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An intelligent drilling optimization application performs as an adaptive autodriller. In the Marcellus Shale, ROP improved 61% and 39% and drilling performance, measured as hours on bottom, improved 25%. With their gee-whiz—albeit artificial—intelligence, robots may be the industry’s answer to jobs deemed dangerous, dirty, distant, or dull. A test showing that it’s possible to automate the billing process for produced water hauling has opened the door for tracking a wide range of field activities. The industry downturn brought on by COVID-19 has motivated big companies to test practical applications sooner.
Hydrogen is big news, and not for the first time. But this time could be different. This article looks at the current state of enabling technology, obstacles to a hydrogen economy, and signs that the hydrogen economy could be emerging. Often it is too difficult to create the fault conditions necessary for training a predictive maintenance algorithm on the actual machine. A digital twin generates simulated failure data which can then be used to design a fault-detection algorithm. Canadian Oil Sands Output Cut 1 Million B/D: What Could Go Wrong? Oil sands producers predicted they could reduce production by 300,000 B/D by turning down steam injection. This will test methods to reduce, rather than stop, injection to avoid the damage caused by rapid cooling in some wells.
Improvements in drilling and completion technology have resulted in significant increases in production rates from new horizontal wells in the United States. Observations over many years show that a well’s initial rate often has a predictable relationship to decline trends indicating a rate related bias in decline trends. This paper studies the relationship between initial flow rates and related decline trends and reserves forecasts for many of the major horizontal development plays in the United States and confirms that there is a rate related bias. Early decline trend forecast methods considering rate bias lead to improved reserve estimates and fewer revisions to estimates as wells mature. The study does not provide a methodology for determining peak rates but focuses on bias in decline trends related to known peak rates.
For years, reviews of oil and gas reserves estimates have shown that downward revisions on high rate wells (Lee 2017, Lee 2019) have been more common than upward revisions indicating a rate related bias. Various authors have discussed methods to correct various biases (SPEE 2010, 2016) (Freeborn 2012, 2013) which tend to result in overestimating production forecasts. Our experience is that forecast bias to the high side is more significant for high rate wells than for low rate wells. This rate Figure 1. Twelve areas for which peak month rate to decline trends relationships are presented. dependency raises the question of whether a rate dependent bias can be documented and corrected. This paper focuses on one factor: the relationship between peak month rates and decline trends or rate dependent decline trend bias. Results are presented for twelve areas noted in Figure 1 with an attempt to minimize rate dependent estimation bias.
High rate bias was documented with production data from a group of similar wells which was sorted from high peak rate to low peak rate and binned for analysis. This ranking minimizes time sequence bias which results when the best wells are drilled first with poorer wells later or if the better wells are drilled toward the end of a study period. The peak rate ranked wells are divided into approximately equal bins with each bin analyzed to determine the expected decline trends for the bin. The decline trends of the bins are then compared to the bin’s peak month rate to document the relationship between decline trends and peak rates.
Hill, A. D. (Texas A&M University) | Laprea-Bigott, M. (Texas A&M University) | Zhu, D. (Texas A&M University) | Moridis, G. (Texas A&M University) | Schechter, D. S. (Texas A&M University) | Datta-Gupta, A. (Texas A&M University) | Abedi, S. (Texas A&M University) | Correa, J. (Lawrence Berkeley National Laboratory) | Birkholzer, J. (Lawrence Berkeley National Laboratory) | Friefeld, B. M. (Class VI Solutions, Inc.) | Zoback, M. D. (Stanford University) | Rasouli, F. (Stanford University) | Cheng, F. (Rice University) | Ajo-Franklin, J. (Rice University / Lawrence Berkeley National Laboratory) | Renk, J. (Department of Energy) | Ogunsola, O. (Department of Energy) | Selvan, K. (INPEX Eagle Ford LLC)
The Eagle Ford Shale Laboratory is a DOE and industry-sponsored multi-disciplinary field experiment aimed at applying advanced diagnostic methods to map hydraulic fractures, proppant distribution, and the stimulated reservoir volume. The field site is an Inpex Eagle Ford, LLC lease in LaSalle county, Texas that has a legacy Eagle Ford producing well and that will be developed with 5 new producers. Utilizing newly-developed monitoring technologies, the project team will deliver unprecedented comprehensive high-quality field data to improve scientific knowledge of three important processes in unconventional oil production from shales: (1) a re-fracturing treatment in which the previously fractured legacy well will be re-stimulated for improved production, (2) a new stimulation stage where the most advanced hydraulic fracturing and geosteering technology will be applied during zipper-fracturing of 3 new producers, and (3) a Gas-Injection Enhanced Oil Recovery (EOR) Phase where one of the wells will be later tested for the efficiency of Huff and Puff gas injection as an EOR method. Field monitoring is being complemented with laboratory testing on cores and drill cuttings, and coupled modeling for design, prediction, calibration, optimization, and code validation. The multi-disciplinary team consists of researchers from Texas A&M University, Lawrence Berkeley National Laboratory, Stanford University, Rice University, and Inpex Eagle Ford, LLC.
The ultimate objective of the Eagle Ford Shale Laboratory Project is to help improve the effectiveness of shale oil production by providing new scientific knowledge and new monitoring technology for both initial stimulation/production as well as enhanced recovery via re-fracturing and EOR. The main scientific/technical objectives of the project are:
Build and test active seismic monitoring with fiber optics in an observation well to conduct: (1) real-time monitoring of fracture propagation and stimulated volume, and (2) 4D seismic monitoring of reservoir changes during initial production and during an EOR pilot.
Test distributed temperature sensing (DTS), distributed acoustic sensing (DAS) and distributed strain sensing (DSS) with fiber optic technology and develop protocols for field application.
Assess spatially and temporally resolved production characteristics and explore relationships with stimulated fracture characteristics by open hole logging, cased hole logging, production logging, and tracer technology.
Understand rock mechanical properties and reservoir fluid properties and their effect of stimulation efficiency through coring and core analysis.
Evaluate suitability of re-fracturing to achieve dramatic improvements in stimulated volume and per well resource recovery.
Develop understanding of gas-based EOR Huff and Puff methods to increase per well resource recovery by lab tests and field test.
To thrive in economically challenging times operators must push boundaries in completion design and optimization. The industry has responded with numerous advances in completion strategies; however, it is critical that the efficacy of any design change is validated diagnostically. This paper presents the results of a nine well systematic study designed to evaluate the most economical approach toward achieving a maximum number of effective producing clusters (EPCs) along a lateral. A comprehensive understanding of the performance differences of each of the completion design experiments was achieved using intervention based distributed fiber optic (DFO) measurements. The paper demonstrates how the integration of acoustic and temperature recordings can be used to quantify completion efficiency.
Decline curve analysis has been the mainstay in unconventional reservoir evaluation. Due to the extremely low matrix permeability, each well is evaluated economically for ultimate recovery as if it were its own reservoir. Classification and normalization of well potential is difficult due to ever changing stimulation practices. The standard methodology for conducting decline curves gives us parameters associated with total contact area and a hyperbolic curve fit parameter that is disconnected from any traditional reservoir characterization descriptor. A new discrete fracture model approach allows direct modelling of inflow performance in terms of fracture geometry, drainage volume shape, and matrix permeability. Running such a model with variable geometrical input to match data in lieu of standard regression techniques allows extraction of a meaningful parameter set for reservoir characterization.
Since the entirety of unconventional well operation is in transient mode, the discrete well solution to the diffusivity equation is used to model temporal well performance. The analytical solution to the diffusivity equation for a line source or a 2D fracture operating under constrained bottomhole pressure consists of a sum of terms each with exponential damping with time. Each of these terms has a relationship with the constant rate, semi-steady state solution for inflow, although the well is neither operated with constant rate, nor will this flow regime ever be realized.
The new model is compared with known literature models, and sensitivity analyses are presented for variable geometry to illustrate the depiction of different time regimes naturally falling out of the unified diffusivity equation solution for discrete fractures. We demonstrate that apparent hyperbolic character transitioning to exponential decline can be modeled directly with this new methodology without the need to define any crossover point.
Each exponential term in the model is related to the various possible interferences that may develop, each occurring at a different time, thus yielding geometrical information about the drainage pattern or development of fracture interference within the context of ultralow matrix permeability. Prior results analyzed by traditional decline curve analysis can be reinterpreted with this model to yield an alternate set of descriptors. The approach can be used to characterize the efficacy of evolving stimulation practices in terms of geometry within the same field, and thus contribute to the current type curve analyses subject to binning. It enables the possibility of intermixing of vertical and horizontal well performance information.
The new method will assist in reservoir characterization, evaluation of evolving stimulation technologies in the same field, and allow classification of new type curves.
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Austin, Texas, USA, 20-22 July 2020. The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper by anyone other than the author without the written consent of URTeC is prohibited. Abstract The publicly available multi-terabyte dataset of the Marcellus Shale Energy and Environmental Lab (MSEEL) consortium provides a unique opportunity to develop fracture models and analyze the effectiveness of the stimulation of a reservoir on a consistent base. Sonic, microresistivity image and production logs, microseismic data, and raw fiber optic measurements are examples of such data. Abundant core samples supplied demonstrate reservoir complexity and high density of natural fractures. The planar fracture model allows us to compare and contrast multiple stimulation strategies and propose engineered completions that cannot be done solely by data-driven approaches. Conclusions about stage spacing, stimulation design, wellbore placement, and stage isolation are shared. The workflow will be detailed to allow others to use, verify, and critique our findings using the same initial data.
Zhang, Zhishuai (Chevron Energy Technology Company) | Fang, Zijun (Chevron Energy Technology Company) | Stefani, Joe (Chevron Energy Technology Company) | DiSiena, James (Chevron Energy Technology Company) | Bevc, Dimitri (Chevron Energy Technology Company) | Ning, Ivan Lim Chen (Chevron Energy Technology Company) | Hughes, Kelly (Chevron Energy Technology Company) | Tan, Yunhui (Chevron Energy Technology Company)
Fiber Optic Sensing, including both low-frequency Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS), can be used to record strain rate or strain for hydraulic fracturing monitoring in an offset well. However, current work focusses on acquisition, processing, and qualitative interpretation. We investigated the modeling of DAS and DSS strain responses to hydraulic fractures during stimulation process. The modeling work provides valuable insights to understand low-frequency DAS and DSS strain measurements during hydraulic stimulation.
We used the Displacement Discontinuity Method (DDM) to model the strain/strain rate field around kinematic propagating fractures. This efficient method provides a quick assessment of models with various fracture extents and net pressures. It also allows simulating the strain responses to a network of fractures in consideration of their interactions. During the stimulation stage of hydraulic treatment, the fracture propagation is modeled by prescribing gradually increased fracture size and calculating the displacement discontinuities that representing fractures at each step. After the stimulation stops, we assume the fracture extent will not change but the net pressure within the fracture gradually decreases due to fluid leakoff. We calculate the displacement discontinuities representing fractures using the fracture extent and the stress boundary conditions on fractures. The strain and stress projected along the monitoring well are calculated from these displacement discontinuities at each time step and converted to strain rate by taking their time derivatives.
We compared and verified our modeling with field observations from the Hydraulic Fracturing Test Site 2 (HFTS2) project, a research experiment performed in the Delaware Basin, West Texas. For a horizontal monitoring well, modeling results explain heart-shaped extending pattern before a fracture hit, polarity flip during stimulation due to fracture interaction, and V-shape patterns when a fracture bypasses the monitoring well from above or below without intersecting. For a vertical monitoring well, modeling shows the different characters of low-frequency DAS and DSS responses when a fracture is near and far away from a vertical monitoring well for both elliptic fractures and layered fractures.
Geomechanical modeling lays the groundwork for quantitative interpretation and fracture-geometry estimation. Our modeling approach provides insight into unraveling the patterns observed by far-field low-frequency DAS and DSS during hydraulic fracturing. Synthetic modeling results of various scenarios can also be used to improve fiber-optic acquisition design for stimulation monitoring.
Low-frequency DAS and DSS modeling and monitoring integrate information on geomechanics, fluid flow, pressure distribution, earth properties, and fracture propagation. The modeling results and field observations can also be compared and validated with engineering data such as pressure and temperature, with geological data such as cores, and with geophysics data such as microseismic and time-lapse seismic, to provide a comprehensive understanding of hydraulic fractures.
There are still many challenges involved with the quantitative interpretation of downhole distributed-temperature measurements for diagnosing multistage-fracturing treatments in horizontal wells. These challenges include handling enormous amounts of data measured by the sensors in a continuous time and space domain, a ready-to-use fast and robust forward model to simulate temperature behavior, and an efficient algorithm to invert the parameters that are of interest. Because multistage fracturing involves many uncertain parameters (ranging from reservoir properties to treatment design, to fracture geometries and conductivity), the problem becomes extremely complicated when the measured temperature is inverted to a downhole flow profile. In this study we present an approach for combining forward and inverse models to interpret downhole temperature data. Our goal was to improve computational efficiency. Field data from a gas well in the Marcellus Shale were used to illustrate the feasibility of quantitative interpretation of temperature measurement for fracture diagnosis.
The forward model used the fast-marching method (FMM). The forward simulation was an order of magnitude faster than the semianalytical model, which is the essential contribution for successfully applying the method in the field case. The improved inversion procedure increased the efficiency of the interpretation. The inversion procedure began with a sensitivity study to select the inversion parameters among various other parameters, such as fracture half-length and the fracture conductivity, and to determine the impact of their uncertainty on inversion. The inversion model used the initial analysis of the temperature gradient to identify the fracture locations with significant temperature changes for interpretation and eliminated the rest of the fracture locations from interpretation. Thus, we obtained a prior estimation of the selected inversion parameters, which was used as an initial estimate for the inversion process. This prior estimation saved significant computation. The inversion was performed fracture by fracture using either parallel computing or sequential computing on the basis of the sensor locations.
We began with a synthetic example containing multiple fractures to illustrate the approach and test the procedure accuracy and computation speed. The primary inversion parameter was flow rate, though we also interpreted either fracture length or fracture conductivity when assuming all the other parameters as additional constraints. With an adequate initial estimate, the inverted parameters matched the reference “true value” properly. The inversion process converged with reasonable iterations for each fracture (2–3 iterations). The operation time highlights the advantages of the inversion approach presented in this paper. The guided initial estimation ensured that the gradient inversion approach converged and avoided local minimization. Finally, a field application for interpreting the DTS measurement for the flow profile of the multistage-fractured horizontal well was performed using this inversion method, and it showed encouraging results.
The results of our investigation illustrate the procedure feasibility for using temperature data to diagnose a multistage-fracturing treatment. Our proposed inversion model was fast and reliable and provided a promising tool that can be used to quantitatively interpret downhole temperature data.
As the industry continues to absorb the lessons, and capitalize on the possibilities, offered by big data, professionals have kept in mind that exploration is not the only boundary of the digital frontier. Production monitoring and surveillance requires heightened degrees of precision and efficiency as operations are streamlined and projects are evaluated continuously. This month’s feature highlights a trio of papers focused on innovative technologies that have been implemented successfully in environments ranging from the deepwater Gulf of Mexico (GOM) to China’s Shengli field. Paper SPE 196188 describes third-generation production-logging-tool technology that uses miniaturization and digitalization to maximize the effect of digital sensors in deepwater fields. The tool possesses a rotating functionality that has yielded robust and accurate data in GOM case studies.