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ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
Abstract Uniformity of proppant distribution among multiple perforation clusters affects treatment efficiency in multistage fractured wells stimulated using the plug-and-perf technique. Multiple physical phenomena taking place in the well and perforation tunnels can cause uneven proppant distribution among multiple clusters. The problem has been studied in the recent years with experimental and computational fluid dynamics (CFD) methods, which provide useful insights but are impractical for routine designs. Simplified models that incorporated the proppant transport efficiency (PTE) correlation derived from the CFD results in a hydraulic fracture model have been also presented in literature. In this paper, we present a numerical model that simulates the transient proppant slurry flow in the wellbore, considering proppant transport and settling including bed formation, rate- and concentration-dependent pressure drop, PTE, and dynamic pressure coupling with the hydraulic fractures. The model is efficient and is designed to be an independent wellbore transport model so it can be integrated with any fracture models, including fully 3D and/or complex fracture network models, for practical design optimization. The model predictions are compared and found to agree with previously published studies. Parametric studies demonstrate sensitivity of proppant distribution to grain size, fluid viscosity, and pumping rate for fixed perforation designs. Analysis of the simulation results shows that the dominant cause of uneven proppant distribution is proppant inertia. Possible slurry stratification is less important, except for the cases with relatively low flow rates and near toe clusters. Accordingly, proppant distribution is less sensitive to perforation phasing than to the number of perforations in clusters. Alterations of the number of perforations per cluster within a stage enable achieving more even proppant distribution.
Abstract In the present cost-constrained environment, it is critical that operators effectively complete their wells while minimizing capital expenditure. Optimization efforts focus on increasing recovery factor by managing landing zone, increasing the number of effective fractures, increasing the size of the fractures, and increasing the length of the lateral, while reducing the total number of stages and job size, without sacrificing efficient proppant and fluid delivery. The same pressure to reduce expenditure also impacts decision making on diagnostic evaluation, reducing operators to ‘free’ or low-cost feedback, like surface production rates and decline curves. Operators are responding to these challenges by utilizing a combination of lower cost, post-completion diagnostics like deployed fiber optics, downhole camera evaluation of perforations and radioactive tracers. These less expensive options allow for a broader scope and number of diagnostic inquiries, whereas a permanent fiber may prove to be cost-prohibitive, reducing diagnostic focus to one well, in one part of a play. Combining differing diagnostic technologies enhances the overall description of the well and reservoir behaviors and improves confidence in their interpretation of stimulation and production efficiency; furthermore, where a single diagnostic measurement may be unlikely to justify dramatic change in a completion strategy, a combination of data points from different domains can and does support design change that leads to rapid, real world performance improvements. Care is needed in the conclusions drawn when utilizing complimentary diagnostics due to the differences in depth of investigation and the non-unique interpretation of some data types. This paper discusses three post-completion diagnostic technologies, perforation evaluation by downhole camera, radioactive tracers, and distributed acoustic and temperature sensing (DAS+DTS) data and their respective physical measurements, strengths and weaknesses and how they can be combined to better understand well and reservoir behavior. It concludes with a review of completion optimization efforts from the Rockies area, where these post-completion diagnostic technologies were applied in the evaluation of eXtreme Limited Entry (XLE) trials. A statistical analysis of the RA tracer, downhole camera measurement of perforation area and deployed fiber optic acquisition of DAS+DTS reveals no correlation between diagnostic answers, indicating no one diagnostic measurement can accurately predict the other, such that it could substitute for that diagnostic and provide the same answer. Asking the right question can often enhance the value of diagnostic descriptions of the system in question. Those answers often lead to the next question and clear the path forward in advancing completion optimization. Complimentary diagnostics facilitate a more complete understanding of stimulation and production performance when compared, increasing confidence when they agree. When one or more appear to disagree, the different respective physical measurements and depths of investigation often reveal a more complete and complex understanding of stimulation and production efficiency. As an aggregate they provide clarity on the effect of efforts to create conductive pathways into the reservoir, allowing operators increased control over the resulting production.
Abstract A seven-step workflow to help subsurface teams establish an initial thesis for optimal completion design (cluster spacing, proppant per cluster) and well spacing in emerging / under-explored resource plays is proposed and executed for the Powder River Basin Niobrara unconventional oil play. The workflow uses Rate Transient Analysis (RTA) to determine the parameter and then walks the reader through how to sequentially decouple the parameter into its constituent parts (frac height (h), number of symmetrical fractures achieved (nf), permeability (k) and fracture half-length (xf)). Once these terms were quantified for each of the case study wells, they were used in a black oil reservoir simulator to compare predicted verses actual cumulative oil performance at 30, 60, 90,120 & 180 days. A long-term production match was achieved using xf as the lone history match parameter. xf verses proppant per effective half-cluster yielded an R value of > 0.90. 28 simulation scenarios were executed to represent a range of cluster spacing, proppant per cluster and well spacing scenarios. Economics (ROR and/or NPV10/Net Acre) were determined for each of these scenarios under three different commodity pricing assumptions ($40/$2.50, $50/$2.50 and $60/$2.50). An initial thesis for optimal cluster spacing, proppant per designed cluster and well spacing were determined to be 12’, 47,500 lbs and 8-14 wells per section (based on whether or not fracture asymmetry is considered) when WTI and Henry Hub are assumed to be $50 & $2.50 flat.
Thiessen, Scott (Hunting Energy Services - Titan Division) | Han, Oliver (Hunting Energy Services - Titan Division) | Ahmed, Ramadan (University of Oklahoma) | Elgaddafi, Rida (University of Oklahoma)
ABSTRACT In hydraulic fracturing, determining the perforation pressure loss is a critical step in the design strategy, on-site troubleshooting diagnostics and post-fracture analysis. Historically, the most widely assumed and thus unknown components in the perforation friction equationare the coefficient of discharge and the holistic perforation diameter. The perforation coefficient of discharge has long been assumed as a dynamic variable dependent on the amount of fluid and proppant pumped through the perforations. This variable becomes increasingly important when clusters are spaced closer together and fewer perforations are shot such as in a limited entry design. Limited entry is a perforating technique used to generate uniform fractures along the wellbore by creating appropriate pressure differentials from cluster to cluster. With the adoption of consistent hole perforating shaped charges, the perforating diameters are more consistent and predictable. While not all consistent hole shaped charges have low diameter variability, the perforating diameters downhole are no longer an unknown, particularly after the introduction of downhole cameras. Therefore, the coefficient of discharge is the only unknown variable remaining. This paper presents an experimental methodology to accurately define the true coefficient of discharge in common completions perforated by a known consistent hole shaped charge. The test setup is illustrated, detailed test steps are discussed, and experimental data with correlations of rate per perforation and discharge coefficient is presented. Completions tested included 4-1/2", 5", and 5-1/2" casings in common weights and grades. Various perforating strategies were examined such as single shot and angled shot. Critical parameters such as entry hole diameters were made by the actual shaped charges and measured before and after the test. Freshwater and slickwater were used as hydraulic fluid and circulated at real-world pump rates through each perforation to simulate the actual field flow conditions. Based on the study, several correlations for the coefficient of discharge of flow through a perforation are created considering casing thickness, entry hole diameter and rate per perforation for the given consistent hole shaped charges. These correlations can improve perforation and fracturing designs where perforation friction are important variables.
Abstract In multi-stage plug-and-perf horizontal well completions, there are a multitude of moving parts and variables to consider when evaluating performance drivers. Properly identifying performance drivers allows an operator to focus their efforts to maximize the rate of return of resource development. Typically, well-to-well comparisons are made to help identify performance drivers, but in many cases the differences are not clear. Identifying these drivers may require a better understanding of performance variability along a single lateral. Data analytics can help to identify performance drivers using existing data from development activities. In the case study below, multiple diagnostics are utilized to identify performance drivers. A combination of completion diagnostics including oil and water tracers, stimulation data, reservoir data, 3D seismic, and borehole image logs were collected on a set of wells in the early appraisal phase of a field. Using oil tracers as the best indication of stage level performance along the laterals, data analytics is applied to uncover the relationships between the tracers and the numerous diagnostics. After smoothing was applied to the dataset, trends between oil tracer recovery, several independent variables and features seen in image logs and 3D seismic were identified. All the analyses pointed to decreasing tracer recovery, and likely decreased oil production, near faulted areas along each lateral. A random forest model showed a moderate prediction power, where the model's predicted tracer recovery on blind stages was able to explain 54% of the variance seen in the tracer response (r=0.54). This analysis suggests the identification of certain faulted areas along the wellbore could lead to ways of improving individual well economics by adjusting completion design in these areas.
Rodríguez-Pradilla, Germán (School of Earth Sciences, University of Bristol, UK.) | Eaton, David (Department of Geoscience, University of Calgary, Canada.) | Popp, Melanie (geoLOGIC Systems Ltd., Calgary, Canada.)
Abstract The goal of this work is to calibrate a regional predictive model for maximum magnitude of seismic activity associated with hydraulic-fracturing in low-permeability formations in the Western Canada Sedimentary Basin (WCSB). Hydraulic fracturing data (i.e. total injected volume, injection rate, and pressure) were compiled from more than 40,000 hydraulic-fractured wells in the WCSB. These wells were drilled into more than 100 different formations over a 20-year period (January 1st, 2000 and January 1st, 2020). The total injected volume per unit area was calculated utilizing an area of 0.2° in longitude by 0.1° in latitude (or approximately 13x11km, somewhat larger than a standard township of 6x6 miles). This volume was then used to correlate with reported seismicity in the same unit areas. Collectively, within the 143 km area considered in this study, a correlation between the total injected volume and the maximum magnitude of seismic events was observed. Results are similar to the maximum-magnitude forecasting model proposed by A. McGarr (JGR, 2014) for seismic events induced by wastewater injection wells in central US. The McGarr method is also based on the total injected fluid per well (or per multiple nearby wells located in the same unit area). However, in some areas in the WCSB, lower injected fluid volumes than the McGarr model predicts were needed to induce seismic events of magnitude 3.0 or higher, although with a similar linear relation. The result of this work is the calculation of a calibration parameter for the McGarr model to better predict the magnitudes of seismic events associated with the injected volumes of hydraulic fracturing. This model can be used to predict induced seismicity in future unconventional hydraulic fracturing treatments and prevent large-magnitude seismic events from occurring. The rich dataset available from the WCSB allowed us to carry out a robust analysis of the influence of critical parameters (such as the total injected fluid) in the maximum magnitude of seismic events associated with the hydraulic-fracturing stimulation of unconventional wells. This analysis could be replicated for any other sedimentary basin with unconventional wells by compiling similar stimulation and earthquake data as in this study.
Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rosecorrelations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.
Abstract Most waterfloods in California target sandstone formations that are unconsolidated in nature with high porosities and high permeabilities. These formations are also characterized by high Poisson ratios and low values of Young's Moduli. There has been a concern if, during the waterfloods of these types of formations, fracturing takes place at high-injection gradients. The influence of various factors on leak-off is studied in detail, indicating that with an increase in rock permeability, the leak-off velocity increases. This study included a comprehensive analysis of the characteristics of such soft formations and their responses to high injection gradients. We show that if the leak-off factors are adjusted to reflect high permeability and proper geomechanical properties, the probability of fracture formation is nil at injection gradients up to 0.9 psi/ft, for unconsolidated rooks. We computed estimated fracture width, fracture height, fracture length and noted for all three calculations, it takes gradients approaching 1psi/ft to note a non-trivial estimated value for these characteristics. This study shows that for unconsolidated formations like those in California targeted for waterfloods, the probability of fracture formation under pressure gradients of 0.9 psi/ft. is nil, and high injectivities can be exercised without the fear of fracture formation.
Alkinani, Husam (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology)
Abstract One of the most vital reservoir properties is permeability. It is usually measured using core samples with two major measurement methods; using gas or using liquid. The purpose of this work is to use a data-driven recurrent neural network model to estimate the equivalent liquid permeability based on gas permeability. By using this model, the equivalent liquid permeability can be predicted for the permeability of core samples with rich clay minerals measured using gas (or any core sample that is measured using gas). This will give an alternative way to the currently used method (Klinkenberg method). Core sample data measurements of more than 500 cores were obtained from limestone formations. The data went through a processing step to eliminate any measurement errors. Then, the data were clustered into training, validation, and testing. After many iterations, a decision was made to have a network with four hidden layer and twenty neurons in each hidden layer, and four delays in the input and the output. The findings showed that the network had stopped training after nine epochs with a validation mean squared error (MSE) of 5.3. The model exhibited excellent performance during training, validation, and testing with an overall R2 of 0.91 which is excellent. These findings prove that the model can closely track the actual equivalent liquid permeability measurements using the gas permeability measurements data within a reasonable margin of error. With the rise of machine learning and other artificial intelligence (AI) methods as well as the potential application in the petroleum industry, these methodologies can revolutionize the industry and save time and money.