Is High Viscosity a Requirement for Fracturing Fluids?

Uniformly distributing proppant inside fractures with low damage on fracture conductivity is the most important index of successful fracturing fluids. However, due to very low proppant suspension capacity of slickwater and friction reducers fracturing fluids and longer fracture closure time in nano & pico darcies formations, proppants settles quickly and accumulates near wellbore resulting in worse-than-expected well performance, as the fracture full capacity is not open and contributing to production. Traditionally, cross-linked polymer fluid systems are capable to suspend and transport high loading of proppants into a hydraulically generated fracture. Nevertheless, amount of unbroken cross-linked polymers is usually left in fractures causing damage to fracture proppant conductivity, depending on polymer loading. To mitigate these challenges, a low viscosity-engineered-fluid with excellent proppantcarrying capacity and suspension-in excess of 30 hours at static formation temperature conditions - has been designed, enhancing proppant placement and distribution within developed fractures, with a 98% plus retained conductivity. In this work experimental and numerical tests are presented together with the path followed in developing a network of packed structures from polymer associations providing low viscosity and maximum proppant suspension. Challenges encountered during field injection with friction are discussed together with the problem understanding characterized via extensive friction loop tests. Suspension tests performed with up to 8-10 PPA of proppant concentration at temperature conditions are shared, together with slot tests performed. Physics-based model results from a 3D Discrete Fracture Network simulator that computes viscosity, and elastic parameters based on shear rate, allows to estimate pressure losses along the flow path from surface lines, tubular goods, perforations, and fracture. This work will demonstrate the advanced capabilities and performance of the engineered fluid over conventional fracturing fluids and its benefits. Additionally, this paper will present field injection pressure analysis performed during the development of this fluid, together with a field case including production results after 8 months of treatment. The field case production decline observed after fracture treatment demonstrates the value of this system in sustaining well production and adding additional reserves.

A Play-Wide Performance Review of Plug and Perf, Ball and Seat, and Pinpoint Fracturing Methods in the Montney Tight Siltstone Reservoir

The Montney reservoir is one of the most prolific unconventional multi-stacked dry and liquid-rich gas plays in North America. The type of fracturing method and fluid has a significant impact on water-phase trapping, casing deformation, and well performance in the Montney. Different fracturing methods (plug and perf/plug and perf with ball/ball and seat/single-entry pinpoint) and fluids (slickwater/hybrid/oil-based/energized/foam) have been tested in 4000+ Montney wells to find optimal fracturing method and fluid for different reservoir qualities and fluid windows and to minimize water-phase trapping and casing deformation. The previous studies reviewing the performance of fracturing methods in Montney do not represent a holistic evaluation of these methods, due to some limitations, including: (1) Using a small sample size, (2) Having a limited scope by focusing on a specific aspect of fracturing (method/fluid), (3) Relying on data analytics approaches that offer limited subsurface insight, and (4) Generating misleading results (e.g., on optimum fracturing method/fluid) through using disparate data that are unstructured and untrustworthy due to significant regional variation in true vertical depth (TVD), geological properties, fluid windows, completed lateral length, fracturing method/fluid/date, and drawdown rate management strategy. The present study eliminates these limitations by rigorously clustering the 4000+ Montney wells based on the TVD, geological properties, fluid window, completed lateral length, fracturing method/fluid/date, and drawdown strategy. This clustering technique allows for isolating the effect of each fracturing method by comparing each well's production (normalized by proppant tonnage, fluid volume, and completed length) to that of its offsets that use different fracturing methods but possess similar geology and fluid window. With similar TVD and fracturing fluid/date, wells completed with pinpoint fracturing outperform their offsets completed with ball and seat and plug and perf fracturing. However, wells completed with ball and seat and plug and perf methods that outperform their offset pinpoint wells have either: (1) Been fractured 1 to 4 years earlier than pinpoint wells and/or (2) Used energized oil-based fluid, hybrid fluid, and energized slickwater versus slickwater used in pinpoint offsets, suggesting that the water-phase trapping is more severe in these pinpoint wells due to the use of slickwater. Previous studies often favored one specific fracturing method or fluid without highlighting these complex interplays between the type of fracturing method/fluid, completion date (regional depletion), and the reservoir properties and hydrodynamics. This clustering technique shows how proper data structuring in disparate datasets containing thousands of wells with significant variations in geological properties, fluid windows, fracturing method/fluid, regional depletion, and drawdown strategy permits a consistent well performance comparison across a play by isolating the impact of any given parameter (e.g., fracturing methods, depletion) that is deemed more crucial to fracturing design and field development planning.

Integrated Hydraulic Fracturing and Well-Test Data Analytics using R

1. Abstract Various datasets are generated during hydraulic fracturing, flowback- and well-testing operations, which require consistent integration to lead to high-quality well performance interpretations. An automated digital workflow has been created to integrate and analyze the data in a consistent manner using the open-source programming language R. This paper describes the workflow, and it explains how it automatically generates well performance models and how it analyzes raw diagnostic fracture injection test (DFIT) data using numerical algorithms and Machine Learning. This workflow is successfully applied in a concession area located in the center of the Sultanate of Oman, where to date a total of 25+ tight gas wells are drilled, hydraulically fractured and well-tested. It resulted in an automated and standardized way of working, which enabled identifying trends leading to improved hydraulic fracturing and well-testing practices.

Track Fractured Well Inflow Performance Using Historic Production Logging and Surface Well Performance Data in Sultanate of Oman

PDO operates about 200 deep gas wells in the X field in the Sultanate of Oman, producing commingled from the Barik gas-condensate and Miqrat lean gas reservoir completed by multiple hydraulic fracturing. Their inflow performance relation (IPR) is tracked to diagnose condensate damage, hydraulic fracture cleanup and differential reservoir pressure depletion. The best IPR data is collected through multi-rate production logging but surface production data serves as an alternative. This paper describes the process of deriving IPR's from production logging and surface production data, and then evaluates 20 years of historic IPR data to quantify the impact of condensate damage and condensate cleanup with progressive reservoir pressure depletion, to demonstrate the massive damage and slow cleanup of hydraulic fractures placed in depleted reservoirs, to show how hydraulic fractures facilitate the vertical cross-flow between isolated reservoir intervals, and to highlight that stress-dependent permeability does not play a major role in this field.

From Technical Evolution to Production Performance: A Technical Review of 700 Wells Over 8 Years in the South Sulige Tight Gas Field

Unlike many unconventional resources that demonstrate a high level of heterogeneity, conventional tight gas formations often perform consistently according to reservoir quality and the applied completion technology. Technical review over a long period may reveal the proper correlation between reservoir quality, completion technology, and well performance. For many parts of the world where conventional tight gas resources still dominate, the learnings from a review can be adapted to improve the performance of reservoirs with similar features. South Sulige Operating Company (SSOC), a joint venture between PetroChina and Total, has been operating in the Ordos basin for tight gas since 2011. The reservoir is known to have low porosity, low permeability, and low reservoir pressure, and requires multistage completion and fracturing to achieve economic production. Over the last 8 years, there has been a clear technical evolution in South Sulige field, as a better understanding of the reservoir, improvement of the completion deployment, optimized fracturing design, and upgraded flowback strategy have led to the continuous improvement of results in this field. Pad drilling of deviated boreholes, multistage completions with sliding sleeve systems, hybrid gel-fracturing, and immediate flowback practices, gradually proved to be the most effective way to deliver the reservoir's potential. Using the absolute open-flow (AOF) during testing phase for comparative assessment from South Sulige field, we can see that in 2012 this number was 126 thousand std m3/d in 2012, and by 2018 this number had increased to 304 thousand std m3/d, representing a 143% incremental increase. Thus, technical evolution has been proved to bring production improvement over time. Currently, South Sulige field not only outperforms offset blocks but also remains the top performer among the fields in the Ordos basin. The drilling and completion practices from SSOC may be well suited to similar reservoirs and fields in the future.

Impact of Delayed Flowback on Well Performance: Case Study

Due to a resource optimization and efficiency improvements, wells that are hydraulically fractured in the tight gas Barik Formation of the Khazzan Field in the Sultanate of Oman are often temporarily left shut-in directly following a large scale massive hydraulic fracturing stimulation treatment. Extensive industry literature has often suggested (and reported), that this may result in a significant direct loss of productivity due to the delayed flowback and the resulting fracture conductivity and formation damage. This paper will review the available data from the Khazzan Field address these concerns; indicating where the concerns should and should not necessarily apply. The Barik Formation in the Khazzan Field is an over-pressured gas-condensate reservoir at 4,500 m with gas permeability ranging from 0.1 to 20 mD. The average well after hydraulic fracturing produces 25 MMscfd and 500 bcpd against a wellhead pressure of 4,000 psi. A typical hydraulic fracturing stimulation treatment consists of 14,000 bbl of a borate-crosslinked guar fluid, placing upwards of 1MM Lbs of high conductivity bauxite proppant within a single fracture. In order to assess the potential production loss due to delayed flowback operations, BP Oman performed a suite of formation damage tests including core samples from the Barik reservoir, fracture conductivity considerations and dynamic behaviors. Additionally, normalized production was compared between offset wells that were cleaned-up and put onto production at different times after the hydraulic fracturing operations. Core tests showed a range of fracture conductivities over time with delayed flowback after using the breaker concentrations from actual treatments. As expected, enhanced conductivity was achieved with additional breaker. The magnitude of the conductivity being created in these massive treatments was also demonstrated to be dominant with respect to damage effects. Finally, a normalized comparison of an extensive suite of wells clearly showed no discernible loss of production resulted from any delay in the flowback operations. This paper describes in details the workflow and resulting analysis of the impact of extensive shut-in versus immediate flowback post massive hydraulic fracturing. It indicates that the impact of such events will be limited if the appropriate steps have been taken to minimize the opportunity for damage to occur. Whereas the existing fracturing literature takes the safe stance of indicating that damage will always result from such shut-ins, this paper will demonstrate the limitations of such assumptions and the flexibility that can be demonstrated with real data.

Pumping test analysis for assessment of hydraulic parameters and aquifer system formation in hilly terrain

Groundwater conditions (GWCs) of an area depends on aquifer hydraulic parameters such as storativity () or storage coefficient (), transmissivity () and hydraulic conductivity (). It plays a key role concerning- groundwater flow modeling, well performance, solute and contaminants transports assessment and also for identification of areas for additional hydrologic testing. Specifically, the geologic formation of a regions control the porosity and permeability, however, in hilly terrain prospecting ground water potential is more challenging due to its limited extent and its occurrences that are usually confined to fractures and weathered rocks. The present study, aims at estimating the hydraulic parameters through pumping test analysis to assess aquifer system formation on hilly terrain from 16 bore wells. The aforesaid parameters were examined through a case study in some selective regions of Hamirpur district of Himachal Pradesh, India. The study area is controlled under two main geological horizons that is the post-tertiary and tertiary. The papers end with comparative results of hydraulic parameters and the aquifers system formation on different GWCs which may be helpful in the outlook of sustainable groundwater resource in the regions.

Stratigraphic Characterization of the Eagle Ford Shale to Identify the Best Landing Zone: A Semi-analytical Workflow

Placement of the horizontal well within the best landing zone is critical to maximize well productivity, thus identification of the best landing zone is important. This paper illustrates an integrated semi-analytical workflow to carry out the stratigraphic characterization of the Eagle Ford shale to identify the best landing zone. The objective of this work is twofold: 1) to establish a workflow for stratigraphic characterization and 2) to understand the local level variability in the well performance.To establish the workflow, we have used the production data, petrophysical information and regional reservoir property maps. As a first step of the workflow, we subdivided the Eagle Ford shale into nine smaller stratigraphic units using the wireline signatures and outcrop study. In the second step, we have used statistical methods such as linear regression, fuzzy groups and theory of granularity to capture the relationship between the geological parameters and the well performances. In this step, we identified volume of clay (Vclay), hydrocarbon filled porosity (HCFP) and total organic carbon (TOC) as key drivers of the well performance. In the third step, we characterized the nine smaller units and identified four stratigraphic units as good reservoirs with two being the best due to their low Vclay, high HCFP and high TOC content.Finally, we reviewed the well paths of four horizontal wells with respect to the best stratigraphic units. We observed that production behavior of these wells is possibly driven by their lateral placement. The better producing wells are placed within the middle of the best stratigraphic units whereas the poor wells are going out the best stratigraphic units. This investigation provides a case study that demonstrates the importance of integrating datasets to identify best landing zones and the suggested workflow can be applied to other areas and reservoirs to better identify targetable zones.

Performance of a Genetic Algorithm for Estimating DeGroot Opinion Diffusion Model Parameters for Health Behavior Interventions

Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM).

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.