it is possible to predict the Young's Modulus in different wells of the same basin using only surface logging data.
Artificial Intelligence AI Assisted Thermography to Detect Corrosion Under Insulation CUI
Authors: Amer Ayman , Alshehri Ali , Saiari Hamad , Meshaikhis Ali , Alshamrany Abdulaziz
Published: December 2021

Corrosion under insulation (CUI) is a critical challenge that affects the integrity of assets where the oil and gas industry is not immune. Its severity arises due to its hidden nature as it can often times go unnoticed. CUI is stimulated, in principle, by moisture ingress through the insulation layers to the surface of the pipeline. This Artificial Intelligence (AI)-powered detection technology stemmed from an urgent need to detect the presence of these corrosion types.

The new approach is based on a Cyber Physical (CP) system that maximizes the potential of thermographic imaging by using a Machine Learning application of Artificial Intelligence. In this work, we describe how common image processing techniques from infra-red images of assets can be enhanced using a machine learning approach allowing the detection of locations highly vulnerable to corrosion through pinpointing locations of CUI anomalies and areas of concern. The machine learning is examining the progression of thermal images, captured over time, corrosion and factors that cause this degradation are predicted by extracting thermal anomaly features and correlating them with corrosion and irregularities in the structural integrity of assets verified visually during the initial learning phase of the ML algorithm. The ML classifier has shown outstanding results in predicting CUI anomalies with a predictive accuracy in the range of 85 – 90% projected from 185 real field assets. Also, IR imaging by itself is subjective and operator dependent, however with this cyber physical transfer learning approach, such dependency has been eliminated. The results and conclusions of this work on real field assets in operation demonstrate the feasibility of this technique to predict and detect thermal anomalies directly correlated to CUI.

This innovative work has led to the development of a cyber-physical that meets the demands of inspection units across the oil and gas industry, providing a real-time system and online assessment tool to monitor the presence of CUI enhancing the output from thermography technologies, using Artificial Intelligence (AI) and machine learning technology. Additional benefits of this approach include safety enhancement through non-contact online inspection and cost savings by reducing the associated scaffolding and downtime.
Capturing the Dynamics of Foam Coarsening in a HPHT Microfluidic System
Authors: Yu Wei , Zhou Xianmin , Kanj Mazen Yousef
Published: December 2021

The foam coarsening process is significant to foam stability in porous media. This study provides, for the first time, direct visualization of the foam coarsening process in porous media under realistic reservoir conditions. Foam coarsening behavior in porous media has shown a similar linear increase in the average bubble area to that in an open system but differs in two stages. The average bubble area increases with a faster rate in stage 1 and then increases with a slower rate in stage 2 and stage 2 dominates the foam coarsening process. The transition between the two stages occurs as the inner bubbles disappear when the edge bubbles start feeling the effects of the walls. The foam at steady-state shows a bimodal size distribution with bubbles trapped in the pore bodies and pore throats. The effects of pore pressure (600-3200 psi) and temperature (22-100 °C) were studied. Foam coarsening dynamics are sensitive to pore pressure and temperature, where higher pore pressure and lower temperature are more favorable to maintain a stable foam. Finally, the coarsening rates of foams generated with different gas phases were compared. In contrast to N2 foam and gas CO2 foam, supercritical CO2 foam exhibits the slowest coarsening rate because of its ultralow interfacial tension.
SmartWater Synergy with Microsphere Injection for Permeable Carbonates
Authors: Cao Dongqing , Han Ming , Saleh Salah , Ayirala Subhash , Al-Yousef Ali
Published: December 2021

This paper presents a laboratory study on combination of SmartWater with microsphere injection to improve oil production in carbonates, which increases the sweep efficiency and oil displacement efficiency. In this study, the properties of a micro-sized polymeric microsphere were investigated including size distribution, rheology, and zeta potential in SmartWater, compared with conventional high salinity injection water. Coreflooding tests using natural permeable carbonate cores were performed to evaluate flow performance and oil production potential at 95°C and 3,100 psi pore pressure. The flow performance was evaluated by the injection of 1 pore volume microspheres, followed by excessive water injection. Oil displacement tests were also performed by injecting 1 pore volume of microspheres dissolved in SmartWater after conventional waterflooding.

The median particle size of the microsphere in conventional injection water with a salinity of 57,670 ppm was about 0.25 µm. The particle size was increased by 50% to 100% with reduced elastic modulus when the microsphere dispersed in SmartWater with lower salinity. The zeta potential value of microsphere was decreased in SmartWater compared to that in conventional injection water, showing more negatively charge property. Flow performance of microsphere solutions in the carbonate cores was found to be dependent on their particle size, strength, and suspension stability. The results from coreflooding tests showed that the microsphere dispersed in SmartWater would result in higher differential pressure than that observed in conventional injection water. The SmartWater caused the microspheres swell to larger but softer particles with better suspension stability, which enhanced both the migration and blocking efficiency of microsphere injection. The oil displacement tests confirmed that the microsphere in SmartWater displaced more oil than that obtained with conventional injection water. This result was clearly supported by the higher differential pressure from microsphere injection in SmartWater. The oil bank appeared historically in the post water injection stage, which was quite different from the reported findings of typical mobility controlling agents in the existing knowledge. The microspheres were observed in the core flood produced fluids, indicating the improvement of microsphere migration by SmartWater. This work, for the first time, demonstrated that the combination of SmartWater and microsphere injection yields additional oil production. The proposed hybrid technique can provide a cost-effective way to improve waterflooding performance in heterogeneous carbonates.
Reservoir Engineering and Geomechanical Aspects of Well Plugging and Abandonment
Authors: Qi Qianru , Ghaliah Khoja , Ershaghi Iraj
Published: December 2021

With the maturation of many oilfields, further well abandonments will occur in the years to come. There are issues about improper well abandonment that can have far-reaching effects for responsible companies or entities. At this time in the US, where most of the operation is operated by non-government entities, sometimes the sovereign state may end up covering the cost of well abandonment when the operator is not financially capable in managing such costs. That will be a burden to the public taxpayers. In this paper, we review an important aspect of the well abandonment practices and at present, based on a reservoir modeling approach, more clearance on the potential formation of free gas that can be a cause of concern. We also discuss the integrity issues of the sealing process. We point out how the development of cracks caused by many factors, including geomechanical effects or slow deterioration of the cement seal, in the long run, may result in generating escape paths for the evolved hydrocarbon gases.
Water Injection Profiling Using Fiber Optic Sensing by Applying the Novel Pressure Rate Temperature Transient PTRA Analysis
Authors: Al-Hashemi Mohammed , Spivakovskaya Daria , Moes Evert , Panhuis Peter in ‘t , Hemink Gijs , Shako Valery , Kortukov Dmitry
Published: December 2021

Fiber Optic Systems, such as Distributed Temperature Sensing (DTS), have been used for wellbore surveillance for more than two decades. One of the traditional applications of DTS is injectivity profiling, both for hydraulically fractured and non-fractured wells. There is a long history of determining injectivity profiles using temperature profiles, usually by analyzing warm-back data with largely pure heat conduction models or by employing a so-called "hot-slug" approach that requires tracking of a temperature transient that arises at the onset of injection. In many of these attempts there is no analysis performed for the key influencing physical factors that could create significant ambiguity in the interpretation results. Among such factors we will consider in detail is the possible impact of cross-flow during the early warm-back stage, but also the temperature transient signal that is related to the location of the fiber-optic sensing cable behind the casing when the fast transient data are used for interpretation such as the "hot slug" during re-injection.

In this paper it will be shown that despite all such potential complications, the high frequency and quality of the transient data that can be obtained from a continuous DTS measurement allow for a highly reliable and robust evaluation of the injectivity profile. The well-known challenge of the ambiguity of the interpretation, produced by the interpretation methods that are conventionally used, is overcome using the innovative "Pressure Rate Temperature Transient Analysis" method that takes maximum use of the complete DTS transient data set and all other available data at the level of the model-based interpretation. This method is based on conversion of field measurements into injectivity profiles taking into account the uncertainty in different parts of the data set, which includes the specifics of the DTS deployment, the uncertainty in surface flow rates, and possible data gaps in the history of the well.

Several case studies will be discussed where this approach was applied to water injection wells. For the analysis, the re-injection and warmback DTS transient temperature measurements were taken from across the sandface. Furthermore, for comparison, injection profiles were also recorded by conventional PLTs in parallel.

This case study will focus mostly on the advanced interpretation opportunities and the challenges related to crossflow through the wellbore during the warm-back phase, related to reservoir pressure dynamics, and finally related to the impact of the method of DTS deployment. In addition to describing the interpretation methodology, this paper will also show the final comparison of the fiber-optic evaluation with the interpretation obtained from the reference PLTs.
Novel Sand Conglomeration Treatment Prevents Sand Production and Enhances Well Productivity: Offshore Caspian Case Study
Authors: Kalabayev Ruslan , Sukhova Ekaterina , Rovshenov Gadam , Gurbanov Guvanch , Gil Joel , Kontarev Roman
Published: December 2021

Many oil producing wells, globally, experience sand production problems when reservoir rock consists of unconsolidated sand. Several wells in the Dzheitune oil field are experiencing a similar challenge. Production of formation fines and sand has caused accumulation of fill and wellbore equipment failures and has necessitated periodical and costly coiled tubing-assisted wellbore cleanout operations. A novel chemical treatment tested in the oil field to tackle the challenge led to positive results.

A well with a relatively short target perforation interval was selected as a candidate for the trial sand conglomeration treatment to avoid any uncertainties related to zone coverage. Pre-requisite sand agglomeration and chemical-crude oil compatibility laboratory studies were carried out to optimize the main system and preflush fluid formulations. Once the laboratory testing was complete, a step-rate test was performed to determine the maximum injection rate below formation fracturing pressure. The chemical systems were prepared using standard blending equipment. The preflush fluid was injected to prepare the treated zone. The main fluid was then injected into the reservoir in several cycles at matrix rate by a bullheading process. Upon completion of the treatment, the well was shut in for several days for optimal agglomeration (conglomeration) before the well was slowly put on production.

A long-term increase in the productivity index and sand-free flow rate with no damage to the wellbore or the reservoir were observed. The technology demonstrated its efficiency in preventing and controlling sand production; avoiding frequent, time-consuming, costly wellbore cleanout operations; and producing hydrocarbons at reduced drawdown pressure.
Recent Advances in Supercritical CO2 Fracturing: New Theory, New Technology, and Application
Authors: Duan Wenguang , Sun Baojiang , Pan Deng , Xu Jianchun , Liu Jian
Published: December 2021

The shale oil reservoir in Jimusaer has the characteristics of low porosity and low permeability, resulting in significant resistance in oil flow compared with conventional oil reservoirs. Fracturing is needed to increase shale oil production. Supercritical CO2 (SC-CO2) is an ideal choice for fracturing fluid due to its unique physical and chemical properties. SC-CO2 fracturing is able to make CO2 flow into microfractures and greatly reduce the pumping pressure. New progress has been made in the application of the supercritical CO2 fracturing technology in Jimusaer.

A phase control model of SC-CO2 fracturing as a function of temperature and pressure is established, which takes into account the SC-CO2 features, intrinsic energy, flow behavior in fracture and fluid filtration. In this paper, the influences of injection pressure and temperature, injection rate, temperature-pressure field, temperature gradient, and phase behavior are analyzed extensively, in addition, the phase control model and its chart of fracture are presented.

The proppant accumulation height reduces by a small amount with the increase of the fracturing fluid injection rate. It is necessary to improve the proppant pumping technology by the sand embankment section and proppant concentration. The liquid transforms into supercritical fluid, when flowing in wellbores and fractures. Different fractures have different phase points, and a lower injection temperature is affected by higher injection rate, lower temperature gradient and closer position from transformation point to the end of fracture. Therefore, in order to achieve a better fracturing effect, the injection temperature, pressure, and rate need to be optimized by surface equipment according to the reservoir conditions, to control the phase behavior of CO2.

We built a phase control model for the SC-CO2 fracturing technology, which considers temperature control. We also developed some new techniques to improve SC-CO2 fracturing which is critically needed in the Jimusaer oilfield.
A Real-Time Friction Prediction Model for in Service Drill String Based on Machine Learning Methods Coupling with Mechanical Mechanism Analysis
Authors: Guo Huijuan , Luo Huaidong , Zhan Guodong , Wang Baodong , Zhu Shuo
Published: December 2021

With highly deviated wells and horizontal wells are widely used in the oil industry. The large slope well sections and long horizontal well sections will lead to a sharp increase of the drill string torque and friction, which may reduce the drilling efficiency, and even lead to accidents. Therefore, real-time and accurate analysis of drill string’s torque and friction is an urgent problem facing by the modern drilling technology. The paper established a real-time friction prediction model that combines machine learning methods with drill string mechanical mechanism analysis model. Based on 84000 sets of field monitoring data obtained on-site, a regular data training set for weight on bit (WOB) and torque prediction was constructed with 23 types of time-series related parameters and 10 types of timing independent parameters. Relationships between time-series related parameters and timing independent parameters with the weight on bit and torque were trained to utilize long and short-term memory (LSTM) neural network and muti-layer back propagation (BP) network respectively. The new developed LSTM-BP neural network achieves high-precision prediction results of WOB and torque with a relative error of less than 14%. Based on derived WOB and torque prediction results, a theoretical mechanical analysis model of the entire drill string was adopted in this paper to develop the quantitative relation between WOB and torque with the friction coefficient of the drill string and oil casing. Suitable friction coefficients along the drill string can be finally obtained by solving the equilibrium function between predicted WOB, torque and measured hook load, rotary-table torque via an iteration algorithm. A case study was performed finally using the proposed intelligent analysis method to calculate the friction coefficients. This proposed methodology can be referenced to decrease the sticking risks and improve the drilling efficiency, which can finally increase the extension limit of horizontal wells in complex strata.
New Generation of Ultra-High Definition Directional Propagation Resistivity for Real Time Reservoir Characterization and Geosteering-While-Drilling
Authors: Sun Keli , Thiel Michael , Mirto Ettore , Tan Sarwa , Liu Jianguo , Feng Yao , Harms Kent , Nesttun Øyen Beate , Pham Tom , Veeraiah Ashwin , Toghi Farid , Liu Ruixia , Wang Hui , Denichou Jean-Michel , Wibowo Vera Krissetiawati , Wang Haifeng , Wang Fei , Snovida Zoriana , Beltran Gil Eliana Carolina , Pinzón Perez Joaquín , Antolinez Delgado Hernan Javier , Liu Wenyu , Wang Jianfu , Omeragic Dzevat
Published: December 2021

Over the last two decades, the continuing integration of distance-to-boundary logging while drilling (LWD) workflows with the directional drilling processes, has dramatically improved geosteering of deviated and horizontal wells. However, the interpretation of underlying propagation azimuthal electromagnetic measurements has remained challenging in complex thin and multi-layered geologies.

Recent technology advancements in LWD electromagnetic propagation resistivity coupled with significant software enhancements provide an opportunity for improving the formation evaluation to reduce wellbore position uncertainty, accurately detecting physical parameters such as layer resistivity and anisotropy, formation dip and azimuth.

A newly developed multilayer mapping-while-drilling service with full azimuthal sensitivity is introduced for use in geosteering and formation evaluation while drilling applications. The tool offers the industry's first combination of axial, tilted and transverse antennas to produce a complete measurement set to enable the interpretation of complex and anisotropic formation. Advanced application algorithms are used to calculate a high-definition map of the formation providing horizontal and vertical resistivity (anisotropy), as well as dipping angle and azimuth. Furthermore, the tool can provide deep resistivity borehole images while drilling in real time.

The new measurement set, more comprehensive than any other directional propagation resistivity tool in the industry, is discussed in detail. The measurements, combined with a new deterministic inversion, enable reconstruction of the resistivity of up to eight formation layers, and significantly outperforms existing directional propagation resistivity services.

The new measurements and data processing workflow are demonstrated with several synthetic and field data. Examples show that this newly developed tool can provide a reliable two-in-one service: geosteering and advanced formation evaluation.
Development of Novel Shale Swelling Inhibitors Using Hydrophobic Ionic Liquids and Gemini Surfactants for Water-Based Drilling Fluids
Authors: Khan Rizwan Ahmed , Murtaza Mobeen , Ahmad Hafiz Mudaser , Abdulraheem Abdulazeez , Kamal Muhammad Shahzad , Mahmoud Mohamed
Published: December 2021

In the last decade, hydrophilic Ionic liquids have been emerged as an additive in drilling fluids for clay swelling inhibition. However, the application of hydrophobic Ionic liquids as a clay swelling inhibitor have not been investigated. In this study, the combination of hydrophobic Ionic liquids and Gemini surfactant were studied to evaluate the inhibition performance.

The novel combination of hydrophobic ionic liquid (Trihexyltetradecyl phosphonium bis(2,4,4-trimethyl pentyl) phosphinate) and cationic gemini surfactant (GB) was prepared by mixing various concentrations of both chemicals and then preparing water based drilling fluid using other drilling fluid additives such as rheological modifier, filtration control agent, and pH control agent. The wettability of sodium bentonite was determined by contact angle with different concentrations of combined solution. Some other experiments such as linear swelling, capillary suction test (CST) and bentonite swell index were performed to study the inhibition performance of ionic liquid.

Different concentrations of novel combined ionic liquid and gemini surfactant were used to prepare the drilling fluids ranging from (0.1 to 0.5 wt.%), and their performances were compared with the base drilling fluid. The wettability results showed that novel drilling fluid having 0.1% Tpb-P - 0.5% GB wt.% concentration has a maximum contact angle indicating the highly hydrophobic surface. The linear swelling was evaluated over the time of 24 hours, and least swelling of bentonite was noticed with 0.1% Tpb-P - 0.5% GB wt.% combined solution compared to linear swelling in deionized water. Furthermore, the results of CST also suggested the improved performance of novel solution at 0.1% Tpb-P - 0.1% GB concentration. The novel combination

The novel combination of hydrophobic ionic liquids and gemini surfactant has been used to formulate the drilling fluid for high temperature applications to modify the wettability and hydration properties of clay. The use of novel combined ionic liquid and gemini surfactant improves the borehole stability by adjusting the clay surface and resulted in upgraded wellbore stability.
A Successful Field Application of Polymer Gel for Water Shutoff in a Fractured Tight Sandstone Reservoir
Authors: Wu Qianhui , Ge Jijiang , Ding Lei , Wei Kaipeng , Liu Yuelong , Deng Xuefeng
Published: December 2021

The wide existence of fractures makes conformance control by polymer gels more challenging in water-flooded oil reservoirs. Selection of an applicable gel system and design of an intelligent approach for gel treatment are key components for a successful field application. Moreover, selecting the candidate wells and determining the injection volume of gel are also critical to the success of gel treatments.

A gel system with adjustable polymer concentrations was applied for conformance control in fractured tight sandstone reservoir, and notably, less than 5% of syneresis was detected after aging for one year at reservoir condition. The viscosity and the gelation time of this gel system can be adjusted according to the targeted reservoir conditions. The pilot test was conducted in Huabei oilfield (China), and the oil recovery after water flooding was only about 20% original oil in place (OOIP). With further exploitation of the oil field, the majority of the reservoir has suffered from poor sweep efficiency and extremely high water cuts. To characterize the distribution of fractures, the seismic coherence cube was utilized. In addition, the pressure transient test, interwell tracer test and the injection-production data were used collaboratively to determine the volume of fractures in the reservoir. The option of gel formulation and the determination of operational parameters are mainly based on the wellhead pressure.

According to the seismic coherence cube, the zone of candidate well group shows a weak coherence state, indicating that numerous fractures exist. Furthermore, there is good continuity between the candidate injection well and the production well. According to the pressure transient test, the volume of re-open fracture is about 1730.9 m3, while the volume of micro-fracture is about 4839.4 m3. Comparably, based on the interwell tracer test, the estimated volume of fractures is approximately 3219.7 m3. Consequently, the designed volume of gel for treatment is 1500.0 m3 in total. The properties of gel slugs were carefully designed, which was tailored to the specific wellbore conditions and formation characteristics. Three months after the gel treatment, the average oil production was increased from 0.36 t/d to 0.9 t/d, and the water cut was decreased from 95.77% to 88.7%. The improved oil production was still benefited from this gel treatment after one year.

This study provides a comprehensive approach, from optimization of gel formulation, followed by selection of candidate wells, to calculation of the injected volume, to design the viable operational parameters, for gel treatment field application in fractured reservoirs. It shows that, besides a gel system with superior properties, a suitable injected volume of gel may enhance the chance of success for gel treatments.
Minimizing Carbon Footprint by Smart Sustainable Reservoir Management
Authors: Katterbauer Klemens , Marsala Alberto , Al Qasim Abdulaziz , Yousif Ali
Published: December 2021

Sustainability and reducing carbon footprint has attracted attention in the oil and gas industry to optimize recovery and increase efficiency. The 4th Industrial Revolution has made an enormous impact in the oil and gas industry and on analyzing carbon footprint reduction opportunities. This allows classification of various reservoir operations, installation of permanent sensors and robots on the field, and reduction of overall power consumption.

We present an overview of new AI approaches for optimizing reservoir performance while reducing their carbon footprint. We will outline the significant carbon emissions contributors for field operations and how their impact will change throughout the production's lifecycle from a reservoir. Based on this analysis, we will outline via an AI-driven optimization framework areas of improvement to reduce the carbon footprint considering the uncertainty.

We analyzed the framework's performance on a synthetic reservoir model with several producing wells, water, and CO2 injecting wells. Beneficial in reducing carbon emissions from the field is the reuse and injection of CO2 for enhancing hydrocarbon production from the reservoir. One hundred different scenarios were then investigated utilizing an innovative autoregressive network model to determine the impact of these components on the overall carbon emission of the field and determine its uncertainty. The conclusions from the analysis were then incorporated into a data-driven optimization routine to minimize carbon footprint while maximizing reservoir performance. The final optimization results of the showcase outlined the ability to reduce the carbon footprint significantly.