Population Of Wells Research Articles

Purging and other sampling variables affecting dissolved methane concentration in water supply wells

Determining whether changes in groundwater methane concentration are naturally occurring or related to oil and gas operations can be complicated by numerous sources of variability. This study of 10 residential water supply wells in Northeastern Pennsylvania evaluates how i) sampling from different points within the water well system, ii) purging different water volumes prior to sampling, and ii) natural variation over time, affects concentrations of naturally occurring dissolved methane and other water quality parameters. Among the population of wells, all had dissolved methane concentrations >1mg/L. Regardless of the volume of water purged or the timing between events, the maximum change in methane concentration (ratio of maximum to minimum concentration) among samples from a single well was 3.2, with eight out of ten wells exhibiting a maximum change less than a factor of two (i.e., <±100%). Among water wells where methane concentration changed by ±50% or more, there was a strong correlation with changes in the concentrations of sodium, chloride, and other salinity indicators such as specific conductivity and TDS. This suggests that significant variability in methane concentration is predominantly related to changes in the relative volumes of sodium-rich fluids feeding the wellbore at any given time. Among study well locations with bladder and diaphragm pressure tanks, there was no significant difference in dissolved methane concentrations between samples collected either upstream or downstream of a pressure tank. There appears to be little benefit to purging multiple casing volumes of water from a well prior to sampling because such volumes tend to be much larger than those representative of normal residential use. We recommend purging a volume sufficient to remove standing water in the pressure tank and lines above the pump intake. This article culminates with additional recommendations for improving sample collection methods and interpreting sampling data.

Unconventional Risk and Uncertainty: What Does Success Look Like?

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 179996, “Unconventional Risk and Uncertainty: Show Me What Success Looks Like,” by David S. Fulford, Apache, prepared for the 2016 SPE/IAEE Hydrocarbon Economics and Evaluation Symposium, Houston, 17–18 May. The paper has not been peer reviewed. This paper presents approaches for proper risking of uncertain recoverable volumes for an unconventional resource, taking into account the chance of false positives from appraisal-well information. A subjective risk tolerance can be included to respect how aggressive or conservative a company may be in pursuit of a project. A method of recasting production profiles is demonstrated as an improvement over the common method of scaling, or factoring, the initial production rate. Introduction Onshore unconventional liquids-shale and gas-shale plays are often referred to as “continuous accumulations” or “resource plays.” The characterization of perceived geologic continuity frames perception of these plays as having little to no risk, to the point that “well manufacturing” is a noncontroversial term used to refer to development of these assets. Exploration-and-production companies often tout a “type well” as an indication of the viability of their assets. However, a type well, by definition, represents the mean of a population of wells. Not all individual wells must be commercial in order for the mean well to be commercial. The well population is something that cannot be predicted, but only estimated. This implies uncertainty. However, the argument that there is risk remains to be made. Regardless of the cause of this no-risk assumption, the tools exist to address uncertainty. Empirical modeling must follow from first principles. This applies not only to the evaluation of individual wells to determine their ultimate recovery but also to the statistical models built to judge the economic value of drilled wells. In the analysis of small sample sizes (and assuming independent and identically distributed samples), the variance is always overestimated. The cause of this phenomenon lies within the roots of regression analysis—or, more accurately, in the assumptions made during the application of regression analysis.

Appraising Unconventional Resources: How Many Wells To Drill and Where To Place Them?

Summary Appraisal programs undertaken by exploration and production (E&amp;P) companies are designed to resolve subsurface uncertainties that contribute to uncertainty in the economic potential of undeveloped fields. Value-of-information (VOI) assessments allow E&amp;P players to quantify the economic value of their proposed appraisal programs before carrying them out. This study proposes a VOI methodology that is tuned to the nature of the subsurface uncertainties in unconventional plays and is capable of assessing a wide range of appraisal strategies (defined as the number and configuration of wells). It addresses two main problems. The first is how to characterize the uncertainty (in a play) that an appraisal program is intended to reduce or resolve. The second is how to cast that characterization of the uncertainty in a VOI context so that the merits of various appraisal programs can be evaluated. This paper characterizes the subsurface uncertainty that arises because of inadequate sampling of natural geologic variability. In this work, three quantities are assumed to be uncertain: the mean and standard deviation (SD) (variability) of the expected ultimate recovery (EUR) of the population of wells to be drilled, should development go ahead, and the range of the variogram that describes the spatial correlation of EUR as a function of the distance between wells. The optimal appraisal program would presumably depend on the true values of these quantities. The methodology is illustrated by application to a typical unconventional play. The VOI of an appraisal program can be optimized in terms of the number of the appraisal wells to be drilled and the placement of those wells. This VOI increases as the placement of the set of wells is changed from being clustered in the central part of the appraised area to approaching uniform distribution across the area. To obtain the optimal well placement, the incremental learning from changing the well locations should be balanced against the incremental costs that are required to increase the spacing of the appraisal wells. The study results demonstrate how the VOI of each incremental appraisal well decreases with the number of appraisal wells and how an optimal number of appraisal wells can be determined.

Repeat Sampling and Coliform Bacteria Detection Rates in New Jersey Domestic Wells

In compliance with the New Jersey Private Well Testing Act, 78,546 wells (93,787 samples, including samples from 13,290 wells that were analyzed more than once) were analyzed for total coliform (TC) bacteria by one or more of 39 laboratories over a 10-year period. Samples containing TC bacteria were further analyzed for the presence of either fecal coliform or E. coli (FC/EC) bacteria. The large population of wells sampled multiple times permitted a systematic study of the effect of repeat sampling on coliform bacteria detection rates. The detection rate increased with the number of times wells were sampled. In bedrock, TC bacteria were detected in 21% of the population of wells analyzed only once, 33% in the population sampled twice, and 43% in the population sampled three times. It was estimated that TC bacteria would be detected in 90% of all wells if each well was analyzed 10 times. For FC/EC bacteria, it was estimated that 21 and 68 samples, respectively, would be required to reach the 50% and 90% population detection rates. In the Coastal Plain (CP), many more samples would be required to achieve the same estimated population detection rates. The population detection rate estimates were also dependent on the type of method used, the pH of the well water, and the geologic formation in which wells were located. A single sample was not sufficient to detect coliform bacteria when present in well water.

DETECTING CILANGES IN THE SPATIAL DISTRIBUTION OF NITRATE CONTAMINATION IN GROUND WATER1

ABSTRACT: Many studies of ground water pollution in general and nitrate contamination in particular have often relied on a one‐time investigation, tracking of individual wells, or aggregate summaries. Studies of changes in spatial distribution of contaminants over time are lacking. This paper presents a method to compare spatial distributions for possible changes over time. The large‐scale spatial distribution at a given time can be considered as a surface over the area (a trend surface). The changes in spatial distribution from period to period can be revealed by the differences in the shape and/or height of surfaces. If such a surface is described by a polynomial function, changes in surfaces can be detected by testing statistically for differences in their corresponding polynomial functions. This method was applied to nitrate concentration in a population of wells in an agricultural drainage basin in Iowa, sampled in three different years. For the period of 1981–1992, the large‐scale spatial distribution of nitrate concentration did not show significant change in the shape of spatial surfaces; while the magnitude of nitrate concentration in the basin, or height of the computed surfaces showed significant fluctuations. The change in magnitude of nitrate concentration is closely related to climatic variations, especially in precipitation. The lack of change in the shape of spatial surfaces means that either the influence of land use/nitrogen management was overshadowed by climatic influence, or the changes in land use/management occurred in a random fashion.