bluuebunny/crossref_metadata_2025 huggingface dataset:
A Coupled Model of Temperature and Pressure for Managed Pressure Cementing in Deep-Water Region
Authors: Wang Xuerui , Hao Feng , Sun Baojiang , Wang Zhiyuan
Published: December 2021

The narrow density window in deep-water environment brought great challenges to well drilling and completion by causing well control issues. Managed Pressure Cementing (MPC) is a new technology developed from Manage Pressure Drilling (MPD), which can precisely control the annular fluid pressure profile. Accurate calculation of wellbore temperature and pressure is the key to MPC. This paper focus on coupled models of temperature and pressure for MPC in deep-water region.

The well cementing process can be divided into two stages: fluid displacement stage and cement setting stage, which displays different characteristics. During the cementing displacement stage, the cement is in a flowable slurry state and is circulated into the annulus. During this process, the rheology of fluids if effected by temperature in wellbore. On basis of the fluid rheology model, a coupled model of temperature and pressure in wellbore is established considering the transient flow characteristics during cementing displacement stage. During cement setting stage, the cement slurry stops flowing and the significant cement hydration reaction starts. A large amount of hydration heat and obvious pressure reduction can be observed. On basis of the cement hydration kinetics model, a coupled model of temperature and pressure in wellbore during cementing setting stage is established.

Based on the models established in this paper, a series of numerical simulations are conducted using a deep-water well. Simulation results show that neglecting the complicated interactions between temperature and pressure can cause a big error. During the cementing displacement stage, higher temperature in the deep part of wellbore reduces the fluid viscosity, which leads to a smaller friction. On the contrary, larger friction is observed near seabed as a result of the low temperature in deep-water environment. The pressure in wellbore changes frequently due to the coexistence of multiple fluids in wellbore. Therefore, a frequent control of annular fluid pressure is required using the MPC technology. During the cement setting stage, an obvious temperature increase is observed as a result of cement hydration heat. The pressure decreases with the depending of cement hydration. An addition back pressure at wellhead has to be added using the MPC technology. The transient temperature and pressure have impact on the rate of cement hydration in turn. Cement in the deep part of wellbore have a faster rate of cement hydration. The low temperature at mudline slows the cement hydration process.

Considering the complicated interactions between temperature, pressure, cement hydration and fluid rheology, coupled models between temperature and pressure based on hydration kinetics during well cementing in deep-water region is established in the manuscript. The new model established in this paper plays an important role in the MPC technology.
The Measurement of Tortuosity of Porous Media Using Imaging, Electrical Measurements, and Pulsed Field Gradient NMR
Authors: Elsayed Mahmoud , Kwak Hyung , El-Husseiny Ammar , Mahmoud Mohamed
Published: December 2021

Tortuosity, in general characterizes the geometric complexity of porous media. It is considered as one of the key factors in characterizing the heterogonous structure of porous media and has significant implications for macroscopic transport flow properties. There are four widely used definitions of tortuosity, that are relevant to different fields from hydrology to chemical and petroleum engineering, which are: geometric, hydraulic, electrical, and diffusional. Recent work showed that hydraulic, electrical and diffusional tortuosity values are roughly equal to each other in glass beads. Nevertheless, the relationship between the different definitions of Tortuosity in natural rocks is not well understood yet. Understanding the relationship between the different Tortuosity definitions in rocks can help to establish a workflow that allows us to estimate other types from the available technique. Therefore, the objective of this study is to investigate the relationship between the different tortuosity definitions in natural rocks. A major focus of this work is to utilize Nuclear Magnetic Resonance (NMR) technology to estimate Tortuosity. Such technique has been traditionally used to obtain diffusional tortuosity which can be defined as the ratio of the free fluid self-diffusion coefficient to the restricted fluid self-diffusion coefficient inside the porous media.

In this study, the following techniques were used to quantify hydraulic, electrical, and diffusional tortuosity respectively on the same rock sample: (1) Microcomputed Tomography 3D imaging (2) Four-Electrodes resistivity measurements (3) Pulsed-Field Gradient Nuclear Magnetic Resonance (PFG NMR). PFG NMR is very powerful, non-invasive technique employed to measure the self-diffusion coefficient for free and confined fluids. The measurements were done based on two carbonate rock core plugs characterized by variable porosity, permeability and texture complexity.

Results show that PFG NMR can be applied directionally to quantify the pore network anisotropy created by fractures. For both samples, hydraulic tortuosity was found to have the lowest magnitude compared to geometric, electrical and diffusional tortuosity. This could be explained by the more heterogeneous microstructure of carbonate rocks. NMR technique has however advantages over the other electrical and imaging techniques for tortuosity characterization: it is faster, non-destructive and can be applied in well bore environment (in situ). We therefore conclude that NMR can provide a tool for estimating not only diffusional tortuosity but also for indirectly obtaining hydraulic and electrical tortuosity.
Bahrain LNG Import Terminal Project (BLNGIT) - 1st LNG Commissioning Cargo and Integration of Imported RLNG with Domestic Khuff Gas in the Existing Network
Authors: AlKhunaizi Fawaz A. Aziz , Prudhvi Muni Shankar , Mohamed Anwar
Published: December 2021

In order to meet the country's future energy requirements, and in particular the increased demand for electricity and water in addition to major industrial growth, Bahrain's National Oil and Gas Authority (NOGA) has taken the strategic decision to import Liquefied Natural Gas (LNG) to supplement existing local gas supplies as a long term energy policy.

NOGA and the National Oil and Gas Holding Company B.S.C (c) (NH) signed project agreements in 2015 for the development of an LNG Receiving and Regasification Terminal in the Kingdom of Bahrain with an international consortium.

The purpose of this paper is to shed light on this ‘first-of-its-kind’ project development in the Kingdom of Bahrain in order to highlight (i) the challenges and management of such challenges, (ii) the ultimate project/operational objectives and successful results, and (iii) lessons learnt.
A Single Artificial Neural Network Model Predicts Bubble Point Physical Properties of Crude Oils
Authors: Al-Marhoun Muhammad
Published: December 2021

Reservoir fluid properties at bubble points play a vital role in reservoir and production engineering computations. Ideally, the bubble point physical properties of crude oils are obtained experimentally. On some occasions, these properties are neither available nor reliable; then, empirically derived correlations or artificial neural network models are used to predict the properties.

This study presents a new single multi-input multi-output artificial neural network model for predicting the six bubble point physical properties of crude oils, namely, oil pressure, oil formation volume factor, isobaric thermal expansion of oil, isothermal compressibility of oil, oil density, and oil viscosity. A large database comprising conventional PVT laboratory reports was collected from major producing reservoirs in the Middle East. The model input is constrained mathematically to be consistent with the limiting values of the physical properties. The new model is represented in mathematical format to be easily used as empirical correlations.

The new neural network model is compared with popular fluid property correlations. The results show that the developed model outperforms the fluid property correlations in terms of the average absolute percent relative error.
Implementing Remote Mudlogging Solutions to Support a Deepwater Project in the Caribbean: A De-Manning Case Study
Authors: Smith Evan
Published: December 2021

Today's oil and gas industry is a global endeavor. With technological advances in data management and transfer, the ability for experienced engineers to receive, interpret, and make decisions from all over the globe in near real-time is not only achievable, but is becoming more desirable. Provoked by downturns and reduced personnel numbers, methods of increasing efficiency and cost reduction has gradually moved engineers away from the rig site, while still undertaking the same roles and responsibilities. This paper examines one case for an operator in the Caribbean.

One major client drilling in the Caribbean was forced to explore reduced staffing options on one of its deep-water drilling rigs after flight cancellations, border closures, and isolation/quarantine procedures were implemented due to the COVID-19 pandemic. This made getting experienced data engineers and sample collection personnel to the rig site impossible. Two data engineers, two mud loggers, and two sample catchers are on the rig during normal operations, but with the above-mentioned challenges, only two mud loggers remained on site. The mudlogging service provider proposed intercompany collaboration with a region experienced in remote operational support, and a remote monitoring station was set up and manned with experienced data engineers to support real-time operations. A focal point between the remote engineers and the rig team was designated, and was responsible for communicating roles and responsibilities, linking the two teams. A robust communication protocol was established between the mudlogging crew, the remote personnel, the drill floor, and the company man which outlined specifics of which events would trigger communication between parties.

Two intermediate hole sections were successfully drilled, without any interruption or delay. The remote engineers successfully participated in the rigs well control drills, calling directly to the rig when needed. During drilling, the experienced remote personnel were able to provide topic specific guidance to the less experienced engineers at the rig site, which accelerated their on-the-job training. This guidance encouraged and allowed for decreased reliance on the remote support over the course of drilling. The operator considered the implementation of the remote engineers a success and looked to implement additional remote resources from other service lines and providers.

Development of additional remote support opportunities directly reduces risk and cost of personnel at the rig site throughout all aspects of the oil and gas industry. Reduction of personnel on site reduces overall exposure to the hazards associated with the rig site and would decrease the probability of incident. Recent improvements in technology and communication have made it possible for this to be a viable solution to de-manning the rig site in an evolving industry.
A New Approach for Building Composite Cores for Corefloods in Complex Carbonate Rocks
Authors: Cinar Yildiray , Zayer Ahmed , Dawood Naseem , Krinis Dimitris
Published: December 2021

Carbonate reservoir rocks are composed of complex pore structures and networks, forming a wide range of sedimentary facies. Considering this complexity, we present a novel approach for a better selection of coreflood composites. In this approach, reservoir plugs undergo a thorough filtration process by completing several lab tests before they get classified into reservoir rock types. Those tests include conventional core analysis (CCA), liquid permeability, plug computed tomography (CT), nuclear magnetic resonance (NMR), end-trim mercury injection capillary pressure (MICP), X-ray diffraction (XRD), thin-section analysis (TS), scanning electron microscopy (SEM), and drainage capillary pressure (Pc). We recommend starting with a large pool of plugs and narrowing down the selection as they complete different stages of the screening process. The CT scans help to exclude plugs exhibiting composite-like behavior or containing vugs and fractures that potentially influence coreflood results. After that, the plugs are categorized into separate groups representing the available reservoir rock types. Then, we look into each rock type and determine whether the selected plugs share similar pore-structures, rock texture, and mineral content. The end-trim MICP is usually helpful in clustering plugs having similar pore-throat size distributions. Nevertheless, it also poses a challenge because it may not represent the whole plug, especially for heterogeneous carbonates. In such a case, we recommend harnessing the NMR capabilities to verify the pore-size distribution. After pore-size distribution verification, plugs are further screened for textural and mineral similarity using the petrographic data (XRD, TS, and SEM). Finally, we evaluate the similarity of brine permeability (Kb), irreducible water saturation (Swir) from Pc, and effective oil permeability data at Swir (Koe, after wettability restoration for unpreserved plugs) before finalizing the composite selection.

The paper demonstrates significant aspects of applying the proposed approach to carbonate reservoir rock samples. It integrates geology, petrophysics, and reservoir engineering elements when deciding the best possible composite for coreflood experiments. By practicing this workflow, we also observe considerable differences in rock types depending on the data source, suggesting that careful use of end-trim data for carbonates is advisable compared to more representative full-plug MICP and NMR test results. In addition, we generally observe that Kb and Koe are usually lower than the Klinkenberg permeability with a varying degree that is plug-specific, highlighting the benefit of incorporating these measurements as additional criteria in coreflood composite selection for carbonate reservoirs.
Accurate Carbonate Pore System Characterization by Nuclear Magnetic Resonance and Micro-CT Techniques
Authors: Gao Jun , Kwak Hyung T. , AlSinan Marwah
Published: December 2021

Carbonate reservoir rocks usually have complex pore systems of broad size distributions, which determine many aspects of oil exploitation, from petrophysical properties to oil/water displacements. An accurate and complete description of these pore systems remains a challenge. A single technique often gives one measurement of complicated microscopic pore space. The new techniques (i.e., micro-CT and NMR) are utilized together with conventional methods (e.g., MICP, BET) to capture a more accurate and complete picture of pore structures.

MICP measures the pore throat while the NMR T2 mainly measures the pore body. Micro-CT provides a 3D image of a limited sample size. Recently, NMR DDIF (decay due to diffusion in the internal field) for direct pore body size is extended from high to low magnetic field, which overcomes many limitations in pore system characterization. This study obtains pore throat size distributions from in-situ centrifuge capillary pressure and pore body size distributions from low field DDIF measurement and verifies them with micro-CT and BET/T2 in different types of carbonate rocks.

The pore throat size distribution of the conventional sample is obtained from in-situ centrifuge capillary pressure. The major features of both macro and micro pore throat size distributions are captured. Pore size distributions are directly obtained from glass beads and carbonate rocks without calibration. Combined analysis of the pore size distribution from two methods reveals the underlying causes of their different petrophysical properties. The pore throat size distribution from in-situ centrifuge capillary pressure and pore size distribution from NMR DDIF can be employed to obtain a better understanding of conventional carbonate pore systems.
Artificial Intelligence and Data Analytics for Virtual Flow Metering
Authors: Gryzlov Anton , Mironova Liliya , Safonov Sergey , Arsalan Muhammad
Published: December 2021

Modern challenges in reservoir management have recently faced new opportunities in production control and optimization strategies. These strategies in turn rely on the availability of monitoring equipment, which is used to obtain production rates in real-time with sufficient accuracy. In particular, a multiphase flow meter is a device for measuring the individual rates of oil, gas and water from a well in real-time without separating fluid phases. Currently, there are several technologies available on the market but multiphase flow meters generally incapable to handle all ranges of operating conditions with satisfactory accuracy in addition to being expensive to maintain.

Virtual Flow Metering (VFM) is a mathematical technique for the indirect estimation of oil, gas and water flowrates produced from a well. This method uses more readily available data from conventional sensors, such as downhole pressure and temperature gauges, and calculates the multiphase rates by combining physical multiphase models, various measurement data and an optimization algorithm.

In this work, a brief overview of the virtual metering methods is presented, which is followed by the application of several advanced machine-learning techniques for a specific case of multiphase production monitoring in a highly dynamic wellbore. The predictive capabilities of different types of machine learning instruments are explored using a model simulated production data. Also, the effect of measurement noise on the quality of estimates is considered. The presented results demonstrate that the data-driven methods are very capable to predict multiphase flow rates with sufficient accuracy and can be considered as a back-up solution for a conventional multiphase meter.
Thermal Effect on Formation Stability Due to Heterogeneity
Authors: Batarseh Sameeh , San Roman Alerigi Damian , Al Harith Abdullah , Assiri Wisam
Published: December 2021

This study evaluates physical and chemical changes induced by high thermal gradients on the formation and their impact to the stability. The heat sources that effect the formation’s stability are varied, including drilling (due to drilling bit friction), perforation, electromagnetic heating (laser or microwave), and thermal recovery or stimulation (steam, resistive heating, combustion, microwave, etc.). This study uses an integrated approach to characterize rock heterogeneity and mapping heat propagation from different heat sources. The information obtained from the study is vital to accurately design and enhance well completion and stimulation

This is an integrated analysis approach combining different advanced characterization and visualization techniques to map heat propagation in the formation. Advanced statistical analysis is also used to determine the key parameters and build fundamental prediction algorithms. Characterization on the samples was performed before, during, and after the exposure to thermal sources; it comprised thin-section, high speed infrared thermography (IR), differential thermal analysis and thermogravimetric analyzer (DTA/TGA), scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), uniaxial stress, and autoscan (provide hardness, composition, velocity, and spectral absorption). The results are integrated, and machine learning is used to derive a predictive algorithm of heat propagation and mapping in the formation with reference to the key formation variables and heterogeneity distribution.

Rock heterogeneity affects the rate and patterns of heat propagation into the formation. Within the rock sample, minerals, laminations, and cementations lead to a heterogeneous, and sometimes anisotropic, distribution of thermal properties (thermal conductivity, heat capacity, diffusivity, etc.). These properties are also affected by the rock structure (porosity, micro-cracks, and fractures) and saturation distribution. The results showed the impact of heat on the mechanical properties of the rocks are due to clays dehydration, mineral dissociations, and micro cracks. High speed thermal imaging provides a unique visualization of heat propagation in heterogeneous rocks. Statistical analysis identified key parameters and their impact on thermal propagation; the output was used to build a machine learning algorithm to predict heat distributions in core samples and near-wellbore.

Characterizing rock properties and understanding how heterogeneity modifies heat propagation in rocks enables the design of optimal completion and stimulation strategies. This paper discusses how advanced characterization and analysis, combined with novel algorithms, can improve this understanding, and unleash innovation and optimization. The data and information gathered are critical to develop numerical models for field-scale applications.
An Innovative Methodology for Estimating Rock Mechanical Properties from Weight or Volume Fractions of Mineralogy and its Application to Middle East Reservoirs
Authors: Prasad Umesh , Hanif Amer , McGlynn Ian , Walles Frank , Abouzaid Ahmed , Hamid Osman
Published: December 2021

The influences of mineralogy on rock mechanical properties have profound application in oil and gas exploration and production processes, including hydraulic fracturing operations. In conventional resources, the rock mechanical properties are predominantly controlled by porosity; however, in unconventional tight formations, the importance of mineralogy as a function of rock mechanical properties has not been fully investigated.

In unconventional tight formations, mechanical properties are often derived from mineralogy weight fraction together with the best estimate of porosity, assumption of fluid types, the extent of pore fillings, and fluid properties. These properties are then adjusted for their volumetric fractions and subsequently calibrated with acoustics or geomechanical lab measurements. A new method is presented that utilizes mineralogy weight fractions (determined from well logs or laboratory measurements). This process uses public domain information of minerals using Voigt and Reuss averaging algorithms as upper and lower bounds, respectively. An average of these bounds (also known as Hill average) provides a representative value for these parameters. Further, based on isotropic conditions, all the elastic properties are calculated.

A typical output consisting of bulk-, shear-, and Young's - modulus, together with Poisson's ratio obtained from traditional methods of volume fractions and this new method using weight fractions is discussed and analyzed along with the sensitivity and the trends for individual rock properties. Furthermore, corresponding strengths, hardness, and fracture toughness could also be estimated using well known public domain algorithms.

Data from carbonate reservoirs has been discussed in this work. This method shows how to estimate grain compressibility that can be challenging to be measured in the lab for unconventional tight rock samples. In low-porosity samples, the relative influence of porosity is negligible compared to the mineralogy composition. This approach reduces several assumptions and uncertainties associated with accurate porosity determination in tight rocks as it does not require the amount of pore fluids and fluid properties in calculations. The grain-compressibility and bulk-compressibility (measured by hydrostatic tests in the laboratory on core plugs or calculated from density and cross-dipole log) are used to calculate poroelastic Biot's coefficient, as this coefficient will be used to calculate in-situ principal effective stresses (overburden, minimum horizontal, and maximum horizontal stresses), which are, together with rock properties and pore pressure, constitutes the geomechanical model. The geomechanical model is used for drilling, completions, and hydraulic fracture modeling, including wellbore stability, and reservoir integrity analyses.
Leveraging Acoustic Logging for Identification & Quantification of Induced Fractures
Authors: Asif Adnan Bin , Alaliwat Mustafa , Hansen Jon , Sheshtawy Mohamed
Published: December 2021

The main objective of the acoustic logging in 15K openhole multistage fracturing completions (OH MSFs) is to identify the fracture initiation points behind pipe and contributing fractures to gas production. The technique will also help to understand the integrity of the OH packers. A well was identified to be a candidate for assessment through such technique. The selected well was one of the early 15K OH MSF completions in the region that was successfully implemented with the goal of hydrocarbon production at sustained commercial rates from a gas formation.

The candidate well was drilled horizontally to achieve maximum contact in a tight gas sandstone formation. Similar wells in the region have seen many challenges of formation breakdown due to high formation stresses. The objective of this work is to use the acoustic data to better characterize fracture properties. The deployment of acoustic log technology can provide information of fractures initiation, contribution for the production and the reliability of the isolation packers between the stages.

The candidate well was completed with five stages open-hole fracturing completion. As the well is in an open hole environment, a typical PLT survey provides the contribution of individual port in the cumulative production but provides limited or no information of contributing fractures behind the pipe. The technique of acoustic logging helped to determine the fracture initiation points in different stages. If fractures can be characterized more accurately, then flow paths and flow behaviors in the reservoir can be better delineated.

The use of acoustic logging has helped to better understand the factors influencing fracture initiation in tight gas sandstone reservoirs; resulting in a better understanding of fractures density and decisions on future openhole length, number of fracturing stages, packers and frac ports placement.
Integration of Cutting Spectroscopy Analysis and Open-Hole Logs to Increase Evaluation Certainty of Complex Clastic Formations – Advantages and Limitations
Authors: Alqunais Ali , Bradford Charles , Qubaisi Khalid
Published: December 2021

This paper presents an approach by integrating advanced cutting analysis, such as x-ray fluorescence (XRF), and open-hole logs for enhanced formation evaluation of complex clastic formations in near real-time. To verify the methodology, results of surface cuttings analyses are compared to and validated with downhole elemental spectroscopy measurements.

In general, when the formation contains clays, the minimum logging requirement to evaluate clastic formations is a triple combo (density, neutron and resistivity) with spectral gamma ray (SGR) logs. In addition to correcting the impact of the drilling fluid additives and properties such as the presence of k-formate in mud, SGR logs become very crucial to differentiate clay types present in the formation. In the absence of SGR, advanced cuttings measurements can be utilized to provide elemental data of major elements including SGR components from the cuttings in near real-time. A comparison was made to evaluate the cuttings analysis as a replacement for SGR. As a part of this work and to validate the petrophysical evaluation results, downhole wireline SGR and elemental spectroscopy data were acquired and compared to the analysis using advanced cutting measurements.

This work was conducted in a siliciclastic formation containing abrasive sandstones of mixed clean quartz and clay minerals. The analysis of cuttings XRF was integrated with basic downhole logs to quantify the clay typing required for representative formation evaluation and well geosteering. Limitations of this approach are identified in drilling complex clastic formations including cutting sampling frequency and effects of drilling including drilling fluid contamination, mud additives, drilling parameters and drilling driving mechanism. Controlling these factors has led to good results from cuttings measurements. The advanced cuttings XRF analysis was benchmarked with wireline SGR and elemental spectroscopy logs.

This approach of using cuttings XRF analysis and basic open-hole logs is a valid option for geosteering in a complex clastic mineralogy formation and providing a near real-time formation evaluation in the absence of spectral gamma ray or elemental spectroscopy. XRF has been proven to provide near real-time analysis with improved reliability across bad hole, wider spectrum of elements and eliminate critical operations risk. Recommendations to optimize the parameters for reliable measurements will be discussed in this paper.
Nanoparticle Tracers in Reservoir-On-A-chip by Surface-Enhanced Raman Scattering - Fluorescence SERS-SEF Imaging Technology
Authors: Chang Sehoon , Eichmann Shannon L. , Wang Wei
Published: December 2021

Nanoparticles or nanocomposite fluids are injected into oil reservoirs for reservoir tracing or to improve injectivity or recovery of oil. Effective application of nanoparticles in fluid flooding still needs to be investigated. Dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles have been developed as nanoparticle reservoir tracers. This presentation discusses their transport and detectability in porous media, providing valuable information for understanding the role of nanoparticles in EOR process. The dual-mode surface-enhanced Raman scattering (SERS) - surface-enhanced fluorescence (SEF) composite nanoparticles are synthesized composed of Ag or Au metal cores, specific dye molecules, and a SiO2 shell materials. To optimize maximum signal enhancement of both phenomena such as SERS and SEF, the distance between core metal nanoparticles and dye molecules are precisely controlled. The synthesized composite nanoparticles barcoded with dye molecules are detectable by both fluorescence and Raman spectroscopies due to the SERS-SEF phenomena. Both fluorescence and Raman microscopic images of dye embedded surfaceenhanced Raman scattering (SERS) surface-enhanced fluorescence (SEF) composite nanoparticles in water phase successfully were collected within microfluidic reservoir-on-a-chip. The reservoir-on-a-chip utilized in this study fabricated based on reservoir rock geometry and coated with calcium carbonate. The synthesized SERS-SEF composite nanoparticles in water solution have been flooded into the microfluidic reservoir-on-a-chip and imaged for probing interfacial behavior of fluids such as liquid-liquid interfaces and studying the behavior of nanoparticles at liquid-rock interfaces. The precise synthesis method to produce the composite nanoparticles has been developed for the embedded dye molecules to generate noticeably enhanced detectability due to the strong SERS phenomenon. In conclusion, SERS-SEF nanoparticles barcoded with the fingerprinted Raman and fluorescence signals can provide a possible pathway toward SERS-SEF nanoprobe as various barcoded tracers to understand fluid behavior in porous media. Composite nanoparticle synthesis and its detection in flow technologies have been developed for visualization of the fluid flow behavior in porous media representing reservoir rock geometry. The results of the high-resolution nanoparticle fluid imaging data in reservoir-on-a-chip can be applied to understand mechanism of nanoparticle fluid assisted chemical enhanced oil recovery.
Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs
Authors: Katterbauer Klemens , Marsala Alberto , Zhang Yanhui , Hoteit Ibrahim
Published: December 2021

Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner.

In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach.

We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.
Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation
Authors: Magana-Mora Arturo , AlJubran Mohammad , Ramasamy Jothibasu , AlBassam Mohammed , Gooneratne Chinthaka , Gonzalez Miguel , Thiel Tim , Deffenbaugh Max
Published: December 2021

Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs.

Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events.

Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT.

Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.
Machine Learning Microservice for Identification of Accident Predecessors
Authors: Gurina Ekaterina , Antipova Ksenia , Klyuchnikov Nikita , Koroteev Dmitry
Published: December 2021

Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar.

The model for forecasting the drilling accidents is based on the "Bag-of-features" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model.

The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model.

The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.
The Value of Seismic Data in Conventional and Unconventional Fields
Authors: Alghamdi Nawaf , Alghenaim Hamad
Published: December 2021

The paper illustrates the value of seismic data in different environments after assessing the benefits and costs of processes such as seismic acquisition, seismic processing and seismic interpretation. Global examples from conventional and unconventional fields are discussed to show how seismic data plays a significant role in determining low-risk and high-reward wells and also eliminating the high-risk and low-reward wells.

This paper shows an example of a conventional field in the state of Kansas, USA, where the net present value (NPV) increased by more than 17 times when 3D seismic data was acquired, while in an unconventional field the commercial success rate rose from 30% to 70% due to 3D seismic acquisition. However, two offshore fields in the Republic of Trinidad and Tobago are discussed to show that the NPV as impacted by advanced seismic processing was more than 111 ($M). Another example comes from Viking Field, a conventional field in Canada, where the NPV was increased from 3800 ($M) to 5000 ($M) when the seismic data was re-processed. Furthermore, the value of investing in seismic data was investigated and quantified by comparing two synthetically modeled scenarios in Saudi Arabia.

Overall, the four examples from North America, Central America and Saudi Arabia illustrate that investment in seismic data has a positive impact on both conventional and unconventional fields. That provides strong evidence to encourage more investments in geophysical technologies.
A Non-Damaging Gelled Acid System Based on Surface Modified Nanoparticles
Authors: Kalgaonkar Rajendra , Bataweel Mohammed , Alkhowaildi Mustafa , Sahu Qasim
Published: December 2021

Gelled acid systems based upon gelation of hydrochloric acid (HCl) are used widely in acid stimulation treatments to prevent fluid leak-off into the high permeable zones of a reservoir. The gelled-up fluid system helps retard the acid reaction to allow deeper acid penetration for hydrocarbon productivity enhancement. Conventional in-situ crosslinked gelled acid systems are made up of polyacrylamide gelling agent, iron-based crosslinker, and a breaker chemical in addition to other additives, with the acid as the base fluid. The polymer-based systems can lead to damage to formation due to a variety of reasons including unbroken polymer residue. Additionally, the iron-based crosslinker systems can lead to scaling or precipitation after the acid reacts with the formation, resulting in formation damage and lowering of hydrocarbon productivity.

In this paper, we showcase a new nanoparticles-based gelled acid system that does not contain any polymer or iron-based crosslinker that can potentially damage the formation. It comprises nanoparticles, a gelation activator, acidizing treatment additives along with HCl. The new in-situ gelled acid system has low viscosity at surface making it easy to pump. With increase in the temperature and as the acid spends there is a viscosity increase. The viscosification and eventual gelation of the new system can be achieved as the acid reacts with a carbonate formation. As the acid further reacts and continues to spend, the gel demonstrates reduction of viscosity. This assists in a better cleanup post the acidizing treatment.

Various experimental techniques were used to highlight the development of the nanoparticle-based acid diversion fluid. The gelation properties of the acid system, as a function of acid strength and temperature, are investigated. Static and dynamic gelation studies as a function of time, temperature and pH are reported. It is demonstrated that the viscosification property is a function of pH and the gelation occurs in a pH widow from 1 to 5 pH units. The gelation performance of the new system is evaluated at temperatures up to 300°F. The effect of different types of surface modification chemistries on the gelation properties is investigated. It is also shown that the gelation and viscosity reduction is entirely a pH dependent phenomenon and does not require any additional breaker chemistry; and therefore provides more control over the system performance.

The new gelled acid system overcomes the inherent challenges faced by conventional in-situ crosslinked gelled acid systems; as it is based upon nanoparticles making it less prone to formation damage as compared to a crosslinked polymer-based system.
Advanced Horizontal Well Correlation Method for Dynamic Update of Subsurface Layers While Geosteering
Authors: Al-Maskeen Abdul Mohsen , Ali Sadaqat
Published: December 2021

A new automated approach to well correlation is presented that utilizes real-time Logging While-Drilling (LWD) data and predicted well curve to dynamically update subsurface layers during geosteering operations. The automatically created predicted log and a dynamically updated structural framework provides the foundation of the process. The predicted log is created using vertical sections of the nearby wells, which provide high confidence for determining depth and stratigraphic position of the geosteered well. The results give a better understanding of thickness variation in the horizontal part of the reservoir and maximize the reservoir contact (Sung, 2008).

A new advanced methodology introduced in this study involves the creation of a dynamic structural framework model, from which horizontal well correlation is performed using real-time well logs and predicted logs that are generated from adjacent wells. The predicted logs are correlated to the LWD logs using anchor points and an interactive stretching and squeezing process that honors true stratigraphic thickness. Each new anchor point results in the creation of an additional control point that is used to build a more precise structural framework model.

This new approach enables more rapid well log interpretation, increased accuracy and the ability to dynamically update the subsurface model during drilling. It also enables more efficient steering of the wellbore into the most productive zones of the reservoir. This study demonstrates how wells with over 10,000 feet of horizontal reservoir contact can be correlated in a real-time geosteering environment in a dynamic, efficient and accurate manner. The proposed process dramatically helps reduce the cost of drilling and the time it takes to dynamically regenerate accurate updated maps of the subsurface. It represents a major improvement in the understanding and modeling of complex, heterogeneous reservoirs by fostering a multi-disciplinary environment of cross-domain experts that are able to collaborate seamlessly as asset-teams. Both accuracy and efficiency gains have been realized by incorporating this methodology in the characterization of multi-stacked reservoirs.