real time system

Normally, the optimization of hydraulic fracturing performance is limited to pre-job modeling and analytics. A design is determined for a particular well or project and applied without significant change during the course of the stimulation. Performance results are collected during the job and then analyzed after the fact, with the primary purpose of designing for the next project.

Significant design improvements can be made by evaluating stage performance in real-time as the well is being stimulated. Unfortunately, real-time analytics are difficult because the immense of volume, variety, and velocity of the available data. The typical frac fleet captures metered data from as many as one hundred measurement points simultaneously on a second-by-second basis. This means that for a single stage, the comma-separated values (CSV) files containing the recorded channels often include over one million discrete data points. Utilizing these large files (approximately 5 MB) with typical off-the-shelf software can be time-consuming. The manual process of file acquisition by analytical staff alone can often exceed the time available between stages. While these files are an invaluable resource, they are often left untouched until long after a job is completed, if they are ever used at all. Cloud-based analytics greatly shorten the acquisition and utilization timeline, making near real-time analysis possible.

While the challenges involved in utilizing "big data"; for actionable analytics are frequently discussed, the technology and approaches described in this paper are relatively new to the field of real-time stage management. This paper introduces a novel and highly effective approach in the field of hydraulic fracturing optimization. The history of CSV analysis is presented along with examples of specific types of beneficial stage analytics.

Directional drilling for hydrocarbon exploration has been challenged to become more cost-effective and consistent with fast-growing drilling operations for both offshore and onshore production areas. Autonomous directional drilling provides a solution to these challenges by providing repeatable drilling decisions for accurate well placement, improved borehole quality, and flexibility to adapt smoothly to new technologies for drilling tools and sensors. This work proposes a model predictive control (MPC)-based approach for trajectory tracking in autonomous drilling. Given a well plan, bottomhole assembly (BHA) configuration, and operational drilling parameters, the optimal control problem is formulated to determine steering commands (i.e., tool face and steering ratio) necessary to achieve drilling objectives while satisfying operational constraints. The proposed control method was recently tested and validated during multiple field trials in various drilling basins on two-and three-dimensional (2D and 3D) well plans for both rotary steerable systems (RSS) and mud motors. Multiple curve sections were drilled successfully with automated steering decisions, generating smooth wellbores and maintaining proximity with the given well plan.

A mathematical model is developed to capture the dynamic features in the wellbore during drilling operations so that it could be used for real-time computations. The model comprises one-dimensional (1D) mud flow solvers, one for the drillpipe and the other for the wellbore annulus including the volume below the drill bit, integrated point models for the bell nipple, bottomhole assembly (BHA) nozzles, 1D shallow water model for the flowline, and point model for the bypass replicating the hydraulic circuit in the drilling rig. The model assumes compressibility of mud for the wellbore section along with its transient gel characteristics. The equations are solved using appropriate explicit numerical solvers and the results capturing the fast transients of the standpipe pressure, bottomhole equivalent circulating density (ECD), and the flow rates during dynamic drilling operations are presented to illustrate the performance of the model with field data.

Pre-salt heterogeneous carbonate reservoirs typically present long net pays, high production/injection rates and some flow assurance risks. This paper presents general information, results and lessons learned regarding the installation of Intelligent Well Completion (IWC) in Santos Basin Pre-Salt Cluster (SBPSC) wells. It also presents some important improvements to be introduced in the future IWC systems specification and qualification based on the lessons learnt in these projects, setting some new challenges to the industry.

The benefits expected with the use of IWC are achieved at the expense of challenging well engineering, since well completion design becomes more complex and well construction risks increase. Detailed and integrated planning is essential for the success of the operations, starting at the earliest phases of the well design and continued through detailed execution plans. The use of standardized practices and procedures has led to significant increases on installation performance. On the other hand, an open mind and a constant search for improvements allowed new solutions and procedures to be developed throughout the years. Regarding the system integration, a flexible and standardized control architecture was developed to allow combining different IWC providers and subsea vendors, which proved to be a successful approach.

The most important improvement in IWC installation was the anticipation of the acid stimulation, nowadays performed before the vertical Wet Christmas Tree (WCT) installation. In order to achieve this goal some crucial improvements were gradually implemented in the stimulation practices, such as, an initial injectivity increase solution and some new acid diversion solutions, which allowed eliminating the use of coiled tubing and, as a consequence, the need of a subsea test tree. The well design team conducted an integrated risk assessment to properly evaluate the new practices and establish some actions to reduce the risks. Intense communication between production zones was observed during the acid job in some of the initial wells, ruining the gains of the IWC. After a comprehensive analysis, some possible causes were identified and with the new stimulation practices this issue was eliminated.

Over the years, with the introduction of several improvements, some of them presented in this paper, the well completion duration was reduced to less than 50% of the one observed in the initial wells. This major performance increase has been essential to keep this deepwater projects feasible, especially in the oil scenario seen in recent years. Some of the new practices and lessons learned in this 100 wells equipped with IWC has set groundbreaking practices for Brazilian pre-salt fields development and may stand as a reference for the industry in similar deepwater projects. Additional requirements for future systems are expected to improve even further the performance in this scenario.

In the last decade, technical advancements have greatly improved the design and execution efficiency of well completions, leading to improved recovery from unconventional reservoirs. However, analyzing fracture diagnostic tests in unconventional plays are still challenging due to high uncertainty in predictive capabilities in the context of fracture dynamics during treatment. The main objective of this study is to identify fracture behavior during injection and pressure fall-off periods in hydraulic fracturing treatments and diagnostic fracture injection tests (DFIT), respectively.

In this study, discrete wavelet transformation (DWT) was used to analyze real field injection and fall-off data in the wavelet domain. The analyzed data are from multi-stage hydraulic fracturing operations and DFIT in unconventional horizontal wells. DWT coefficients reveal very crucial information related to the nature of the events within recorded signals; they also reveal various patterns that are hard to recognize otherwise. The high-frequency components of the pressure and rate signals (detail coefficients) that are calculated by the wavelet transformation determine localization and separation of various events. We compared the identified events for injection and fall-off periods with moving reference point (MRP) and G-function analysis, respectively.

The main advantage of our proposed approach is that it is based on real-time data and does not require any assumptions related to existing or created fractures. Also, it is very sensitive to physical changes in the system; thus, it reveals hidden information related to those changes. Consequently, the energy of detail coefficients represents several events at different frequencies. We used pseudo-frequency of wavelet coefficients as a diagnostic tool for an accurate comparison of fracture propagation and fracture closure events to determine similarities and differences between them. For example, the signal energy of detail coefficients from the wavelet transformation of hydraulic fracturing data demonstrates abrupt frequency changes during dilation or fracture height growth during fracture propagation. Therefore, we were able to identify those events by energy density analysis in both time and pseudo-frequency domains in an objective manner, which otherwise was not possible with conventional methodologies such as G- function derivative analysis.

This paper details the successful methodology for effective implementation of a new fracture diagnostic technique for fracturing operations or DFITs in unconventional horizontal wells. This new fracture diagnostic method does not require any reservoir or fracture pre-assumptions; it mainly relies on the pressure behavior, which is a result of various events at different frequencies. Pressure fall-off behavior of a DFIT gives essential information related to closure event of the created mini-fracture. Identification of these events at different pseudo-frequency ranges improves the understanding of the dynamic fracture behavior also the characteristics of the reservoir. Unlike many other diagnostic techniques, this data-driven approach requires minimum input/data for analysis. This approach also lends itself to real-time application quite easily.

Development of unconventional reservoirs in North America has increased significantly over the past decade. The increased activity in this space has provided significant data with respect to through-tubing drillouts which had previously not been attainable. This paper is focused on using the field data from the Montney and Duvernay formations along with laboratory data and numerical modeling to understand the hole cleanout associated with through-tubing drillouts of frac plugs.

Initially, an extensive full-scale flow loop laboratory testing program was conducted to obtain data on debris transportation for hole cleanout during through-tubing applications. The testing was conducted on various coiled tubing (CT)-production tubing configurations using various solid particles. The laboratory data was used to develop empirical correlations needed for a transient debris transport model. This model was then used for frac plug drillouts to ensure successful hole cleaning in actual field applications. Computational fluid dynamics (CFD) modelling was also used to further understand and quantify the differences between the laboratory data, field data and transient debris transport model results.

The objective of the work conducted was to gain a better understanding of debris transport and validate the empirical modelling approach developed for hole cleaning. The validation process was conducted in several stages. The first stage was to validate the laboratory data against the Montney and Duvernay field data. The second stage was to verify the results obtained from the empirical model against the results obtained from a computational fluid dynamic model. The results from both modelling approaches were lastly compared to the field data. All these results challenge the current industry's understanding and best practices for through-tubing drillouts in the Montney and Duvernay formations. With the contentious increase of lateral lengths and higher stage counts, the process of drilling out frac plugs has become more complex. This study explicitly benefits all operators in their ever-increasing need to understand their frac plug drillout operations to ensure efficient, cost effective, and most importantly, consistent and repeatable results.

While efficient results for frac plug drillout operations have been accomplished to date, the on-going feedback from the field has been the requirement to produce repeatable drillouts. This paper is the first to show a holistic approach for obtaining a transient debris transport model used for through-tubing drillouts of frac plugs. The novelty also consists of the transient debris transport model validation through laboratory data and actual Montney and Duvernay field data.

By miniaturizing and ruggedizing equipment used for quantum paramagnetic spectroscopy, it is now possible to take a real-time chemical snapshot of molecules flowing through the wellhead or other surface fixtures. The digital time-series captures unique chemical properties of the fluid, such as the percentage of asphaltene in the oil, the oil-water ratio and gas-oil ratio. That data can be transmitted via industry-standard cloud protocols and be monitored from a global service center. 12 months of real-time data has been collected from operations around the world and the real-time monitoring has enabled prompt feedback for upgrades in both hardware and software. In a three-phase well configuration that had high rates of both water (over 90%) and gas (~1 MMSCf/day), this feedback drove some significant hardware modifications in order to optimize the consistency of asphaltene data.

The heart of the system is a microwave resonator that was designed to receive fluid at wellhead conditions with minimal reduction from wellhead pressure and temperature. The parameters of the resonator were optimized to maximize microwave intensity for typical oilfield fluids. A tailor-made set-up of fluid accumulator and control-valves upstream of the resonator ensured that the resonator could obtain samples that were mostly oil. By combining the resonator with a solenoid that created a large magnetic field across the oil, the resulting system provided spectroscopic data similar to that available in chemical laboratories but in a smaller package and one that tolerates some gas and conductive water in the oil. The combined quantum data is now provided continuously to the operator via a cloud or other communication architecture of operator choosing. It is anticipated that the resulting Internet of Things (IoT) system will make possible the optimization of chemical program and asphaltene remediation by incorporating system data with integrated flow assurance management. Qualification for offshore is ongoing with 5ksi pressure certification already achieved.

It was not obvious before installation, but once the 3-phase system was installed and the data transmitting in real-time, it became clear that software to automatically extract asphaltene information from spectral data needed to be able to cope with sudden and large changes in both asphaltene level and water-cut/gas-oil ratio which in turn required building an adaptive software model. Asphaltene percentage at one producing well was seen to vary from 0.3% to 3% in a single day. It was also discovered from the cloud-based monitoring that daily temperature variation introduced a phase variation in the shape of the sensor response. Correct derivation of spectral voltages was achieved through the combination of machine learning, model-based analysis and additional diagnostic data such as the quality factor of the resonator and its resonance frequency. As a consequence, the AI-based software could extract the not only the asphaltene percentage but the oil-water cut in the resonator and its gas-oil ratio.

For the first time, it is now possible to make a change in, say injected chemicals, look at the times-series data for the corresponding change in asphaltene and then adjust the chemicals accordingly. Such frequency of sampling (and volume of data) would be too much to handle with samples collected by hand. This device lays the platform for a multiplicity of chemical sensors to be connected to the cloud in real-time and in turn sets the stage to take the hardware offshore and eventually to subsea.

Reservoirs in the Barents Sea are several times shallower than in other parts of the NCS, essentially due to recent uplift and erosion of younger sediments. A proper understanding of their geomechanics is considered paramount for their successful development. In turn, the lack of any available analogue makes the proper in situ measurement of key parameters compulsory.

The paper describes the planning and execution of an appraisal well solely dedicated to the purpose of geomechanics data acquisition in the shallowest oil reservoir on the NCS – i.e. coring, logging, XLOT and injection testing. It focuses on the operations conducted in the oil reservoir itself, which included an entirely novel multi-cycle injection test aimed at estimating the large-scale thermal stress coefficient of the formations around the well – i.e. the impact of the injection temperature on the fracture pressure of the formations.

Every operation in the well was challenging due to the sea depth being about twice that of the overburden thickness and to the formations being quite consolidated, which was met by careful iterative multidisciplinary-planning. The equipment was often taken to its limit and sometimes extended beyond its standard use – e.g. the metering systems.

The injection test itself could not be performed traditionally – i.e. use of surface data and downhole memory gauge. Instead, the downhole gauge data were sampled, pumped out and transferred to a remote site where real time advanced analytics was used to ensure that safety criteria were always met throughout the operation in terms of vertical fracture propagation and lack of reservoir compartmentalisation. In addition, this allowed adjusting the planned injection schedule to the exact formation's response, which could not be fully quantified ahead of time.

All the targets of the appraisal well were met. The injection test – i.e. the shallowest on the NCS and perhaps worldwide in an offshore environment – was performed successfully. Its main results are considered essential for a possible future field development – e.g. the injectivity is confirmed and, in addition, a significant thermal effect is proven.

The series of novel technologies deployed in the extreme environment presented in the paper can easily and beneficially be extended to more traditional reservoirs. This concerns performing multi-cycle injection tests on appraisal wells on a systematic basis to prepare and optimise the development plan, real-time monitoring through advanced analytics and adjustment of these tests, start-up of injection wells during field development, monitoring and optimisation of water injection schemes, etc.

Today’s fast-paced development of petroleum resources depends on an efficient and accurate evaluation of both clastic and unconventional reservoirs. A new high-performance, slim logging-while-drilling (LWD) natural gamma ray spectroscopy tool has been developed to assist real-time petrophysical evaluations of net-to-gross for conventional reservoirs and to identify "sweet spots" for completion for unconventional reservoirs. Additionally, its azimuthal sensitivity can help position the well in lateral operations.

This new tool provides wireline quality formation thorium (Th), uranium (U), and potassium (K) elemental concentrations in real time that can quantify clay content, identify clay minerals, and estimate total organic content. Further, real-time processing provides a color display derived from a Briggs color cube rendition of relative elemental contributions that can be correlated with stratigraphic features in the field. This first-of-its-kind, real-time feature is output at a high sampling rate for the full azimuth of the borehole and should be a useful aid in geosteering applications where the goal is to maintain the borehole within a target formation or to follow a known stratigraphic feature.

Calibration and characterization of the tool were performed using newly developed Monte Carlo modeling techniques superior to previously used laboratory techniques while maintaining direct links to industry standards at the API Gamma Ray Calibration and K-U-Th Logging Calibration Facilities at the University of Houston. These techniques were developed because the borehole at the API Gamma Ray Calibration Facility cannot accept the 5.25-in. collar diameter, and the potassium formation at the API K-U-Th Logging Calibration Facility is not reliable. The instrument is fully characterized for operations in barite-, hematite-, or formate-weighted water-based mud systems as well as barite- or hematite-weighted oil-based muds. Further, corrections for borehole potassium can be applied in real time.

A novel mechanical design enables the sensor to operate at temperature up to 329°F and borehole pressure up to 25,000 psi while minimizing the attenuation of formation gamma rays entering the detector and maintaining good azimuthal sensitivity. The tool uses a robust, constrained, weighted least-squares (WLS) analysis to derive elemental concentrations from measured pulse-height gamma ray spectra. Proprietary spectral processing algorithms regulate the detector gain without the use of an additional radioactive reference source and compensate for variations of the detector’s energy resolution caused by operating conditions within the borehole that change over time. A general description of the tool together with its operational specifications, details of the computer models used to calibrate and characterize its responses, and example logs from early field trials are within this paper.

Successful in-situ fluid cleanup and sampling operations are commonly driven by a fast and reliable analysis of pressure, rate, and contamination measurements. Currently, techniques such as pressure transient analysis (PTA) and rate transient analysis (RTA) provide important information to quantify reservoir complexity, whereas fluid contamination measurements are overlooked for reservoir characterization purposes. The objective in this paper is to introduce a new interpretation technique to relate fluid contamination measurements with reservoir properties by identifying early- and late-time flow regimes in the derivative plots of reciprocal fluid contamination. Among several applications, this new transient analysis method is effective for improving logging-while-drilling (LWD) fluid sampling operations.

The derivative methods used in PTA and RTA inspired the development of the new fluid contamination interpretation method. Contamination transient analysis (CTA) evaluates transient measurements acquired during mud-filtrate invasion cleanup to infer reservoir geometry. We apply derivative methods to the reciprocal of the time evolution of fluid contamination to identify flow regimes in cases of water-based mud invading either water-or hydrocarbon-saturated formations. LWD operations are considered under a continuous invasion effect, i.e. the fluid cleanup procedure is performed while mud filtrate continues to invade the formation. This constraint brings about a significant technical challenge for LWD fluid sampling jobs. Alternatively, this new method could be integrated with other pressure transient techniques to improve the interpretation of measurements. For example, in a pretest case where the pressure transient does not achieve the radial flow regime, fluid cleanup could provide complementary information about late-time flow regimes to enhance the acquisition of measurements in real time.

We document synthetic and field examples of applications of a new interpretation method. Seven reservoir cases are simulated to obtain contamination data: (1) homogeneous isotropic reservoir, (2) formation thickness, (3) laminated formations, (4) geological faults, (5) mud-filtrate invasion (6) reservoir properties, and (7) permeability anisotropy. All these cases are compared for single-phase and multiphase flow during LWD fluid sampling operations. Additionally, field case studies are analyzed to highlight the value of the reciprocal contamination derivative (RCD) in real-time operations. Reservoir limits and features such as saturating fluid and depth of invasion are identified in the flow regimes detected with derivative plots of the reciprocal of the contamination. Consequently, LWD cleanup and sampling efficiency could be optimized based on contamination transient analysis by identifying the flow regimes taking place in the reservoir during filtrate cleanup, hence improving the prediction of the time required to acquire non-contaminated fluid samples.

The new approach of the reciprocal contamination derivative is an alternative way to optimize fluid cleanup efficiency and to quantify the spatial complexity of the reservoir during real-time LWD operations. In addition, this new technique enables the evaluation of reservoir properties in less operational time than PTA without the need of pressure build-up stages, increasing fluid sampling efficiency in terms of quality and time.