Impact of Uncertainty in Estimation of Shale Gas Reservoir and Completion Properties on EUR Forecast and Optimal Development Planning: A Marcellus Case Study

Abstract

The use of multi-stage fractured horizontal wells has made production from shale gas plays feasible. The production history from these wells is characterized by long transient linear transient flow. The key reservoir (nature) and completion (nurture) properties that impact flow behavior of the well are permeability, Original Hydrocarbon in Place (OHIP), number of hydraulic fractures, fracture area and fracture spacing. Understanding the interrelationship between these parameters is critical for optimal development of shale gas resource plays. The proposed shale gas workflow uses a hybrid analytical model. An analytics based diagnostic process analyzes well performance history and a numerical model validates the feasible history match models for representative forecasts. The model results allow the user to capture the range of uncertainty in estimating individual reservoir or completion properties, such as permeability, fracture area, fracture spacing, etc. The diagnostic process provides the relationship between key factors dictating well performance such as OHIP, effective fracture area, effective reservoir permeability, effective fracture spacing and well spacing. In this paper, wells from the Marcellus play, in Pennsylvania, USA are evaluated using the proposed shale gas workflow. Surveillance is an integral part of this workflow. The paper shows how surveillance can be used for resource management and exception management. Along with data mining, the workflow accelerates the learning curve to evaluate the effectiveness of current field practices. The results help with understanding the effectiveness of proppant pumped, the potential number of contributing clusters and production issues. Continued surveillance reduces the uncertainty surrounding all parameters. This knowledge base can then be used to optimize the asset development strategy, maximizing the return on investment (ROI).

Numerical study of the effect of uneven proppant distribution between multiple fractures on shale gas well performance

“…Therefore, the total number of hydraulic fractures is 44. The detailed reservoir properties for this model are the average values based on available data from published Marcellus shale work [33][34][35][36][37] and are listed in Table 2. In general, there are high uncertainties in reservoir and fracture properties in shale gas reservoirs.…”

Shut-in based production optimization of shale-gas systems

“…This phenomenon has been observed in production data with isolated shut-ins, e.g. King (2010); Valkó (2009) ;Jayakumar et al (2011);Jayakumar and Rai (2014), although, as mentioned in the previous section, most frequently related to an initial shut-in period (Cheng, 2012;Makhanov et al, 2013). Shut-ins of shale-gas wells have also been performed and studied related to the plunger-lift technique, however, with durations of minutes or hours.…”

Deciphering and handling uncertainty in shale gas supply chain design and optimization: Novel modeling framework and computationally efficient solution algorithm

“…The total shale gas production rate at a shale site equals the sum of the individual production rates of the different wells. Therefore, the total shale gas production at each shale site in each time period can be calculated by SP i;t;js 5NN i Á spp i;t Á eur i;js ; 8i; t; js (11) where spp i,t denotes the shale gas production profile of a shale well at shale site i in time period t; we use this time-dependent parameter to account for the decreasing feature of the shale gas production profile of a certain well. 15,18 eur i,js is the parameter accounting for different EURs of the shale well at shale site i in scenario js.…”

“…Where umb1 i,t , umb2 i,t , umb3 p,t , umb4 p,t , umb5 p,t , umb6 are dual variables corresponding to original mass-balance constraints (11), (12), (13), (14), (15), and (10), respectively. Similarly, vcp1 i,p,t , vcp2 p,m,t , vcp3 p,t , vcp4 m,t , vcp5 t are dual variables corresponding to original capacity constraints (16), (17), (18), (19), and (20), respectively.…”

“…4,5 Currently, there are publications regarding design and operations of shale gas supply chains, [6][7][8][9] and some works present a general analysis of the uncertainty in shale gas supply chains, [10][11][12][13] while only a few of them provide a quantitative solution using mathematical programming tools. Yang and Grossmann 14 presented a mixedinteger linear programming (MILP) model optimizing water use life cycle for shale wells.…”