The objectives of this paper are to summarize effective Reserves estimation methods for use in unconventional reservoirs, and to propose systematic procedures for classification of Resources other than Reserves (ROTR) volumes. We propose optimal timing for application of decline curve analysis (DCA), rate transient analysis (RTA), and reservoir simulation. Using these techniques, we provide results for one well from a 38-well database in the Permian Basin wells (TX USA). We then describe how the volumes are classified and categorized and how those volumes move between Reserves and ROTR as more information becomes available.
We begin with the analysis of well performance, where we specify the information that is necessary for each estimation method. We then suggest procedures to identify the flow regimes using diagnostic plots, provide guidance on the application of multi-segment DCA models, and finally suggest procedures for the application of RTA and reservoir simulation. We continue with progress toward Reserves classification, starting with suggested procedures to reclassify Prospective Resources as Contingent Resources (upon discovery). We provide post-discovery guidance on development and commerciality for the project maturity sub-classes (within the Contingent Resources classification). We explain that “established technologies” must be technically and economically viable before they can be used for development decisions. And finally, we examine requirements to remove contingencies so that the volumes can be reclassified properly as Reserves.
Our major suggestions for well performance analysis are, first, that the multi-segment DCA approach is most effective in unconventional reservoirs when specifically relevant models are used for transient flow and boundary-dominated flow. Furthermore, we suggest that RTA using analytical models expands possibilities of forecasting for changes in well conditions and for well spacing studies. Though time and computationally time consuming, compositional simulation is required for confident analysis of near-critical reservoir fluids.
For movement of resources toward Reserves, we suggest that there is no linear path to define the movement from Prospective to Contingent Resources, though there are certain criteria which must be met for a given project. Certain contingencies, such as price of oil and available technologies, dominate the classification of resource volumes.
This paper provides a visual representation of when to use each Reserves estimation method depending on available data. We present a thorough analysis of best practices for each Reserves estimation method. We provide graphical representation of the movement between Prospective to Contingent Resources categories, the progression in chance of development and commerciality within project maturity sub-classes for Contingent Resources, and the contingencies that must be resolved to move from Contingent Resources to Reserves. Finally, we present an explanation of the criteria that must be met before volumes can be reclassified and/or recategorized from undiscovered to discovered.
The objective of this work is to develop a methodology to estimate the fraction of Reserves assigned to each Reserves category (1P, 2P, and 3P) of the PRMS resources classification matrix using a cumulative distribution function (CDF). Previous published work has often used Swanson's Mean (SM) as the basis for allocating Reserves to individual categories, but we found that this method, which relates the Reserves categories through a CDF for a normal distribution, is an inaccurate means to determine the relationship of the Reserves categories with asymmetric distributions, and our work identified a better method, Gaussian Quadrature (GQ).
Production data are lognormally distributed, regardless of basin type, and thus are not compatible with the SM concept. The GQ algorithm provides a methodology to estimate the fraction of Reserves that lie within the 1P, 2P, and 3P categories — known as their
We selected 38 wells from a Permian Basin dataset available to us, and we performed probabilistic decline curve analysis (DCA) using the Arps Hyperbolic model and Monte Carlo simulation (MCS) to obtain a probability distribution of the 1P, 2P, and 3P volumes. We considered this information to be our "truth case," to which we compared relative weights of different Reserves categories from the GQ and SM methodologies. We also performed probabilistic rate transient analysis (RTA) using the IHS
The probabilistic DCA results indicated that the SM method is an
Based on our results, we conclude that the GQ method is accurate and can be used to approximate the relationship between the relative weights of resources in PRMS categories. This relationship will aid entities in reporting Reserves of different categories to regulatory agencies because it can be recreated for any field, play, or region. These distributions of Reserves and Resources Other than Reserves (ROTR) are important for planning and for resource inventorying. The GQ method provides a measure of confidence in our prediction of the Reserves weights because of the relatively smaller percentage differences between the probabilistic DCA, RTA, and GQ weights than those implied by the SM method. For reference, our proposed methodology can be implemented in both conventional and unconventional reservoirs.
Several recent studies have reported that proppant "bridging" (blocking) occurs at the interface between primary and secondary fractures. Such bridging blocks flow and significantly reduces the efficiency of proppant placement. The prevention of bridging is of great importance, but the criteria for bridging formation have yet to be determined. In this numerical study of proppant transport, we propose bridging formation criteria and analyze the associated distribution correlations that quantify the amount of proppant that migrates into the secondary fractures.
To model the complex interactions between proppant particles, fracturing fluids, and fracture walls, we use the discrete element method (DEM) coupled with computational fluid dynamics (CFD). We calibrate our model using widely accepted bed-load transport measurements. The simulation domain involves a "T-type" intersection of primary and secondary fractures. We investigate the effects of various proppant sizes and concentrations on bridging formation. In all cases, we investigate the occurrence of bridging and we quantify its impact by estimating the corresponding percentage of proppant reaching the secondary fractures.
Our simulation results show that the efficiency of proppant placement in the secondary fractures depends on the flow regime. In the suspension regime, proppant particles can be easily mobilized by the fluid drag force. This leads to a relative high proppant placement efficiency in the secondary fractures. When proppants are in the bed-load transport regime, kinetic energy transferred from the fluid drag force is dissipated by inter-particle collisions and the friction force. In this case, the amount of proppants entering the secondary fractures and the distance that proppants can cover are restricted compared to the case of proppants associated with suspension transport.
Our investigation reveals that two parameters are critical for the occurrence of proppant bridging (blocking) at the secondary fracture interface. These parameters are — the proppant concentration
In this work, we assess the historical well performance for a mature gas condensate field in Oman (the field name is designated as "BHA," where "BHA" is a pseudonym). The reservoirs of the BHA field are complex and have low permeabilities which results in substantial uncertainty in reserves estimation, which in turn has resulted in regular modifications of the booked volumes. To confine these booked volumes, we employed two techniques: "time-rate" analysis (or Decline Curve Analysis (DCA)) and "time-rate-pressure" analysis (or Rate Transient Analysis (RTA)). To perform the decline curve analysis work we used Microsoft Excel to match data using both the Modified Hyperbolic (MH) and Power-Law Exponential (PLE) DCA relations. We also used the Kappa Engineering product "Topaze" to conduct the Rate Transient Analysis (or RTA) by first estimating reservoir parameters and then performing a simulation history match of both the rate and pressure data. The DCA and RTA models were both used to construct a 30-year forecast and 30-year EUR values were obtained using these forecasts. Finally, we created parametric correlations using estimated reservoir properties from RTA and the matched parameters obtained using the MH and PLE relations for DCA. The core purpose of this work was to provide assurance in the booked reserves volumes for these low permeability reservoirs and to obtain correlations of reserves and reservoir property estimates for fields like the BHA field.
Blasingame, Thomas (Texas A&M University) | Olorode, Olufemi (Afren Resources) | Odunowo, Tioluwanimi Oluwagbemiga (Texas A&M University) | Moridis, George (Lawrence Berkeley National Laboratory) | Freeman, Craig Matthew (Texas A&M University)
Copyright 2013, Society of Petroleum Engineers This paper was prepared for presentation at the Unconventional Resources Conference-USA held in The Woodlands, Texas, USA, 10-12 April 2013. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Low to ultralow permeability formations require "special" treatments/stimulation to make them produce economical quantities of hydrocarbon and at the moment, multistage hydraulic fracturing (MSHF or MHF) is the most commonly used stimulation method for enhancing the exploitation of these reservoirs. Recently, the slot-drill (SD) completion technique was proposed as an alternative treatment method in such formations (Carter 2009).
Oil and gas reserves estimates that honor disclosure requirements of the US Securities and Exchange Commission (SEC) are critically important in the international oil and gas industry. Unfortunately, a number of exploration and production (E&P) companies have allegedly overstated and subsequently written down certain reserves volumes in recent years. In some cases, the consequences have been quite adverse. We document some of these cases of reserves overstatements and summarize the consequences. Reserves write downs are of obvious interest to numerous groups involved in the reserves estimation process and outcome, including estimators, managers, investors, creditors, and regulators. The magnitude and nature of recent overstatement cases, relative unfamiliarity with the SEC's inner workings, and the SEC's new reserves-reporting requirements increase the need to examine critically reserves disclosures and reserves overstatements.
The analysis of production data to determine reservoir characteristics,completion effectiveness, and hydrocarbons in place has become very popular in recent years. Although production analysis (PA) for reservoir characterization is approaching the popularity of pressure-transient analysis (PTA), there are few consistent diagnostic methods in practice for the analysis of production data.Many of the diagnostic methods for production-data analysis are little more than observation-based approaches--and some are essentially rules of thumb.
In this work, we provide guidelines for the analysis of production data, as well as identify common pitfalls and challenges. Although PTA and production-data analyses have the same governing theory (and solutions), we must recognize that pressure transient data are acquired as part of a controlled experiment, performed as a specific event [e.g., a pressure-buildup (PBU) test]. In contrast, production data are generally considered to be surveillance/monitoring data--with little control and considerable variance occurring during the acquisition of the production data. We note that since both PA and PTA have the same governing relations, it is possible "in theory" that the same deliverables of PTA can be obtained using PA.
This paper attempts to provide a state-of-the-technology review of current production-data-analysis techniques/tools--particularly tools to diagnose the reservoir model and assess the reservoir condition. The reservoir model is diagnosed mainly by examining the character exhibited by the data [that is the evidence of transient flow (e.g., quarter-slope might indicate a finite-conductivity fracture, or half-slope might indicate radial/pseudoradial flow)]. In addition, one can also assess the reservoir condition by inspecting the character of production data, which can confirm the evidence of boundary-dominated flow such that unit slope may indicate the boundary-dominated-flow regime and, therefore, in-place fluid volume can be estimated.
This work also identifies the challenges and pitfalls of PA--and we try to provide guidance toward best practices and best tools. To complement this mission, we use relevant field examples to address specific issues, and we illustrate the value and function of production-data analysis for a wide range of reservoir types and properties. In this work, we propose the use of a sequence of raw and enhanced data plots for the diagnostic analysis of production data. We strongly believe that a comprehensive and systematic approach for production-data diagnosis has significant importance for the analysis and forecast of production performance.