bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of Downhole Drilling Dynamics to Understand Drilling Performance Challenges in Unconventional Horizontal Wells: Delaware Basin Wolfcamp Shale Case Study
Authors: Barraza Jesus , Sanchez Oscar , Taylor Richard 
Published: October 2020

Abstract
There are many challenges to be overcome when drilling lateral sections in the Wolfcamp horizon of the Delaware Basin. These include wellbore instability, formation heterogeneity, low rate of penetration (ROP), poor weight transfer and drill bit damage. Downhole data was captured in order to provide insight into these challenges.
A Downhole Dynamics Tool (DDT) was used to capture the data necessary to investigate the drilling performance challenges. The data collected was downhole weight on bit (DHWOB) and torque, downhole bending moments and tool face, annular and bore pressure readings, and downhole vibrations. The analysis of these data provided insights into the effectiveness of connection and WOB zeroing practices, clarity about recurrent bit nozzle plugging and bit damage, and a better understanding of wellbore tortuosity and effective WOB transfer.
The DDT was used on multiple wells, in memory mode and in real-time. On the initial trial well, the tool was run in memory-only mode due to drilling system requirements; subsequently real-time data was additionally used to further optimize drilling performance. After retrieving the data and analyzing trends from the initial memory-only runs, the following could be highlighted: Using downhole pressure and WOB, it was shown that the bit was still on-bottom during connections. Pipe stretch calculations were performed and matched with downhole data in order to verify minimum stick up length required to effectively pick the bit off-bottom during connections. This allowed the development and adoption of a new standard for connection practices.The downhole bending moment data identified the presence of wellbore rugosity driven by several factors, including magnitude of the steering force from the rotary steerable system (RSS) steering unit.Downhole WOB data showed that weight transfer efficiency was not 100%. The loss of WOB is inferred to be caused by factors such as hole rugosity, inconsistently zeroed surface WOB and friction losses.The data obtained about the causes of bit plugging, bit coring damage, and wellbore rugosity, was used to redesign and optimize future bits to be used in lateral drilling operations. Some of the recommended enhancements included changes in nozzle orientation, improved impact resistance for inner cutting structure and improved gauge pad design.
Use of downhole data identified factors affecting drilling performance which differed from what was initially believed. The use of downhole data enabled changes in operational practices, drill string and bottom hole assembly (BHA) design, bit design, and drilling parameter roadmaps. The lessons learned and best practices from the data analysis were successfully transferred and implemented across all development areas in the Permian Basin.
Simultaneous Assessment of Wettability and Water Saturation Through Integration of 2D NMR and Electrical Resistivity Measurements
Authors: Azizoglu Zulkuf , Posenato Garcia Artur , Newgord Chelsea , Heidari Zoya 
Published: October 2020

Abstract
Nuclear Magnetic Resonance (NMR) transverse relaxation (T2) measurements are sensitive to fluid saturations and wettability of the rocks. However, quantifying wettability index solely from NMR T2 models relies on accurate estimates of water saturation. It has been shown that integrating resistivity measurements and NMR T2 distributions enables simultaneous assessment of wettability and water saturation. Nevertheless, there still exist significant uncertainties in this approach in the presence of multiple types of fluids with overlapping transverse relaxation times or multi-modal pore-size distribution. Thus, the objectives of this paper are (a) to introduce a new multi-physics workflow integrating 2-dimensional (2D) NMR with resistivity measurements to simultaneously quantify wettability index and water/hydrocarbon saturation and (b) to verify the reliability of the introduced workflow with core measurements in different rock types.
The workflow starts by using 2D diffusivity-transverse relaxation (D-T2) or longitudinal-transverse relaxation (T1-T2) measurements to estimate fluid saturations. We use a nonlinear inversion algorithm to fit a multi-modal Gaussian distribution to the 2D NMR measurements. We then calculate water and hydrocarbon saturations using the fluid volumes estimated from the multi-modal Gaussian distribution. This estimate of water/hydrocarbon saturation is an input to our new physics-based resistivity model that explicitly incorporates the influence of wettability. The inputs to the resistivity model include water saturation, resistivity index, and pore-geometry-related parameters. In the core-scale verification step, we compare the results of wettability and fluid saturations obtained from the new workflow with gravimetrically assessed water saturation, and Amott Index measurements on core sample.
We successfully verified the reliability of the new workflow with experimental measurements. Estimated water saturation using the introduced workflow resulted in an average relative error of less than 6% compared to the gravimetrically assessed water saturations. Wettability indices obtained from the workflow were in agreement with those estimated using Amott indices. The average absolute error between the estimated wettability indices and the Amott indices was 0.34. In conclusion, results demonstrated that integration of 2D NMR and electrical resistivity measurements enables reliable and simultaneous assessment of wettability and water saturation in different rock types. The method introduced in this paper is promising for reliable and real-time wettability assessment both in the laboratory and in-situ condition. It provides a physics-based and robust method to accurately and simultaneously estimate water/hydrocarbon saturation and wettability in hydrocarbon-bearing rocks.
A New Approach for Sand Control: High-Rate Water Pack for Unconsolidated Reservoirs
Authors: Nunez Walter , Valencia Brian , Polo Rosana , Vera William , Lizcano Silvia , Noguera Andres 
Published: October 2020

Abstract
Various sand control techniques were implemented in producing wells of an eastern Colombian field, but the lack of success rendered them technically and economically prohibitive, so they were not used again. Consequently, a sand management study was conducted in the field.
The study found that by choosing the correct sand control technique, Productivity Index (PI) was improved, allowing wells to produce which could not do so in the past. The new technique, called the High-Rate Water Pack (HRWP) method, also allowed optimum drainage of reserves and opened opportunities for new developments.
This paper discusses candidate selection, fluid design, perforating technique, screen design, proppant selection, pumping scheme, operational aspects, and post-production methodology, which together infer the performance of productivity.
Field cases will be presented that show how oil production reached 1,000 BOPD in six treated wells, improving economics in wells where the HRWP technique was applied.
Seismic Inversion for Risk Reduction in Drilling and Production in Unconventional Reservoirs
Authors: Sayers Colin , Dasgupta Sagnik , Gofer Edan , Hearn Richard , Koesoemadinata Adam , Sudhakar Vasudhaven 
Published: October 2020

Abstract
Knowledge of pore pressure, in-situ stress, and lithology in unconventional reservoirs is important for safe and economic drilling, hydrocarbon production, and geomechanics applications such as wellbore stability analysis and hydraulic fracturing. Reliable predrill predictions of pore pressure, in-situ stress, and lithology are thus required for safe drilling and optimal development in such reservoirs. In the Permian Basin, changes in lithology occur over vertical depths that cannot be resolved by seismic velocities obtained by kinematic analysis, as these have poor vertical resolution. To obtain improved vertical resolution, seismic prestack depth-migrated (PSDM) data are input to amplitude variation with offset (AVO) inversion, for an area in the Delaware Basin where wide-offset 3D seismic data are available. AVO inversion provides estimates of both P- and S-impedance. The results are used to build a 3D mechanical earth model, which is employed to predict pore pressure, in-situ stress, and geomechanical properties. The model enables integrating the results of seismic inversion with drilling data, measurements on cores, wireline logs, formation and fracture closure pressures, and other data. By employing P- and S-impedance, and their ratio, pore pressure, in-situ stress, and lithology derived from seismic prestack inversion provides greater resolution than estimates obtained using seismic velocities from kinematic analysis. Examples from the Permian Basin illustrate the importance of the results for unconventional reservoir development.
Improving the Estimation of Fracture Length Using Resistivity and Borehole Image Logs
Authors: Gong Bo , Wang Hanming , Crousse Luisa 
Published: October 2020

Abstract
Understanding the geometry and distribution of subsurface fracture networks is essential for reservoir quality characterization, field production forecast, well placement optimization, and hydrocarbon recovery design. Estimating fracture properties with well data has long been a challenge, because wells only intercept a very small volume of rock, and hence have a high chance of under-sampling fracture networks. Among all the fracture properties, fracture length is one of the most difficult aspect to characterize, as the fracture planes are often truncated by wells. In practice, most fracture lengths are observed from outcrops, which may differ from the actual subsurface environment. Due to its close links to vertical and lateral connectivity within the reservoirs, fracture length could be a crucial factor in assessing the potential for hydrocarbon transport. Therefore, a reliable source for fracture length data would greatly reduce the high uncertainties associated with fractured reservoir characterization.
In this study, we present a method that categorizes fracture length using laterolog-type resistivity and electrical borehole image logs, based on the different sensitivities of the two types of downhole measurements. In a well drilled with water-based mud, open fractures can get invaded and become electrically conductive, altering the responses of resistivity and imaging tools. While borehole imaging tools are capable of detecting many open fractures regardless of their length, resistivity tools are generally more sensitive to long fractures. Through numerical modeling and inversion, resistivity and borehole image can be processed to locate open fractures. By comparing the identified fractures from resistivity and borehole image in one well, one can separate the long fractures from the short ones.
The method was tested on a horizontal well in a carbonate formation, where two fracture sets were originally identified. After a joint interpretation with resistivity and borehole image, one set of the fractures appeared to be consistently shorter than the other. Upon further analysis of the local stress regime, the shorter fractures were identified as drilling-induced fractures, which should not be incorporated in the modeling of natural fractures. The new results have improved the understanding of the regional fracture system, leading to a more accurate fracture network model.
The presented method provides new information on the extensiveness of subsurface fractures, which is an essential input for fractured reservoir models. Geologists and engineers can leverage the results and focus on the longer fractures for modeling and simulations, as they are more likely to form effective paths for hydrocarbon flow. The case study has also shown the method could be used as a powerful tool to separate natural fractures from drilling-induced fractures, which may be extremely difficult to differentiate in high-angle or horizontal wells.
New Horizontal Producer In Existing Waterflood: Mitigating Water Breakthrough
Authors: Bell Colton , Cheeseman Brenton , Gowisnock Brandon , Koseluk David 
Published: October 2020

Abstract
This case study reviews the new development program of Occidental's Yoakum County, Texas asset in the Permian Basin. The focus is on well planning and completion design, with the objective of improving sweep efficiency and mitigating water breakthrough in newly drilled and completed wells. This effort is supported primarily through the initial completion design utilizing re-closable sleeves along with the collection and interpretation of data, which allowed Occidental to make impactful adjustments with reasonably low risk and capital expenditure. Occidental's success in this unit is largely due to the initial planning and tailored completion design that supported initial stimulation, and future waterflood results.
Seeking to improve production in a waterflood field that had been producing for several decades, Occidental identified reservoir sections that appeared not to be swept effectively by the ongoing, originally designed, waterflood program. Focusing on a high-oil-potential target area, that had not been fully exploited, Occidental devised a program to improve sweep efficiency by converting two vertical producing wells to injectors, returning a temporarily abandoned vertical injector well to service, and drilling one new vertical injector well. Also planned were two new horizontal producer wells with the intent to include re-closable sleeves in both. The two new producer wells faced two challenges: 1. A water-bearing strata below the target zone which presented a risk of impaired oil production should induced fractures connect to the water zone; 2. Based on production history and known reservoir complexities in the target area, there was a high probability of future water breakthrough problems. This work brings lessons to the forefront that could add significant value when planning and executing new wells in existing waterfloods, including: Integration of geoscience and diagnostic methods to better understand the major influences on well performance, in particular, accurately identifying water breakthroughOptimizing completion design to eliminate water breakthrough and increase the profitability of a legacy waterflood field development plan.
Completion Design Improvement Using a Deep Convolutional Network
Authors: Li Liwei , Nasrabadi Nasser , Carr Timothy 
Published: October 2020

Abstract
Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.
Experimental Investigation of the Role of Different Clays in Low Salinity Waterflooding
Authors: Ivuawuogu Henry , Cheng Yaoze , Zhang Yin , Khataniar Santanu 
Published: October 2020

Abstract
Extensive studies have demonstrated that low salinity waterflooding (LSWF) can improve oil recovery effectively, and its typical recovery mechanisms have been proposed. However, there is stilla significant debate on the functions of clay duringLSWF. This study is to identify the influence of different clays, including montmorillonite, illite, and kaolinite, in LSWF performance and their mechanisms. The zeta potentials of sand, montmorillonite, illite, and kaolinite in the presence of high salinity water (HSW)and low salinity water (LSW) with salinities of27,501 mg/Land 2,485 mg/Lwere first examined, respectively. Then, the swelling factors of the three clay minerals in the HSW and LSW were measured in succession to determine their swelling characteristics. Subsequently, coreflooding experiments were conducted usingone clay-free sandpack column and fivesand pack columnsrespectively containing 10 wt% of montmorillonite, 10 wt% of illite, 10 wt% of kaolinite,compound clays (5wt% montmorillonite + 2.5wt% illite + 2.5wt% kaolinite), and 5wt% of montmorillonite. It has been found thatLSW could generate more negative zeta potential values for sand, montmorillonite, illite, and kaolinite, which made them more water-wet and thus favor oil recovery. Montmorillonite obviously swelled in HSW, and it could further swell significantly by contacting LSW. Although illite showed some swelling in HSW, LSW could not further expand it. Kaolinite did not swell in both HSW and LSW. The results from six coreflooding experiments showed that after high salinity waterflooding, the subsequent LSWF could generally further improve the oil recovery. The sand pack columnscontaining montmorillonite showed higherincremental oil recovery during LSWF (17.42% from sand pack column with 10wt% of montmorillonite, 10.27% from sand pack column with compound clay, and 8.90% from sand pack column with 5wt% of montmorillonite). Also, LSWF could improve oil recovery for the clay-free sand pack column, the sand pack column with kaolinite, and the sand pack column with illite by 0.73%, 0.83%, and 1.03%, respectively. Therefore, clay minerals would play an important role in determining oil recovery performance duringLSWF, and the more swelling the clay in LSW, the more favor the clay in LSWF. This study proved that both wettability alteration and clay swelling could attribute to the improved oil recovery byLSWF.
Leak Detection in Carbon Sequestration Projects Using Machine Learning Methods: Cranfield Site, Mississippi, USA
Authors: Sinha Saurabh , Pires De Lima Rafael , Lin Youzuo , Y. Sun Alexander , Symon Neill , Pawar Rajesh , Guthrie George 
Published: October 2020

SUMMARY
Due to international commitments on carbon capture and storage (CCS), an increase in CCS projects is expected in the near future. Saline aquifers and depleted hydrocarbon reservoirs with good seals and located in tectonically stable zones make an excellent storage formation option for geological carbon sequestration. However, stored carbon dioxide (CO2) takes a long time to convert into diagenetically stable form. Hence, ensuring the CO2 does not leak from these reservoirs in this time period is the key to any successful CCS project. Numerous methods are developed over the past couple of decades to identify the leaks which utilizes various types of geophysical, geochemical and engineering data. We demonstrate the automated leakage detection in CCS projects using pressure data obtained from Cranfield reservoir, Mississippi, USA. Our dataset consists of CO2 injection rates and pressure monitoring data obtained from a pressure pulse test. We first demonstrate the differences between the pressure pulse signal in case of a baseline pulse test and a pulse test with an artificially induced leak onsite. We then use machine learning techniques to automatically differentiate between the two tests. The results indicate that even simple deep learning architectures such as multi-layer feedforward network (MFNN) can identify a leak using pressure data and can be used to raise an early warning flag.
Generalizable Data-Driven Techniques for Microstructural Analysis of Shales
Authors: Ganguly Eliza , Misra Siddharth , Wu Yaokun 
Published: October 2020

Abstract
Microstructure of a material determines the transport, chemical and mechanical properties. Geological materials and geomaterials are imaged using microscopy tools. The microscopy images are analyzed to better understand the microstructural topology and morphology. Image segmentation is an essential step prior to the microstructural analysis. In this study, we trained a Random Forest classifier to relate certain features corresponding to each pixel and its neighboring pixels in a scanning electron microscopy (SEM) image of shale to a specific component type; thereby developing a methodology to segment SEM images of shale samples into 4 component types, namely, pore/crack, organic/kerogen, matrix and pyrite. We evaluate the generalization capability of the ML-assisted image-segmentation method by using SEM maps from Wolfcamp and Barnett shale formations. The two formations differ in topology, morphology and distribution of the four components. 16 features were computed and then implemented when building the classifier for the desired segmentation. The features belong to seven categories that define each pixel along with its neighboring pixels based on spatial information. The segmentation workflow was rigorously tested on the inner-region and outer-region pixels. The deployment of the workflow takes an average of 50 seconds to segment 1 image slice of size 2058-by-2606 pixels using an Intel (R) Xeon (R) CPU E5-1603 @ 2080GHz with 32GB RAM. We use F1 score and confusion matrix to present the performance of the segmentation models. Models trained on Wolfcamp-Shale SEM images can robustly detect matrix and pyrite in Barnett-Shale SEM images, but poorly perform for pore/crack and kerogen/organic components, where kerogen/organic pixels are wrongly segmented as pore/crack pixels. Models trained on Barnett-Shale SEM images can robustly detect pyrite in Wolfcamp-Shale SEM images, but poorly perform for the remaining components, where pore/crack pixels are wrongly segmented as kerogen/organic pixels or matrix pixels, and kerogen/organic pixels are wrongly segmented as matrix pixels. Model trained on Barnett shale cannot detect cracks in the Wolfcamp samples because the presence of cracks is very limited in the Barnett samples.