From high perm oil to tight gas - A practical approach to model hydraulically fractured well performance in coarse grid reservoir simulators.

2012 ◽  
Author(s):  
Mark W. Burgoyne ◽  
Adam James H. Little
Keyword(s):  
Coarse Grid ◽  
Practical Approach ◽  
Tight Gas ◽  
Well Performance ◽  
Fractured Well

scholarly journals Unconventional Gas Production from Hydraulically Fractured Well-An Application of Direct Search Based Optimization Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2726-2737
Keyword(s):  
Objective Function ◽  
Fracture Propagation ◽  
Gas Production ◽  
Production Model ◽  
Tight Gas ◽  
Gas Reservoirs ◽  
Unconventional Gas ◽  
Fracture Geometry ◽  
Propagation Behavior ◽  
Fractured Well

Unconventional gas reservoirs are now the targets for meeting the demand for gas. These reservoirs are at the depth of more than 10,000 ft (even over 15000 depth as well) and are difficult to be exploited by conventional methods. For the last decades hydraulic fracturing has become the tool to develop these resources. Mathematical models (2D and pseudo-3D) have been developed for fracture geometry, which should be realistically created at the depth by surface controllable treatment parameters. If the reservoir rock is sandstone, then proppant fracturing is suitable and if the rock is carbonates, then acid fracturing is applicable. In both cases, proper design of controllable treatment parameters within constraints is essential. This needs proper optimization model which gives real controllable parametric vales. The model needs the most important analyses from geomechanical study and linear elastic fracture mechanics of rock containing unconventional gas so that fracture geometry makes maximum contact with the reservoirs for maximum recovery. Currently available software may lack proper optimization scheme containing geomechanical stress model, fracture geometry, natural fracture interactions, real field constraints and proper reservoir engineering model of unconventional gas resources, that is, production model from hydraulically fractured well (vertical and horizontal). An optimization algorithm has been developed to integrate all the modules, as mentioned above, controllable parameters, field constraints and production model with an objective function of maximum production (with or without minimization of treatment cost). Optimization is basically developed based on Direct Search Genetic and Polytope algorithm, which can handle dual objective function, non-differentiable equations, discontinuity and non-linearity. A dual objective function will meet operator’s economic requirements and investigate conflict between two objectives. The integrated model can be applied to a vertical or horizontal well in tight gas or ultra-tight shale gas deeper than over 10,000 ft. A simulation (with industrial simulators) was conducted to investigate and analyse fracture propagation behavior, under varying parameters with respect to the fracture design process, for tight gas reservoirs. Results indicate that hydraulic fracture propagation behavior is not uninhibited in deep reservoirs as some may believe that minor variations of variables such as in-situ stress, fluid properties etc. are often detrimental to fracture propagation in some conditions. Application of this model to a hypothetical tight and ultra-tight unconventional gas formations indicates a significant gas production at lower treatment cost; whereas the resources do not flow without any stimulation (hydraulic fracturing).

Beyond Volumetrics: Unconventional Petrophysics for Efficient Resource Appraisal (Example from the Khazzan Field, Sultanate of Oman)

2015 ◽  
Author(s):  
David R. Spain ◽  
German D. Merletti ◽  
William Dawson
Keyword(s):  
Gas Permeability ◽  
Stress Profile ◽  
Tight Gas ◽  
Well Performance ◽  
Flow Properties ◽  
Dynamic Flow ◽  
Stress Regime ◽  
Flow Units ◽  
Fluid Saturation ◽  
Rock Typing

Abstract The Middle East region holds substantial resources of unconventional tight gas and shale gas. The efficient extraction of these resources requires significant technology and expertise across numerous disciplines, including reservoir description and geomechanical characterization, hydraulic fracture modelling and design, advanced numerical simulation capabilities, sensor and surveillance technologies, and tightly integrated workflows. The effective application of these integrated subsurface and completion workflows leads to improved capital efficiency and well performance through increased well potential, increased ultimate recovery, and reduced costs. Key elements include dynamic rock typing to highlight potential flow units that will maximize gas deliverability, geomechanical modelling to provide a calibrated stress profile, and an integrated model that demonstrates the importance of understanding both dynamic flow properties and geomechanical response in complex tectonic environments. Dynamic rock typing focuses on using both depositional and petrophysical properties including rock type, porosity, and effective gas permeability at reservoir conditions to divide the reservoir into flow units in the context of their saturation history. The geomechanical profiling generates a tectonics-corrected minimum horizontal stress (SHmin) and the net confining stress (NCS). The rock-log-test calibration requires the evaluation and integration of subsurface fracture tests, including After-Closure Analysis (ACA), Data Fracs and Micro Fracs. All three involve different injection volumes and sampled reservoir volumes. Tight gas petrophysical studies must go “beyond volumetrics”, and should consider not only the static (storage) and dynamic (flow) properties within the context of the petroleum system and evolution of the current day pore geometry and fluid saturation distribution, but also the geomechanical stress regime and its implications for efficient completion optimization. Alternative interpretations test the range of uncertainty and are useful in designing field trials and surveillance strategies to reduce the subsurface uncertainty and to mitigate development risks.

Transient Analysis of Tight Gas Well Performance - More Case Histories

2003 ◽  
Author(s):  
Jorge A. Arevalo-Villagran ◽  
Heber Cinco-Ley ◽  
Robert. A. Wattenbarger ◽  
Francisco Garcia-Hernandez ◽  
Fernando Samaniego-Verduzco
Keyword(s):  
Transient Analysis ◽  
Tight Gas ◽  
Well Performance ◽  
Case Histories ◽  
Gas Well

The Use of Rate-Transient-Analysis Modeling To Quantify Uncertainties in Commingled Tight Gas Production-Forecasting and Decline-Analysis Parameters in the Alberta Deep Basin

2014 ◽  
Vol 17 (02) ◽  
pp. 209-219 ◽  
Author(s):  
H.. Luo ◽  
G.F.. F. Mahiya ◽  
S.. Pannett ◽  
P.. Benham
Keyword(s):  
Transient Analysis ◽  
Early Time ◽  
Gas Production ◽  
Tight Gas ◽  
Well Performance ◽  
Deep Basin ◽  
Type Curves ◽  
Production Forecasting ◽  
Rate Transient Analysis ◽  
Production History

Summary The evaluation of expected ultimate recovery (EUR) for tight gas wells has generally relied upon the Arps equation for decline-curve analysis (DCA) as a popular approach. However, it is typical in tight gas reservoirs to have limited production history that has yet to reach boundary-dominated flow because of the low permeability of such systems. Commingled production makes the situation even more complicated with multiboundary behavior. When suitable analogs are not available, rate-transient analysis (RTA) can play an important role to justify DCA assumptions for production forecasting. The Deep-basin East field has been developed with hydraulically fractured vertical wells through commingled production from multiple formations since 2002. To evaluate potential of this field, DCA type curves for various areas were established according to well performance and geological trending. Multiple-segment DCA methodology demonstrated reasonable forecasts, in which one Arps equation is used to describe the rapidly decreasing transient period in early time and another equation is used for boundary-dominated flow. However, a limitation of this approach is the uncertainty of the forecast in the absence of extended production data because the EUR can be sensitive to adjustments in some assumed DCA parameters of the second segment. In this paper, we used RTA to assess reservoir and fracture properties in multiple layers and built RTA-type well models around which uncertainty analyses were performed. The distributions of the model properties were then used in Monte Carlo analysis to forecast production and define uncertainty ranges for EUR and DCA parameters. The resulting forecasts and EUR distribution from RTA modeling generally support the DCA assumptions used for the type curves for corresponding areas of the field. The study also showed how the contribution from the various commingled layers changes with time. The proposed workflow provides a fit-for-purpose way to quantify uncertainties in tight gas production forecasting, especially for cases when production history is limited and field-level numerical simulation is not practicable.

scholarly journals Prediction of Hydraulic Fractured Well Performance Using Empirical Correlation and Machine Learning

2021 ◽  
Vol 44 (2) ◽  
pp. 131-145
Author(s):  
Kamal Hamzah ◽  
Amega Yasutra ◽  
Dedy Irawan
Keyword(s):  
Machine Learning ◽  
Hydraulic Fracturing ◽  
Petroleum Industry ◽  
Prediction Method ◽  
Empirical Correlation ◽  
Well Performance ◽  
Data Limitations ◽  
Medium Permeability ◽  
Water Cut ◽  
Fractured Well

Hydraulic fracturing has been established as one of production enhancement methods in the petroleum industry. This method is proven to increase productivity and reserves in low permeability reservoirs, while in medium permeability, it accelerates production without affecting well reserves. However, production result looks scattered and appears to have no direct correlation to individual parameters. It also tend to have a decreasing trend, hence the success ratio needs to be increased. Hydraulic fracturing in the South Sumatra area has been implemented since 2002 and there is plenty of data that can be analyzed to resolve the relationship between actual production with reservoir parameters and fracturing treatment. Empirical correlation approach and machine learning (ML) methods are both used to evaluate this relationship. Concept of Darcy's equation is utilized as basis for the empirical correlation on the actual data. The ML method is then applied to provide better predictions both for production rate and water cut. This method has also been developed to solve data limitations so that the prediction method can be used for all wells. Empirical correlation can gives an R2 of 0.67, while ML can gives a better R2 that is close to 0.80. Furthermore, this prediction method can be used for well candidate selection means.

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