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The first objective of this work is to determine the volume of hydrocarbon that can be moved from Resources other than Reserves (ROTR) to Reserves, or from Proved Undeveloped Reserves (PUD) to Reserves based on well placement. The second objective is to create a model that incorporates the production history and forecasted estimated ultimate recovery (EUR), in this case by implementing multi-segment decline curve analysis (DCA) as presented in URTeC 336 (
The first portion of this work involves running a sensitivity analysis to determine the spatial well relationships that may trigger movements in certain regulatory frameworks. A successful well may promote the offsetting 2P wells to PUD wells. We incorporate the methodology in
In the second part of this work, we create a model which includes the production history and the forecasted EURs. As time moves forward, continuity and consistency must be maintained across the model. Assume the following scenario: we plan to move a volume "
This paper provides planners with a methodology (or tool) that will allow evaluators to progress resources from classifications with lower chances of commercially (COC) to classes with higher chances of commercially (top sub-classes of Reserves) and also to progress resources from categories with large uncertainty to categories with less uncertainty of eventual recovery. This is important to entities of all sizes for planning purposes because companies should track their resources regardless of project stage or size. Our methodology provides continuous tracking of volumes when moving from Prospective Resources to Contingent Resources to Reserves throughout the life of the project, and allows for more accurate Reserves reporting.
We begin this work with the relationship between the Reserves categories in the PRMS matrix, modeled using the Gaussian Quadrature (GQ) presented in SPE 195480 by
We then develop functional relationships across the vertical elements of the PRMS matrix by simulating event-variant movement across categories. Resources move on a time basis, and the rate of movement differs for different classes and categories. We implement the COC presented by
Based on our results, the uncertainty of the relative weights of Contingent and Prospective Resources categories increases as we move down the PRMS matrix, so as we incorporate this uncertainty and the weights differ slightly from those estimated for Reserves (presented in SPE 195480). We also note that the COC is user-defined for every project, so the proposed relationships will differ for every project The time-rate of movement between categories also differs for every project; there is no "one-size-fits-all" solution. The COC changes for each project because the risks differ in each project and it is at the engineer's discretion to use the appropriate COC
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.
This paper presents a methodology that provides the upstream oil and gas industry with a robust approach to petroleum inventory management. More specifically, we describe the proper order of movements of resources from Prospective Resources to Contingent Resources, to Reserves (and back). Our methodology describes the "causes of change in classification" and what these changes mean when classifying Reserves as well as "resources other than reserves" (ROTR).
We begin with the updated PRMS and COGEH documents (2018). We then define the three steps that are necessary to move through Prospective Resources before we can begin moving into the sub-classes of Contingent Resources. We define the movement for Prospective Resources to become discovered, making these Prospective Resources become Contingent Resources. We define the progression, following discovery, in chance of development and commerciality in the project maturity sub-classes within the Contingent Resources classification. We define the criteria for a technology to become "established" and explain that these technologies must be technically reliable and economic before they can be used for development decisions. Lastly, we define the contingencies and the movement through each contingency for the volumes to become Reserves.
The main results we provide in this work are that the movement from Prospective Resources to Contingent Resources to Reserves cannot be defined by straight movements from one class to another. This means that there is no direct path that can be used to define the movement from Prospective to Contingent Resources, though there are certain criteria that must be met regardless of the project. We also show that certain contingencies, such as price of oil and available technologies, dominate the movement of resource volumes.
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)
Low to ultralow permeability formations require "special" treatments/stimulation to make them produce economical quantities of hydrocarbon and at the moment, multi-stage 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).
This paper documents the results of a comprehensive numerical simulation study conducted to evaluate the production performance of the SD technique and compare its performance to that of the standard MSHF approach. We investigated three low permeability formations of interest, namely a shale-gas, a tight-gas, and a tight/shale-oil formation. The simulation domains were discretized by using Voronoi gridding schemes to create representative meshes of the different reservoir and completion systems modeled in this study.
The results from this study indicated that the SD method does not, in general, appear to be competitive in terms of reservoir performance and recovery compared to the more traditional MSHF method. Our findings indicate that the larger surface area to flow that results from the application of MSHF is much more significant than the higher conductivity achieved using the SD technique. However, there may exist cases, e.g., lack of adequate water volumes for hydraulic fracturing, or very high irreducible water saturation that leads to adverse relative permeability conditions and production performance, in which the low-cost SD method may make production feasible from an otherwise challenging (if not inaccessible) resource.
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.