bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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

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

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

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

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.
Investigation of Vertical Fracture Complexity Induced Stress Interference in Multilayer Shale Gas Reservoirs with Complex Natural Fractures
Authors: Pei Yanli , Yu Wei , Sepehrnoori Kamy
Published: October 2020

The depletion of producing layers leads to significant stress changes in adjacent targets, especially when complex natural fractures are present. Due to weak bedding interfaces or small stress barriers, hydraulic fractures can easily penetrate neighboring layers, and this further increases the chance of multilayer stress disturbance. In this work, we investigate the effects of vertical fracture complexity, i.e., hydraulic fracture penetration and interlayer natural fracture existence, on stress interference between different layers using a data set from a typical shale gas well in the Sichuan Basin. Two geological contexts, i.e., without interlayer natural fractures (w/o INF) and with interlayer natural fractures (w/ INF), are considered under different degrees of fracture penetration and interlayer connectivity. An in-house iteratively coupled geomechanics and compositional reservoir simulator is used to model the three-dimensional pressure and stress changes. The non-uniform hydraulic fractures and stochastic natural fractures are incorporated in our coupled simulation with an embedded discrete fracture model (EDFM). Comprehensive spatial-temporal stress analysis quantifies the approximate range of orientation change of SHmax and magnitude change of Shmin under various reservoir conditions. Numerical results indicate that the presence of natural fractures in the interlayer upgrades the risk of stress interference between different pay zones. A larger hydraulic fracture penetration increases gas production, but also exerts a significant impact on stress reorientation and redistribution in the upper potential layer. The orientation change of SHmax along the prospective infill location is below 10 degrees in the w/o INF cases but up to 30 degrees in the w/ INF cases with a moderate number of interlayer natural fractures. The average magnitude change of Shmin is within 3.5 MPa along the prospective infill location for most w/o INF cases, whereas that in the w/ INF cases is above 10 MPa at most times. Moreover, the existence of natural fractures in the interlayer brings forward the occurrence of maximum orientation change in the upper layer by around one year. While inducing non-negligible stress drop in the upper layer, higher interlayer matrix permeability does not significantly reorient the horizontal principal stresses. Varying the density of interlayer natural fractures not only affects the stress magnitude but also causes considerable orientation change in the upper layer. The findings from this work help understand the extent of stress interference in the upper potential layer of the Sichuan Basin under different vertical fracture complexities. It is of guiding significance for future hydraulic fracture design and child-well operations in similar highly fractured tight formations.
Making Sense Out of a Complicated Parent/Child Well Dataset: A Bakken Case Study
Authors: Fowler Garrett , McClure Mark , Cipolla Craig
Published: October 2020

We use a high-quality dataset in the Bakken Shale to calibrate a numerical model to a complex and diverse set of parent/child observations. Two vertical wells (V1 and V2) were drilled 1000 ft and 1200 ft away from a legacy well with 10 years of production, H1. A DFIT was performed in the V1, followed by a 24 hour low-rate injection in the H1 (a microseismic depletion delineation, MDD, test). Subsequently, a small frac job was performed in the V1, followed by DFITs in the V1 and V2. The dataset yields a diversity of data to calibrate a numerical model: historical production of the H1, pressure response in the H1 from the MDD injection and the V1 fracture treatment, production rate uplift in the H1 following the V1 frac, microseismic, and pressure response during the three DFITs. The entire dataset was history matched in a single continuous simulation with a numerical simulator that fully integrates hydraulic fracture and reservoir simulation. The simulation was set up to closely match a geologic model that was built in prior work. The integrated simulation allows simulation of the fractures reopening around the H1 as a consequence of the MDD, the transport of proppant from the V1 to the H1 well, and the subsequent communication and poroelastic stress response. The Biot coefficient was calibrated to match the observed change in stress at the H1 well after ten years of depletion. The fracture toughness was calibrated to match the observed fracture geometry from the microseismic around the V1 well during fracturing. A proppant transport parameter called ‘maximum immobilized proppant’ was tuned to the production and DFIT data. The match to the V2 DFIT suggests that it is not directly in contact with the V1 fracture, even though the wells are relatively close together along fracture strike. The initial V1 DFIT suggests that it has, at most, weak contact with the H1. The second V1 DFIT, performed after the fracturing treatment, demonstrates communication with the H1, and consequently, depletion. The observations demonstrate that the H1 was able to produce from the previously undepleted rock around the V1, even though it was 1000 ft away. Overall, the results indicate that Bakken wells can achieve substantial (at least 1000 ft) effective half-length, that frac hits on parent wells in the Bakken do not necessarily result in production degradation and can even increase production, that the apparent Biot coefficient is relatively low (∼0.34), that the amount of proppant trapping due to localized screenout is relatively low (but nonzero), and this entire, complex dataset can be explained using a planar fracture modeling approach.
Multivariate Time Series Modelling Approach for Production Forecasting in Unconventional Resources
Authors: Alimohammadi Hamzeh , Rahmanifard Hamid , Chen Nancy
Published: October 2020

Evaluating the potential of the unconventional resources is a key for the development of this type of reservoirs. The currently adopted models for the well production forecast including decline curve analysis often fail to capture the complexity of flow performance by over-simplifying it and cannot produce reliable results due to the operational problems and most importantly the inadequate production history. In this study, a deep learning approach is developed to predict the long-term well performance based on a moderate duration of production data.

A data-driven procedure was implemented based on deep neural networks for flowrate predication using multivariate inputs. The production forecast was formulated as a time series regression problem where multiple inputs including tubing-head pressure and bottom-hole temperature are used as the input of a reverse model that estimates flow rate. Different recurrent neural networks (RNNs) including Long Short Term Memory, Gated Recurrent Units, and Bidirectional Recurrent Neural Networks were tested in this study to select the most time-efficient and accurate model of production forecasting.

The method presented in this paper provided a time efficient process which learned multi-domain sequence and was used to forecast production in unconventional resources. The developed deep learning networks did not require any feature handcrafting and could learn directly form the raw data. Reconstructed and predicted flowrates using deep learning was also used to estimate missing flowrate history. The study showed that deep neural networks have great capability to tolerate noise and optimize computation when multivariate input is used.

The technique can also be applied to other type of forecasting problems of prediction of pressure and rate in conventional reservoirs, prediction rate from temperature, and multi-well production forecasting.
Water Shutoff and Zonal Isolation for High Permeability Depleted Reservoir Using Organically Crosslinked Polymer Sealant System
Authors: Al-Azmi Nasser , Al-Sabea Salem , Abdullah Abu-Eida , Patra Milan , Khandelwal Nakul , Gaur Rishika , Chawla Sapna
Published: October 2020

Increasing water cut in oil-producing zones is a common issue, particularly in mature fields. Currently, most decisions are governed by economics, and incurring additional expenses, such as handling produced water, is undesirable. Depending upon the source of the water production, chemical isolation provides one effective solution to this issue.

This paper describes a cost-effective coiled tubing (CT) intervention to implement permanent zonal isolation for water shutoff using an organically crosslinked polymer (OCP) sealant system and a modified organically crosslinked polymer (m-OCP) sealant system to provide a controlled, shallow penetration solution to the problem in a high-permeability, low-pressure reservoir.

The traditional water shutoff method uses rig intervention for cement squeezes, which targeting shallow penetration can be time consuming and expensive in a high-permeability, low-pressure reservoir.

The OCP sealant system is an organically crosslinked polymer that is thermally activated to effectively seal the targeted interval. The m-OCP sealant system combines particulates with the OCP sealant system to provide leakoff control to help promote shallow matrix penetration.

The production logging tool (PLT) data for the candidate well indicated that the maximum water cut originated from the lower perforations and a single zone. CT intervention was selected to accurately place the OCP and m-OCP sealant systems and to permanently block water production by creating polymer barriers inside the reservoir and to remove any remaining OCP/m-OCP from the wellbore.

OCP and m-OCP are resistant to acid and H2S and provide the required radial penetration. This system provides a predictable and controlled set time (as shown by laboratory testing). Because this system does not develop compressive strength, a simplified cleanout with a jetting nozzle is required to wash it from the well.

After completing the zonal isolation with the OCP sealant system, the pressure test for the zone indicated a good seal. An electric submersible pump (ESP) was run on the completion string, and initial test results showed that the water cut was reduced from 97 to 75%, and oil production increased from 175 to 300 bpd. Increased production will recover all intervention and chemical costs within 20 days.

The polymer sealant system with the customized intervention solution successfully reduced the water cut for this west Kuwait field. The same approach can be applied to other similar fields worldwide.
Data Science Applied to Pedagogical Methodologies Focused on Changing the Negative Perception of the Oil and Gas Industry in Colombia
Authors: Guevara Israel , Ardila David , Daza Kevin , Ovalle Oscar , Pastor Paola , Ramirez Leyla
Published: October 2020

In Colombia, one of the biggest challenges for the oil and gas industry is to generate a sound reputation and a new perception about its benefits to the communities located in the area of influence. Even though, the oil and gas industry generates between 9 to 11% of the Gross Domestic Product (GDP) in Colombia, there is a negative perception of local communities due to the impacts of the operations of the industry on the people and environment; as a consequence, the implementation of pedagogical methodologies to educate communities about the sustainable view of the modern Oil and gas Industry, and the importance to the access to energy and water is a need.

In this sense, programs such as Energy4Me and PetrolCopa Reto Escolar have been implemented over the last two years in different regions of Colombia, which have allowed the collection of a considerable amount of data with great technical and social value from more than 10000 participants. This information has been refined, processed and analyzed to show how data science generates added value in the continuous improvement of the methodologies implemented by the data center of SPE Colombian Section; thus, improving relationship between industry and local communities through a fluent communication and knowledge channel.

The analysis of the information and its implementation in these educational programs was carried out through the application of different statistical methods and digital tools for processing and monitoring information, which resulted in the design of optimal processes for the collection of structured and unstructured data such as; community behavior parameters, social problems, demographic characteristics and acquired technical knowledge. This process took into account at the same time other related variables such as the heterogeneity of rural communities, logistical and operational challenges and the social conditions of each region. The data gathering process involved 500 volunteers in more than 5000 people from different regions, ethnic groups, ages, gender and level of education. Once the information was correlated and the methodological approach of the programs improved, trends and direct relationships were established between the data analyzed and the decrease in the rate of rejection by the communities towards Oil and Gas industry in the study regions.
Smart Custom Well Design Based On Automated Offset Well Analysis
Authors: Selveindran Anand , Wesley Avinash , Chaudhari Nitish , Pirela Helmut
Published: October 2020

Offset well analysis is the process of investigating and integrating historical drilling performance from neighboring wells into prospect well design. Traditional offset analysis is a time and resource intensive process that requires a lot of manual input and analysis to make various design decisions. Our proposed workflow automates much of this analysis, while allowing the user to customize designs based on operating metrics resulting in a quicker and more comprehensive offset well analysis that provides the user with intelligent offset recommendations and a base design for the prospect well. Historical data is queried from a structured engineering and operation database comprising of data ranging from subsurface geology, drilling to production and end-of-well. We gather this historical data from nearby wells and learn from those in order to produce a first pass design for the prospect well to begin with. With this data, our proposed workflow implements algorithms to identify representative offset wells with similar geology, trajectory and other characteristics. A similarity analysis based on various geological sequences across the wells is conducted by a deep neural network algorithm, which is trained to analyze sequential patterns within. Trajectory similarity is performed using well surveys (dogleg severity, inclination, and azimuth). A recurrent neural network is employed to learn the well survey patterns and classify wells with similar trajectories. Finally, drilling performance metrics are used to rank the offsets and aid selection of the best offset design to be used as a base template for the prospect well. The proposed workflow significantly reduces analysis time; the software analysis time is less than five minutes. The user can almost instantaneously query the database and obtain wells that are similar in terms of trajectory and geology, and rank those wells on certain pre-defined metrics. The engineer is also able to view key events and hazards history for the offset wells. This provides the engineer with a compilation of hazards and risks by depth and cause, allowing the engineer to focus on mitigating risks and designing better and safer wells. The engineer can accept the top ranked offset and automatically select casing design based on default metrics of cost, time, and NPT, or can implement other metrics and create a composite casing design from different hole-sections from different offsets.
Fracture Wing Growth Study Using Synchronized Surface Pressure Data from Monitored Offsetting Wells
Authors: Rainbolt Michael , Cooper Drew , Timmons Taylor
Published: October 2020

Extensive literature has been published concerning Fracture Driven Interactions (FDIs). Many of these works describe FDI anatomy, physics and impact on existing wells including the success or failure of various mitigation techniques. In this paper, time synchronized FDI surface pressure data from existing offsetting wells is used to study fracture wing growth. This includes timing, fracture half-length estimation and observing the movement of injected fluid sequentially from well-to-well during fracturing operations.

In 2019, an in-fill Eagle Ford well development consisting of two newly drilled wells and seven offsetting existing wells was performed. The new wells were located such that each treatment well was bounded on both sides by existing wells. Further, there were first order wells (closest well to treatment well) and second order wells (next well over from the first order well) bounding one of the treatment wells and FDIs generated pressure communication were seen in both the first order well and the second order well. Six of the existing wells were preloaded. This was done to test the efficacy of preloading to protect the existing wells from FDI damage.

The workflow was organized into first deriving detailed data from all FDIs that occurred in individually monitored wells. This was followed by examining well pairs bounding the two treatment wells to study fracture wing development in each stage. Lastly, first order and second order wells that had sequential FDIs from the treatment wells were examined to study fluid volumes and timing between wells as well as FDI magnitude dampening in second order wells. All monitored wells were left shut-in during the completion operations. The wireless surface pressure monitoring sensors were time-synchronized to internet time and the data was viewed in real-time.

The authors’ found that preloading dampened FDIs but did not completely stop them. The degree of the success of preloading was graded by determining a reduction of recovery time (if any) and how well production rates were protected. At the time of this writing, flowback operations are ongoing and one primary well recovered to pre-frac rates within one week. We anticipate a corresponding reduction of recovery time in the other wells.

This paper presents methods and processes that offer a solution to identify candidate stages for FDI mitigation and potential optimization of project economics. The time synchronization with internet time eliminated all uncertainty with regards to timing. As will be shown, for this type of study, there cannot be uncertainty with timing. No inconsistencies in timing were found. It was also determined that FDI data must be viewable in real-time for it to be used to make on-the-fly mitigation decisions. In this project, a "passive well defense" technique was utilized; preload but take no other actions.
Effect of Surface Tension on Sweeping Performance of Injected Gas During Intermittent Gas Lift in Liquid Loaded Horizontal Wells
Authors: Croce Daniel D. , Zerpa Luis E.
Published: October 2020

As the reservoir pressure drops in the vicinities of horizontal gas wells drilled in tight reservoirs, the produced gas loses its ability to drag liquids to surface. This results in the accumulation of liquid in the bottom of the well. Since conventional artificial lift methods fail to remove the stagnant liquid in an efficient and reliable manner, the accumulated liquid limits the well production down to unprofitable levels. As an alternative solution to this problem, it is proposed a method called Backsweep, which relies on the use of intermittent gas injection in the horizontal section of the well. Results obtained in an experimental flow loop, using water and compressed air, show an improvement on the liquid removal efficiency (i.e., produced liquid volume / stagnant liquid volume) at Eotvos (Eo) numbers around 12, obtained by reducing the internal diameter (ID) of the flowlines, but with increased frictional pressure drops per unit length. This work focuses on the effect of changing the surface tension and viscosity of the stagnant liquid, to optimize the liquid removal efficiency, avoiding increases in frictional pressure drop from reducing the ID of the line. Sucrose, xanthan gum and sodium chloride at different concentrations, were used for this purpose. Xanthan gum at a concentration of 0.05% w/w delivered the best results of the three substances, with an increase of over 15% (up to 77% in average) when compared to only using water, as the Eo number was reduced in 5% and the viscosity number (Nvis) was increased in over 70 times. The improvement on the liquid removal efficiency of the device at lower Eo is associated to a shaper nose of the Taylor bubble and larger convergence angle of the flow lines in the liquid film next to the wall.
Unconventional Measurements Bootstrap Reservoir Simulation
Authors: VonGonten W. D. , Woods Terry , Yang Yi-Kun , Picha Tim , Lindsay Garrett , Ali Safdar
Published: October 2020

Production history matching data is an important step in any study that seeks to optimize unconventional completions and well development criteria. Understanding the reservoir mechanisms during production allows for better optimization of the hydraulic fracture system. Generating a model that fits historical data can be easy but honoring the true petrophysics and fluid dynamics of the reservoir is often challenging. Some of the major challenges during reservoir simulation are uncertainties in water saturation, permeability, phase behavior, and effective fracture surface area during production. This paper discusses how fit-for-purpose core measurements help reduce the uncertainty in these parameters, ultimately requiring a multiple porosity reservoir simulation model to account for these improved measurements and understandings.

Industry accepted core analysis techniques under-estimate reservoir water saturation due to loss of water from evaporation and core handling techniques (preservation, crushing, time). Proper evaluation of the void space will be shown and how this is better calibrated to field data. A review of how steady-state liquid permeability testing provides better estimates for reservoir permeability and deliverability in shale reservoirs will be discussed. Coupling these measurements with imbibition effects from hydraulic fracturing fluids and lab studies showing oil-wet and water-wet pore systems acting independently of each other, a slightly "outside of the box" reservoir simulation model was needed to mimic these physics.

The proposed reservoir simulation methodology consists of multiple porosities and was developed to incorporate near-wellbore hydraulic fracture effects that are observed during lab testing. Combining this methodology with other lab measurements and a fully three-dimensional (3D) hydraulic fracture model, the number of "knobs" that need to be turned to get a good history match are reduced.

Two examples will be presented in this paper showing how the proposed model better honors the physics of lab measurements and provides the user more flexibility during reservoir simulation, especially when buildup data is available. Reducing the uncertainty in these parameters has provided a workflow that helps minimize the multiple non-unique realizations during the history match process and provides a more reliable model for the engineer while reducing the amount of time needed to obtain a match.
Validating BHA Analysis Models with Real-Time Measurements for Improved Drilling Performance
Authors: Smith Mark , Broglie, P.E. Ryan , Gandikota Raju , Frenzel Mark
Published: October 2020

A West Texas operator historically used four to seven PDC bits to TD their Ward County, Wolfcamp B laterals. Formations there are lithologically more challenging when compared to the same interval in adjacent Reeves County. Drilling performance in Reeves County Wolfcamp A laterals averages approximately 1,091 Feet/Day while those in Ward County seldom make 600 Feet/Day. Often daily progress is less than that. The objective was to improve Ward County lateral drilling performance.

In the study presented here, we examined the BHAs, bits and motors in consecutive Wolfcamp B laterals on the same pad which used 7 and 5 bits to reach TD respectively. Several of these PDC bits were Damaged Beyond Repair (DBR) as illustrated later. We demonstrate the process of utilizing Time Domain Analyses (TDA) as an accurate method to identify and analyze the nonlinear, dynamic behavior of drilling systems. These enhanced models allow the engineer to explore a full range of alternative BHA responses based upon current drilling conditions. They account for most of the specific details of downhole equipment such as the bit, the rotary steerable systems (RSS) or steerable mud motor, the MWD and their interaction with the hole. The software used allows virtual sensors to be placed anywhere along the drill string to evaluate the dynamics for additional insights.