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Application of Multi-Well Normalization of Open Hole Logs in Integrated Reservoir Studies
Aly, Ahmed M. (S.A. Holditch & Associates, Inc.) | Hunt, Ercill R. (S.A. Holditch & Associates, Inc.) | Pursell, David A. (S.A. Holditch & Associates, Inc.) | McCain, William D. (S.A. Holditch & Associates, Inc.)
Abstract Well logs are the foundation on which characterization of layered reservoirs is based. However, multi-well normalization of the well logs is necessary to reduce the probability of major errors and inconsistencies in the results of the log analysis. If not removed, these inconsistencies will cause failure in any attempt to integrate log analysis results with core, well test, and production data. This paper presents a comprehensive procedure for multiwell normalization of well logs. Use of this method will ensure that the well log data can be used effectively in reservoir characterization. The advantages of multi-well normalization are illustrated in three examples of integrated reservoir studies. Example 1 illustrates the multi-well normalization process using both histograms and M-N crossplots to verify the normalization. Example 2 from a 300 well reservoir study shows the use of multi-well dual-porosity crossplot with the histogram normalization. Example 3 introduces the benefits of multi-well normalization for permeability calculations from well-logs and the effect of removing the log errors in developing a relationship between the log response and permeability. This example also illustrates the integration between well-logs and core data. Introduction Practice in using well log analysis as a part of integrated reservoir studies has shown that for the results to be accurate, consistent and comparative well-to-well, the log data require corrections' with a process called multi-well normalization. This process ensures that each logging tool reads the correct values, i.e., that the density tool accurately records formation density, the neutron log accurately reflects hydrogen index, and so forth. Experience indicates that approximately 65 to 70 percent of gamma ray logs, 50 percent of density logs, 40 to 50 percent of neutron logs, and five to 10 percent of sonic logs require some normalization to correct for variances in field calibrations of the logging tools. The normalized well log data can be effectively integrated, correlated, and calibrated with core data. The resulting correlations can be extended vertically to include layers which were not cored and laterally from well-to-well across the study area. The difference in the scale of measurement of the two sets of data must be taken into account. The core data have a scale of few cubic inches, while the log data have a scale of a few cubic meters and well test data have a scale of a few acres. Table 1 shows that even among the log measurements there are different volumes of investigation for the different tools. Multi-Well Normalization Multi-well normalization is a key activity to ensure accurate and consistent results from a multi-well log analysis study. Normalization is an iterative process that uses three tools; histograms, crossplots, and depth-based logs. These tools may be applied in the rock layers being analyzed or in nearby layers. In a marine environment of deposition, the nearby or interbeded shales may be consistent enough over a large area for normalization purposes. For instance, the Bossier shale in East Texas can be used in the normalization process. In many basins, there are often enough very low porosity carbonates with sufficient areal extent to be useful in the normalization process. This is especially true in evaporate deposition cycles. When sandstone is abundant such as in the Travis Peak & Cotton Valley of the East Texas, Prairie du Chein of Michigan, Lorelle formation of Australia, the Miocene reservoirs of the Gulf of Suez in Egypt, and many others, the sandstone itself can be used in the normalization process. P. 139^
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.58)
AVO crossplotting has been widely used in the past few years as a way of deriving improved hydrocarbon indicators Castagna and Smith (1994) gave an alternative definition of from seismic data.
Squeezing Blood From a Stone โ Distinguishing Incremental From Accelerated Recovery in Moderate to Tight Gas Infill Development Using Production Data Only
Wong, J. R. (Santos Ltd.) | Shrivastava, R.. (Advanced Well Technologies) | Headland, M. K. (Santos Ltd.) | Chipperfield, S. T. (Santos Ltd.) | Blasingame, T. A. (Texas A&M U.)
Abstract With increasing demand for natural gas, higher product prices and the availability of improved extraction technologies, there is increasing focus on infill drilling in tight gas reservoirs. However, quantification of incremental recovery (one of the key value generators) is often challenging particularly in commingled multi-layered heterogeneous fluvial reservoirs with a paucity of data collection. The issues related to the volume and quality of data collected are magnified when low cost infill development is undertaken. This paper demonstrates a new technique using production data (in isolation) to estimate incremental and accelerated recovery for such a development. For infill development, various methods have been proposed to quantify incremental recovery. The most common range from simple reservoir continuity models, Arps Decline Curves, Material Balance, field analogue studies, to complex reservoir simulation. This paper discusses a new methodology termed โProgressive Multi-well Blasingame Analysisโ based on Blasingame type curves which successively compares boundary-dominated responses from each infill phase to distinguish incremental from accelerated recovery achieved from each phase of infill development. The paper reviews the theoretical support for the Progressive Multi-well Blasingame Analysis method via a numerical simulation study. Demonstration of this methodology is performed using a field case study to quantify incremental recovery and identify additional infill opportunities in an environment where limited reservoir surveillance was conducted. The paper concludes by discussing the applicability and the pros and cons of this technique. In essence, this paper addresses an existing knowledge gap in industry as it provides a method to efficiently evaluate a group of wells and distinguish incremental from accelerated recovery using only the production rate and flowing tubing head pressure data.
- Research Report (0.48)
- Overview > Innovation (0.48)
- Geology > Sedimentary Geology > Depositional Environment > Continental Environment > Fluvial Environment (0.48)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Oceania > Australia > South Australia > Cooper Basin (0.99)
- Oceania > Australia > Queensland > Cooper Basin (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- (23 more...)
- Information Technology > Modeling & Simulation (0.70)
- Information Technology > Data Science (0.48)
Introduction Summary Several studies have shown the benefits of using rock physics depth trends in exploration areas. This study combines rock physics depth trends with modified forms of well known resistivity models, to establish resistivity depth trends. We present a workflow to extrapolate observed resistivities to scenarios not encountered in the well. The modelled resistivities can be used to reduce uncertainty in EM investigations, by providing realistic constraints for the background resistivity model. In resent years there has been an increasingly focus on the electrical properties of rock in the subsurface. Advancing Electromagnetic (EM) technology enables resistivity recordings which have large potential for separating hydrocarbons from brine and background shale in reservoir rocks. But some of the first enthusiasm has eased, and there has been increased awareness about limitations and ambiguities using the technology. These limitations are partly connected to lack of knowledge about which resistivities to expect in brine filled and hydrocarbon filled formations. It is impossible to identify anomalous resistivities from an EM investigation if the background resistivity trend is completely unknown, since resistivity anomalies must have a reference resistivity to deviate from. Various authors have addressed the coupling between seismic and electrical rock properties (e.g. Hacikoylu et al., 2006 and Brevik et al., 2009). This study aims to improve resistivity predictions in areas with sparse well information. We use rock physics depth trends to estimate elastic properties of sands and shales in a large depth range. Then the rock physics depth trend is connected to the resistivity depth trend, which enables predictions of resistivity in sands and shales in a much larger depth interval than originally observed in the well log. This can provide useful constraints on the background model when performing controlled source electromagnetic studies. Rock physics depth trends Rock physics depth trends can be valuable for extrapolating elastic properties from a well to surrounding area Figure 1 illustrates a typical situation where we only have well control of a small part of the possible scenarios we can encounter. A proper understanding of the depth trends in the well area should be established before predictions of elastic properties in new environments are performed. Modelled depth-trends improve understanding of observed trends, and can serve as a tool for identifying anomalies like overpressure, unexpected lithologies or hydrocarbons in the subsurface. The trends can also be used to aid interpreters by predicting expected seismic contrasts with depth, in AVO analysis (Avseth et al., 2003), and to constrain seismic inversion (Rimstad & Omre, 2009). The deviations from the porosity trends are some places large on log scale, but the model is meant to capture the large scale trend. This study focuses on shales and sands with or without quartz cement. Calcite cement is known to not follow depth trends, so anomalously low porosity sands are not allowed to influence the estimated porosity-depth trend. The deviations from the porosity trends are some places large on log scale, but the model is meant to capture the large scale trend.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
Introduction The paradigm of American energy is undergoing a massive transformation. In addition to the evolution of renewable energy technologies, and the changes now being driven by the consideration of climate issues and other environmental factors, the oil and gas opportunities that are now evolving in the United States could be the key factor in the country's successful transition to full energy independence. We are finally in a position, thanks not only to rich product reserves in the lower 48 states but also new technologies that allow us to fully exploit these reserves, to meet all of the United States' energy needs, both today and well into the future. At the heart of this opportunity in oil and gas are directional drilling techniques and multi-well site plans. These off-shore-born approaches to upstream production have come onshore in recent years and are proving to be true game changers in terms of field efficiency and cost savings.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs (1.00)
- Facilities Design, Construction and Operation > Processing Systems and Design > Separation and treating (0.69)
- Health, Safety, Environment & Sustainability > Sustainability/Social Responsibility > Sustainable development (0.54)
- (2 more...)