Steam-conformance control in horizontal injectors is important for efficient reservoir-heat management in heavy-oil fields. Suboptimal conformance and nonuniform heating of the reservoir can substantially affect the economics of the field development and oil-production response and result in nonuniform steam breakthrough. To achieve the required control, it is essential to have an appropriate well-completion architecture and robust surveillance.
Five fiber-optic systems, each with a unique steam-conformance-control-completion configuration, have been installed in two horizontal steam injectors to help mature steam-injection-flow profiling and conformance-control solutions. These fiber-optic systems have used custom-designed fiber-optic bundles of multimode and single-mode fibers for distributed-temperature sensing (DTS) and distributed-acoustic sensing (DAS), respectively. Fiber-optic systems were also installed in a steam-injection-test-flow loop. All the optical fibers successfully acquired data in the wells and flow loop, measuring temperature and acoustic energy.
A portfolio of algorithms and signal-processing techniques was developed to interpret the DTS and DAS data for quantitative steam-injection-flow profiling. The heavily instrumented flow-loop environment was used to characterize DTS and DAS response in a design-of-experiment (DOE) matrix to improve the flow-profiling algorithms. These algorithms are dependent on independent physical principles derived from multiphase flow, thermal hydraulic models, acoustic effects, large-data-array processing, and combinations of these methods for both transient and steady-state steam flow. A high-confidence flow profile is computed using the convergence of the algorithms. The flow-profiling-algorithm results were further validated using 11 short-offset injector observation wells wells in the reservoir that confirmed steam movement near the injectors.
Deasy, Michael (Range Resources – Appalachia, LLC) | Brown, Kenneth (Range Resources – Appalachia, LLC) | He, Jonathan (Range Resources – Appalachia, LLC) | Lipscomb, Wade (Range Resources – Appalachia, LLC) | Ockree, Matthew (Range Resources – Appalachia, LLC) | Voller, Katharine (Range Resources – Appalachia, LLC) | Frantz, Joe (Range Resources – Appalachia, LLC)
This paper presents a case history and lookback on the Reduced Cluster Spacing (RCS) completion design that was initiated in 2012. We review results of the initial analyses used to demonstrate proof of concept, summarize key aspects of the completion design, and discuss execution and results of the initial pilot tests and subsequent field-wide implementation. We propose a method to incorporate results of RCS models into production forecasts, and quantify the impact of RCS designs on volumes and economics over time.
We begin by presenting proof of concept analyses used to justify the initial field pilot. We then discuss RCS field trials, commenting on key aspects of project design and operational execution. We compare RCS well performance to control wells using normalized production plots, discuss type curve (TC) forecasting for RCS wells, and touch briefly on more rigorous modeling of RCS completions. We present a methodology to incorporate results from rigorous models into simpler type curves suitable for quick economic analyses and volumetric comparisons. We conclude by reviewing the economic and production impact of RCS on production at the well level and field development level.
Case histories are presented demonstrating the use of a production normalization process to assess the value of different completion designs. We demonstrate that RCS completion designs have been successful in terms of both volumes uplift and economic performance. We describe positive and negative aspects of the route taken to implement this strategy in the field. We conclude that the use of "uplift factors" derived from modeling can be used to efficiently incorporate detailed model findings into typical engineering workflows for volumes and economics forecasting. As a result of the work presented in this paper, RCS completions have become the standard in our Marcellus wells.
This lookback will present a method to effectively demonstrate proof of concept for new completion designs and assess the field implementation of novel completion strategies. This method is demonstrated by quantifying the value of reduced cluster spacing achieved in the Marcellus. We also provide a simple way to incorporate complex model results into every day engineering and economic forecasts.
Carr, Timothy (West Virginia University) | Ghahfarokhi, Payam Kavousi (West Virginia University) | Carney, BJ (Northeast Natural Energy, LLC) | Hewit, Jay (Northeast Natural Energy, LLC) | Vagnetti, Robert (National Energy Technology Laboratory, US Department of Energy)
Distributed temperature sensing (DTS) was used to record temperature from early 2016 to present for a Marcellus Shale horizontal dry gas well, MIP-3H, located in Monongalia County, West Virginia. In addition, after wellbore clean-out with water and nitrogen a flow scanner production log was conveyed on March 02, 2017. The flow scanner provides one day of gas and water production from each of the 28 stages in MIP-3H and from each of the clusters. The DTS data provides an opportunity to inspect the reservoir for Joule-Thompson (JT) effect, a phenomenon that describes cooling of an non-ideal gas as it expands from high pressure to low pressure, and obtain a relative production attribute along the lateral of the MIP-3H. The original fiber-optic DTS data shows the temperature along the lateral; however, due to the geometry of the well with toe up and the presence of a small fault and minor water production at Stage 10 relative gas production of each stage cannot be directly determined from the raw DTS data. We present two methods to generate DTS attributes that can be used to better reveal relative gas and water production through time from each perforation cluster and each stage of the MIP-3H. The first attribute deals with the deviations of the DTS measurements from the calculated geothermal temperature, while the second attribute calculated the difference between DTS temperature and the average daily DTS temperature along the lateral of the MIP-3H. We show that the latter DTS attribute provides a more robust image of temperature variations regime along the lateral than the former attribute. Negative values of the DTS attributes reveals JT cooling, resulting from stages of the MIP-3H with higher natural gas production. A correlation analysis of the production log with the calculated DTS attributes suggests that the production log is not representative of the entire production life of MIP-3H well. Temporal correlation with the DTS attributes is highest close to the production log recording day (March 2, 2017) decrease rapidly and the weak correlation switches from positive to negative.
Traditionally, in order to estimate the production potential at a new, prospective field site via simulation or material balance, one needs to collect various forms of expensive field data and/or make assumptions about the nature of the formation at that site. Decline curve analysis would not be applicable in this scenario, as producing wells need to pre-exist in the target field. The objective of our work is to make first-order forecasts of production rates at prospective, undrilled sites using only production data from existing wells in the entire play. This is accomplished through co-kriging of decline curve parameter values, where the parameter values are obtained at each existing well by fitting an appropriate decline model to the production history. Co-kriging gives the best linear unbiased prediction of parameter values at undrilled locations, and also estimates uncertainty in those predictions. Thus, we can obtain production forecasts at P10, P50, and P90, as well as calculate EUR at those same levels, across the spatial domain of the play.
To demonstrate the proposed methodology, we used monthly gas flow rates and well locations from the Marcellus shale gas play in this research. Looking only at horizontal and directional wells, the gas production rates at each well were carefully filtered and screened. Also, we normalized the rates by perforation interval length. We kept only production histories of 24 months or longer in duration to ensure good decline curve fits. Ultimately, we were left with 5,637 production records. Here, we chose Duong’s decline model to represent production decline in this shale gas play, and fitting of this decline curve was accomplished through ordinary least square regression.
Interpolation was done by universal co-kriging with consideration to correlate the four parameters in Duongs’ model, which also showed a linear trend (the parameters show dependency on the
We forecasted potential gas production in the study area using co-kriging. Heat maps of decline curve parameters as well as EUR were constructed to give operators a big picture of the production potential in the play. The methods proposed are easy to implement and do not require various expensive data like permeability, bottom hole pressure, etc., giving operators a risk-based analysis of prospective sites. We also made this analysis available to the public in a user-friendly web app.
Current decline models fail to capture all of the behavior in shale gas production histories. That is, upon fitting one of these models, one often sees significant and sustained deviation of the flow rate data points from the decline trend. One way to measure this "lost signal" is to look at the autocorrelation in the residuals about the fitted decline model. Indeed, with many shale gas wells we see significant amounts of autocorrelation, especially when comparing the flow rate at one time to the next (lag one). Theoretically, this serially autocorrelated error can impact decline curve analysis in two ways: 1) inefficient estimation of decline curve parameters, and 2) lost signal in the data. Borrowing from time series statistics, there are two conventional ways of dealing with these potential problems: 1) estimate the decline curve parameters with generalized least squares or generalized nonlinear least squares, and 2) fitting an ARMA model to the residuals and adding it to the fitted decline curve.
This paper investigates the practical implications of these two procedures by exercising them over decline curves fit to 8,527 Marcellus shale gas wells (all wells from that play with viable data for the analysis). The study explores the effect that generalized regression methods and ARMA-modeled residuals have on six different decline curves, and performance is measured in terms of sum of squared residuals (a metric for goodness-of-fit, calculated on the training data (first 24 months of each record)) and mean absolute percent error (a standard metric for forecasting accuracy, calculated on the testing data (all production rates after 24 months)).
We find that inclusion of the ARMA-modeled residuals largely improves the goodness-of-fit for any decline curve, and improves the forecasting accuracy for the Hyperbolic decline curve and Duong's model. The use of generalized least squares or generalized nonlinear least squares has little benefit in fitting the decline curves, except for the Logistic Growth model, where it improves both fit and forecasting accuracy.
BP has started production from a prolific new natural gas well in the Mancos Shale of New Mexico, a discovery that points to the area's potential as a large new gas supply source for the United States. Early production rates at the NEBU 602 Com 1H well in San Juan County are the highest achieved in the past 14 years within the San Juan Basin, a large oil- and gas-producing area spanning southwest Colorado and northwest New Mexico that includes the Mancos Shale. The well achieved an average 30-day initial production rate of 12.9 MMcf/D. The successful well test took place on assets that BP acquired from Devon Energy in late 2015, which expanded the company's position in the basin and provided improved access to the Mancos. The NEBU 602 Com 1H well was drilled with a 10,000-ft lateral in an area known as the Northeast Blanco Unit (NEBU), a section of federal lands in New Mexico's San Juan and Rio Arriba counties and an area where BP has been present since the 1920s.
Nagoo, A. S. (Nagoo & Associates) | Kulkarni, P. M. (Equinor) | Arnold, C. (Escondido Resources) | Dunham, M. (Bravo Natural Resources) | Sosa, J. (Jones Energy) | Oyewole, P. O. (Proline Energy Resources)
In this seminal work, we reveal for the first time an extensively field-tested, demonstrably accurate and simple analytical equation for the calculation of the critical gas velocity limit (or onset of liquid flow reversal) in horizontal wells as an explicit and direct function of diameter, inclination and fluid properties. For the independently verifiable and first-of-its-kind multi-play field validation study, we carefully assimilate a very large database of actual horizontal gassy oil and gas liquid loading wells from several unconventional U.S. shale plays with different bubble point and dew point fluid systems and varying gas-to-liquid ratios and varying water cuts. The shale plays in our validation database include the Eagle Ford, Woodford, Cleveland Sands, Haynesville, Cotton Valley, Fayetteville, Marcellus and Barnett formations within their associated Western Gulf, South Texas, Arkoma, Western Anadarko, East Texas, Appalachian and Permian basins. Then, after summarizing our comprehensive field testing results, practical production optimization applications of the new analytical equation and advanced use cases of interest are further highlighted in various liquid loading prediction and prevention scenarios.
As opposed to prior critical gas velocity calculation methods (droplet reversal-based, film reversal-based, flow structure stability/energy), video observations both in the lab and the field clearly show continuously-evolving, co-existing and competing flow structures even with simple fluids without mass exchanges. Therefore, this work avoids skewed assumptions on demarcating the prevailing or dominant flow structure. Instead, the new analytical equation developed is based on an analysis of the major forces in the flow field, namely the axial buoyancy vector, the convective inertial and the interfacial tension forces, in combination with an assumption of the onset of liquid flow reversal based on flow field bridging (Taylor instability). Since the new analytical equation was formulated using these minimalist assumptions, this unique characteristic results in the highest predictability obtainable for the critical gas velocity calculation because there is the least amount of uncertainties (fudge factors). The consistent accuracy of the equation against our extensive horizontal well liquids loading database verifies this fact. Moreover, the simplicity of form of the equation makes it easy to use in that every practicing engineer in practice can perform fast hand or spreadsheet calculations. In effect, this equates to having a model as simple as the Turner model but now with additional direct functions of diameter and inclination. Also, the results clearly invalidate the need for artificial variables (such as interfacial friction factor) that cannot be directly measured in any experiment. In terms of usage, the new model is used in liquid loading prevention scenarios such as end-of-tubing (EOT) landing optimization and tubing-casing selection. Evidently, this work proves that no complex, computer-only procedure is necessary for accurate critical gas velocity calculation. This finding has significant speed and improved answer-reliability implications in strong favor of the presented simple equation for use in artificial lift, production optimization and digital oilfield software in industry, in addition to being ideally suited for ‘physics-guided data analytics’ applications in real-time production operations environments.
Ghahfarokhi, Payam Kavousi (West Virginia University) | Bhattacharya, Shuvajit (University of Alaska Anchorage) | Carr, Timothy (West Virginia University) | Shahkarami, Alireza (Saint Francis University, Loretto) | Elliott, Justin (West Virginia University)
First downhole application of distributed acoustic sensing for hydraulic-fracturing monitoring and diagnostics.
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Houston, Texas, USA, 23-25 July 2018. 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.
Ansari, Amir (Department of Petroleum and Natural Gas Engineering, West Virginia University) | Fathi, Ebrahim (Department of Petroleum and Natural Gas Engineering, West Virginia University) | Belyadi, Fatemeh (Department of Petroleum and Natural Gas Engineering, West Virginia University) | Takbiri-Borujeni, Ali (Department of Petroleum and Natural Gas Engineering, West Virginia University) | Belyadi, Hoss (Department of Petroleum and Natural Gas Engineering, West Virginia University)
Liquid loading in horizontal gas wells impairs gas production and if not diagnosed in a timely manner can kill the well. Liquid loading occurs when the gas production rate declines and gas velocity drops below the critical velocity required to carry liquid to surface. Different models used in conventional reservoirs such as droplet, film or transient multiphase flow models are also applied with modifications in unconventional gas reservoirs. However, none of these models show great success when applied to inclined and horizontal wells in shale gas reservoirs. This is due to the fact that these models are developed for vertical wells and cannot identify the right multiphase flow regime in inclined and horizontal sections of the well. It is also extremely hard defining the right liquid droplet size and shape or liquid film thickness as well trajectory changes. Furthermore, these models cannot accurately predict the transient between annular and slug flow regimes in horizontal wells. As more wells are produced in shale gas reservoirs, a great amount of information from production control and monitoring becomes available which can be used to build a data-based smart model for real time diagnostics of liquid loading in new wells. In this new approach, data (pressure, completion, and productions) from many wells which have experienced or not experienced liquid loading problems in the same area will be the basis for developing the smart model.
What is being proposed here is a unique approach that includes developing a data-based technology for the training of neural networks that can be used as a smart model in real time to identify the start of liquid loading in unconventional gas wells. This innovative technique incorporates a unique fuzzy pattern recognition algorithm and unsupervised analysis technique to identify the most influential parameters impacting liquid loading in unconventional gas wells. The main objective for this manuscript is to develop a smart model that can predict the dynamics of liquid-gas interface and identify the start of liquid loading. Finally, the minimum gas velocity/rate to avoid the liquid loading can be determined.
For this study, a Marcellus Shale reservoir is selected. Production and completions history of 160 wells are collected. First the study is performed on a single well where 70 percent of the information is used for neural network training purposes, 15% for calibration, and 15% for validation of the model. The results show that the smart model is able to precisely predict the start of the liquid loading in the well and raise a warning flag when the possibility of liquid loading is high. Next, series of wells in the region is picked and smart model is built based on the 70% training, 15% calibration, and 15% validation. This model is then used to predict the liquid loading in a different well in the same region as a completely blind well. The results show high accuracy and reliability in predicting the start of liquid loading. To overcome the implicit dependency of the model to Turner et al. critical velocity criteria during the training, unsupervised learning algorithm is used to predict the loading and unloading status of the wells. The technique showed great success in predicting the well status and confirmed with field observations.
The new smart model developed for Marcellus Shale shows great promise that this approach can be applied in other areas where limited history of production and liquid loading exists.