A Study of Terminal Decline Rates of Oil & Gas Wells
A Study of Terminal Decline Rates of Oil & Gas Wells

PAYNE INSTITUTE COMMENTARY SERIES: COMMENTARY
June 9, 2025
SUMMARY
In the United States alone, the Environmental Protection Agency estimates that there may be nearly 4 million inadequately decommissioned oil and gas wells. These wells and associated surface facilities represent material health and safety risks for people and nature in their immediate surroundings. They also collectively are a significant contributor to greenhouse gas emissions, a driving force behind global warming[1].
A climate mitigation business model is emerging in which a project developer undertakes to properly plug a leaking abandoned wellbore (or a leaking orphaned well, which is a subset of abandoned wells that lack a viable owner) to stop its fugitive emissions, specifically methane, as well as leaks of brine or other hazardous fluids. These developers are establishing value for the work associated with this plugging (and land reclamation) via private sources of capital, including in the voluntary carbon offset market.
Funding from voluntary carbon markets is generally tied to the ability of a project developer to make a reasonable, specific claim as to the volume of methane that will be prevented from leaking by plugging a particular well. In other words, this private finance is directly tied to a $ per unit of avoided methane emissions.
The purpose of this study was to consider a critical element in the determination of how a project developer should calculate the future avoided emissions from plugging the well. Just as a flowing well would be expected to produce less hydrocarbons every year of its productive life, so too should an abandoned well be expected to emit less every year. In other words, the volume of emissions avoided by proper plugging of a well should be in part determined by the well’s expected natural rate of production decline.
The oil and gas industry’s 100+ years of production from wells has fostered an understanding of wells’ production trends. In a well’s early years of production, the decline rate is considered to be hyperbolic and is defined in the industry with the use of the “Arps” equation. With a hyperbolic decline curve, however, the rate of decline is assumed to fall continuously over time and would, eventually, approach a flat, or zero, rate. By virtue of operator experience, and to maintain a more conservative stance with respect to the economic outlook for wells, the Oil & Gas industry converts the basis for forecasting production to assume that the decline curve shifts from hyperbolic to exponential — and as such that the decline rate becomes constant and is assumed to remain at that rate for many years (or even decades).This decline profile forecasted and/or observed in the latter stage of a well’s productive life is known as “Terminal Decline.”
Calculating the expected decline of a specific well typically requires knowledge about the reservoir being accessed by the well as well as its production history. Yet this information is not always available —particularly for wells that have been abandoned or orphaned. This is even more true for wells that have been leaking after abandonment as there is almost certainly no record of how much methane was emitted in that period.
This study of well decline rates considered whether there is enough consistency in the decline behavior across onshore wells in the U.S. — particularly during the period of Terminal Decline that is most likely to be relevant for orphan well crediting projects — that generalized decline rates could be developed to serve as a reasonable proxy for any well’s behavior. If so, these proxy decline rates could offer an orphan well project developer the basis to make a reasonably conservative calculation of the future avoided emissions volume of a plugged well by measuring point in time rates taken present day and applying those declines forecasts over a period of years into the future.
For this study, the Payne Institute for Public Policy conducted a systematic review of decline rates for more than 70,000 wells across the United States. This dataset was reduced to ~14,000 wells by applying the following filters: (1) at least 30 years of continuous recorded production history (although many wells are known to have produced hydrocarbons for much longer, we established that 30 years was more than adequate to establish terminal decline rates and sought to retain a very large dataset); (2) periods of no longer than six consecutive months in which the wells were off-line (i.e. not producing) within that 30 year period; (3) production was weighted to natural gas (a Gas-to-Oil ratio (GOR) of 5 thousand standard cubic feet produced per barrel of oil, which would make them more prone to having material methane emissions if inadequately plugged, and only natural gas production was included in our analysis; and (4) production could not, on average, rise over any of the three periods.
An initial pass was made to understand the point in time at which Terminal Decline begins. The dataset was reviewed by assessing the (unweighted) average annual decline rates across all the wells to determine natural “breakpoints” in which the rate of change of the decline rates showed a meaningful variation (based on an assessment of the second derivatives of year over year changes in production). In our dataset, those breakpoints pointed to three periods: Period 1 (years 1-4), Period 2 (years 5-9), and Period 3 (years 10-30). We believe the first period is consistent with hyperbolic decline and that the third period is consistent with the idea of Terminal Decline Rate; the second period is arguably a blend of the two as it is reasonable to expect that there is some transition from hyperbolic to exponential/terminal decline.
The study’s results focus is on Period 3, i.e., the period we most closely associate with Terminal Decline for the wells in the dataset (although results are provided for the other periods for information purposes). In Period 3, the average annual decline rate, as determined by regression analysis performed on the aggregated production of all ~14,000 wells in the dataset, was 6.65%. The average annual decline rate in Period 3 across the wells was 6.4% and the range of average annual decline (considered by the 80th and 20th percentile wells) was 2.9% and 9.4%, respectively (see Table 1).

a Calculated as exponential decline through each period
b For discussion of R-Squared vs. RMSE, please see the “Study Methodology: Assessing Quality of Fit” section
c 20% of the wells have a faster decline rate than the 20th percentile well
d 20% of the wells have a slower decline rate than the 80th percentile well
Source: Payne Institute; well data from Enverus
To assess whether other factors influence decline rates, the study looked at whether rates changed materially if the wells in the data set were segmented in different ways, including flow rate (size), region (basin) and vintage year (year of first production). This segmentation analysis yielded what we suggest are not meaningful differences given a reasonable tolerance. For example, the average annual decline rate in Period 3 was lowest for the smallest flow rate segment (<10,000 cubic feet per day, or 10 MCFD), and in the Appalachian basin at 4.9%, respectively, and highest for a portion of the Permian basin (“Permian – Other”) at 7.7% (see Tables 2-4 in the Results section for all segment average results). We submit that both are not too dissimilar from the dataset average of the 6.4% noted above that was the average across all wells.
We conclude that the wells studied exhibit a relatively narrow band of mean decline rates and that such consistent decline behavior is not overly impacted (higher or lower) by well size, basin and year of first production. Therefore, it is reasonable to use existing historical production decline rates as a meaningful predictor for any onshore well’s future behavior/potential emissions from leakage in the United States.
INTRODUCTION
Oil and natural gas wells tap into stores of hydrocarbons deep underground. Pressure conditions in the subsurface provide “drive” to bring some of those hydrocarbons to the surface. Wells produce hydrocarbons in this manner for multiple decades. Indeed, there are examples of wells that have produced hydrocarbons for over 100 years.[1] Over time, however, the production from every oil and gas well is expected to decline as this drive lessens.
Estimating the rate of production and decline over time for any given well has varying degrees of certainty. A reasonable expectation has been developed, underpinned by several decades of oil and gas operations study that has fostered an understanding of well behavior and production declines. Decline curve analysis and widespread oil and gas operator experience has established that wells’ production declines are more aggressive earlier on in their productive lives, and then typically fall over time until they eventually settle to a fixed value – a “Terminal Decline Rate”.
An operator generally stops production from a well, a process known as abandonment, when it is uneconomic to continue production (i.e. when the costs to sustain production are greater than the revenue being generated by the produced hydrocarbons). There are rules in every state requiring a well’s operator to appropriately decommission a well when production stops. However, there are vast numbers of wells, by some estimates as many as 4 million, that remain unplugged or have been plugged without following appropriate plugging procedures.
Wells that have not been properly plugged will leak methane, a potent greenhouse gas. Given the vast number of abandoned and orphaned wells leaking methane in the United States today, and the negative impact this has and will continue to have on climate change, there is an immediate need to plug these wells. Public funds and resources to plug them are woefully inadequate, leading to an emerging interest to tap into private financial sources and voluntary carbon markets to tackle the problem. It is thought likely that such financial sources would evaluate “investing” in plugging abandoned and orphaned wells based on their climate mitigation potential, i.e. how much greenhouse gas emissions could be avoided through the plugging activity.
A project developer in this space must necessarily estimate the avoided methane emissions from plugging a well. Measurements can be taken at the point in which a plugging project is initiated. Once a well is plugged, however, it is not possible to take any further measurements and avoided emissions must be forecasted.
Estimating these avoided emissions can be greatly assisted by techniques such as decline curve analysis. Petroleum engineers can estimate with a high degree of certainty what the production volume would have been had a well continued to produce naturally — or how much natural gas a leaking well will continue to emit if the well is left in its unplugged state. By analyzing the decline behavior of the more than 14,000 wells in our dataset, this study considers the premise that there is enough similarity in decline behavior of wells in the United Stats that production history for those wells can be used to establish decline rate assumptions – especially for the period of Terminal Decline – that can be reasonably applied to any conventional (vertical) well to estimate it’s future emission volume. Such decline assumptions could then be used to predict, with a high degree of certainty, the climate mitigation impact of plugging these orphaned or abandoned wells.
DECLINE CURVE ANALYSIS
Most analysis of the productivity of oil and natural gas wells starts before the well is even drilled, with the objective of determining the economic potential of the future production profile. As wells are drilled, their production trend provides the opportunity to refine the views that were developed earlier. Said differently, a well’s production informs what can be expected if the well is to be produced going forward. Similarly, a well’s current rate of emissions can be the basis for that wells emissions potential into the future.
Arps Curve and Terminal Decline Rates
In 1944, J.J. Arps published what is now called the Arps Equation to describe and predict the production rate of a producing well under constant bottomhole flowing pressure. It was derived from empirical observations of oil wells under certain conditions but has been found to have wide applicability to many reservoirs, particularly “tight” and unconventional formations.
Arps Equation: Qt = Q0 / (1 + bDt)
Qt = Production rate at time t
Q0 = Initial production rate
b = the decline exponent, also called the “b-factor.”
D = Initial decline rate
t = time
Arps’ “b-factor” is assumed to be constant over the life of the well. A constant b-factor (between 0 and 1) results in a continually decreasing (assumed) decline rate. Operator experience, however, has suggested that it is prudent to take a more conservative approach to estimating future value as the well settles into its most predictable stage. Instead of continuing to approach zero, or a no-decline forecast, a consensus has emerged that the decline should be held constant over time as the well moves towards the end of its life. In the SPEE’s 2021 Annual Parameters Survey, over 90% of the respondents said they always or usually use the Arps equation for production forecasting for the initial and medium term of a well’s life. Most switch to exponential decline to capture later-life behavior with an assumed terminal decline rate of 4%-7%[2].
Said differently, most reservoir engineers believe that, although the “hyperbolic” behavior predicted by the Arps equation accurately reflects a wells potential production, it should be shifted to a more conservative exponential decline profile for a portion of a well’s productive life so as not to over-estimate future potential. This constant exponential decline is escribed the “terminal decline” or “Dmin”. (To tie terminal decline back to the Arps equation, it would be the equivalent of the b-factor equaling 0.)
Despite its wide usage, there are some caveats about the concept of terminal decline in a well’s natural state. First, it hasn’t been derived analytically in real-world situations, so if it occurs, it must be observed. There is also no known physical reason why wells must switch from hyperbolic to exponential decline. In other words, there is no clear point at which it is understood that the well decline will “switch” to an exponential rate[3].
Second, an analysis of terminal decline is hard because many wells are impacted by efforts through the course of a well’s life to stimulate more production out of the reservoir. Such stimulation, which commonly occurs early in a well’s development in the form of hydraulic fracturing and later in the form of artificial lift alter the decline trajectory of a well, brings production “forward” relative to how the reservoir would produce it naturally.
Third, questions surrounding the use of terminal decline haven’t been terribly relevant when assessing the economics of a producing well. The assumed value of the terminal decline typically has a small impact on the present value of future reserves[4]. And as wells get older, new forecasts tend to be generated that use the most recent production behavior and may indicate a low, but non-zero b-factor, eliminating the need for a terminal decline assumption.
STUDY METHODOLOGY
The Dataset
Using Enverus’ Prism platform, the study accessed the monthly natural gas production history for wells across four basins in the United States (Anadarko, Appalachian, Permian and Denver-Julesburg (DJ)). These basins were selected due to their large natural gas production history and the prevalence of orphan wells within them. From a total of over 70,000 wells in these basins, additional conditions were established, which filtered the data set to 14,107 wells (the “Dataset”). These conditions, and the reasons for them, include:
- Each included well must have had a minimum of 360 months of production history.
- This period was intended to ensure a large number of wells would be included and that each included well offered a “complete” production history, through to Terminal Decline. It was not intended to suggest that wells only produce for 30 years. Many wells produce for longer.[5] But where that is the case, production history after 30 years as not included in our dataset.
- Each included well must have had no more than six continuous months of zero production during any of the initial 360 months of activity.
- This parameter was designed to ensure that [mechanical disruptions to a wells production profile were not falsely shallowing the decline profiles by including zero/null production months]. For clarity, wells with multiple months of zero production, lasting less than six continuous months, were included.
- Each included well must have been a vertical well.
- This was done to avoid the potentially distorting effects of unconventional/horizontal developments that are not representative of the traditional production profile most often observed in abandoned and orphaned wells.
- Each included well had a Gas-to-Oil ratio (GOR) of 5 thousand standard cubic feet produced per barrel of oil over the full lifecycle of the well’s production history.
- This overrides what can be somewhat arbitrary classifications within Enverus (or by the States) between oil wells and natural gas wells. Most oil-producing wells turn gassier through their productive lives (i.e. their GORs rise over time). It is presumed that any fugitive emissions from abandoned or orphaned wells will be natural gas.
- Wells were excluded if they experienced production growth, on average, in any of the three Periods.
- This was designed to have wells excluded that underwent significant change — most likely from human intervention such as recompletion or secondary/tertiary recovery methods — that clearly deviate from a well’s natural pressure regime.
- We acknowledge that this condition only partially excludes human intervention. First, we understand that wells included in the dataset easily could have undergone some form of artificial lift during the first 30 years of their productive lives even if the average production declined over one of our three Periods. Second, by tying our calculations to our periods, we imposed an arbitrary cutoff that may have missed wells for which production rose across a period, just not across one of our “Periods” (e.g., an increase from year 3 to year 6 but not from year 4 to 9).
Determining the “Periods”
Monthly production data was downloaded from Enverus for each well in the Dataset and then normalized to date of first production and summed to calculate annual production by well. Decline rates were calculated by determining the best fit assuming an exponential decline rate. And then the decline rates were used to “break up” the 30 year production history into three periods using a “Binary Segmentation” analysis. This analysis derives break points based on identifying significant differences in annual decline rate trends (with the parameters set to find only two break points and thus to have three periods). The binary segmentation process is iterative; it identifies likely breakpoints through an assessment of the change in rate of change (i.e., the second derivative) and then compares actual decline rates against that predicted by the trend line of the newly created period on either side of the breakpoint. The periods derived through this exercise came after year 4 and after year 9; thus the periods for analysis were assigned to be years 1-4 (Period 1), years 5-9 (Period 2) and Years 10-30 (Period 3).
Annual decline rates were then calculated for the Dataset for each Period, summing annual production for each well (i.e. summing year 1 production from all the wells, then summing year 2 production for all the wells and so on) and then deriving the “best fit” exponential decline rate across each period for these aggregated volumes. Another way to think of this is that these three averages reflect a volume-weighted average decline rate across the Dataset.
Segmentation Analysis
The Dataset was then further segmented — by size (average flow rate), geography (producing basin) and vintage (the year of completion) — and average (exponential) decline rates were calculated for each segment for the three Periods referenced above. This segmentation was performed to explore if wells with certain characteristics behaved, on average, differently than others. If meaningful differences existed within these segments, it could point to the need to reflect this segmentation in crediting methodologies.
Segment average decline rates reflect an average of each well’s “best fit” exponential decline rate within each segment. Thus the average annual decline rate of 4.9% for wells below 10 MCFD (see Tables 2 and 3), for example, reflects the average of the 1,710 wells’ decline rates in that size range.
Assessing Quality of Fit
To assess the “quality of fit” of our regression equation, we show two metrics: R2 and the root mean squared error (RMSE) in Table 1 (in the Executive Summary). R2 is, of course, more commonly used. However, in our view, R2 is not very informative in this analysis. With only a handful of points per Period (because we are summing the wells’ production in each year and thus fitting an aggregate “well’s” annual production) and a log transformation that linearizes the decline, R² is inflated — it approaches 1.0 simply because there is very little variability for the model to explain. In other words, a high R² in this case does not reflect true error in predictions; it says nothing about the magnitude of the residuals and can remain high even if the model has only modest predictive ability.
On the other hand, RMSE provides a more direct and interpretable measure of fit quality. RMSE quantifies the typical prediction error in the same units as the data (in this case BCF/day), making it easy to gauge how far off the model’s estimates are in practical terms. The RMSE for Period 1 (years 1–4) is 0.085 BCF/day, meaning the model’s predicted gas production deviates by about 0.085 billion cubic feet per day on average, or 1.4%, from observed values. Period 2 (years 5–9) has a RMSE of 0.003 BCF/day — a virtually zero error, which seems to speak to the very small change in decline in that interval (i.e., the model had little variation to account for). Period 3 (years 10–30) has a RMSE of 0.012 BCF/day, or 1.1%. In other words, the error for all three periods is very small, suggesting that the exponential model captures the long-term decline consistently and accurately.
RESULTS
The results of the calculations/analysis, along with the calculated correlation are represented in the following tables:
Figure 1: Best Fit by Period vs. Production
Table 2: Total and Flow Rate Segment Results
Table 3: Basin Segment Results
Table 4: Vintage Segment Results
Figure 2: Distribution of wells by Flow Rate Segment

Source: Payne Institute; well data from Enverus

a Calculated as exponential decline through each period
b Calculated based on the average flow rate over each well’s 30 year life, i.e. not volume-weighted
Source: Payne Institute; well data from Enverus

a Calculated as exponential decline through each period
b Calculated based on the average flow rate over each well’s 30 year life, i.e. not volume-weighted
Source: Payne Institute; well data from Enverus

a Calculated as exponential decline through each period
b Calculated based on the average flow rate over each well’s 30 year life, i.e. production is not aggregated
Source: Payne Institute; well data from Enverus

Source: Payne Institute; well data from Enverus
DISCUSSION
The study results were consistent with our expectations for hydrocarbon well behavior and common practice within the Oil and Gas industry. First, “later life” well behavior, at which point decline behavior can be associated with Terminal Decline, appears to become prevalent by around years 9 or 10 (following initial years of more aggressive decline). Second, the annual (exponential) decline rates across the ~14,000 well dataset appears to coalesce to a narrow mid-to-upper single digit percentage range. This narrow range appears to affirm that a (single) proxy for a given well’s terminal decline rate can be reasonably derived from the dataset. In other words, the results appear to support the idea that U.S. onshore oil and gas wells settle into, more or less, a similar decline rate, regardless of when the wells were drilled (which might have impacted drilling and completion techniques) and where they are located (that geology might play a significant role).
These conclusions are largely based on the following:
First, the wells exhibited significant reduction in annual decline rates through 30 years of production, with the average of the mean annual decline rates across the wells in the study falling from 27.0% in Period 1 to 14.2% in Period 2 to 6.4% in Period 3 (see Tables 1 and 2). The average in Period 3 is consistent with oilfield industry “consensus” on such rates as noted in the Decline Curve Analysis section.
Second, this range of decline rates (around the mean) also gets “tighter” per Period. Using the 20th and 80th percentile wells as illustration, the range in Period 1 is 27.4% percentage points, falls to 14.8% percentage points in Period 2 and falls again to 6.5% in Period 3 (see Tables 1 and 2). Perhaps this is to be expected given that the average rate is falling, but the fact that it does bolsters a sense that a “terminal” decline rate can be approximated across the universe of U.S. of aging or abandoned conventional wells.
Third, the average of the mean decline rates for different segments of the data set also shows consistency. This consistency was greatest for Vintage, which ranged from an average of 6.0% per year for wells first producing between 1980 and 1989 to 6.9% for wells drilled after 1990 (see Table 4). However, the spread in averages by Basin, which ranged from 4.9% in Appalachia to 7.7% in Permian – Other (see Table 3), and by Average Period Flow Rate, which ranged from 4.9% for the smallest well category (<10 MCFD) to 7.3% for the largest (>100 MCFD) (see Table 2), can also be considered to be narrow, particularly relative to the spread in averages for these segments in the earlier Periods.
CONCLUSION
The current landscape of abandoned and orphaned wells in the U.S. presents health and environmental risks and a collective climate impact. The numbers of such wells are also poised to grow, given the current inventory of wells with small production volumes that are likely reaching their natural economic life. If private finance through carbon offset credits is going to play a role in plugging these wells, it is important that we find a credible and conservative method for estimating the methane emissions that will be avoided by carbon project plugging activities.
This study demonstrates that the estimate of future emissions for an individual well that is left unplugged can be empirically based — by looking at the experience of production from thousands of wells across the United States. Indeed, our analysis confirmed that the decline behavior across various basins and a large volume of wells can be reasonably represented in a “type curve” that lands within a 4.9-7.7% terminal decline rate range around ten years after production began. (It is worth noting that this is consistent with oilfield reservoir engineers’ collective perception of terminal decline rates.)
This observation lends confidence that one can generalize about the potential emissions profile of a well that has not been decommissioned adequately (or at all), even if relatively little is known about that well or its production history. The consistency of well behavior — certainly after the most aggressive periods of production decline (i.e., after ~10 years) but even arguably when the wells are younger — allows for the idea of selecting annual exponential decline rates that can be used for carbon credit issuance purposes.
ACKNOWLEDGMENTS
The Payne Institute would like to acknowledge the financial support provided by Tradewater, LLC and Rebellion Energy Solutions. Their contributions were used to pay students to conduct the research underlying this paper.
We would also like to acknowledge that Rebellion brought forward the initial hypothesis on terminal decline rates and encouraged a scientific evaluation of the concept of terminal decline.
While Tradewater and Rebellion provided support for this paper, the study itself was conducted independently by the Colorado School of Mines. Our analysis, methodology, and conclusions belong to the Colorado School of Mines alone.
References
[1] Source: U.S. Environmental Protection Agency, EPA 430-R-24-004 | Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2022.
[2] One example is the McClintock Well #1, which has produced continuously since 1861
[3] Society of Petroleum Evaluation Engineers | SURVEY OF ECONOMIC PARAMETERS USED IN PROPERTY EVALUATIONS
[4] Many researchers point out that it’s more accurate to create production forecasts with multiple segments using different (declining) b-factors, and that the final segment’s b-factor may lie in the 0.3 to 0.5 range. The challenge with this approach is the additional time required to identify flow regimes, determine the decline parameters and build the forecasts. Identifying the flow regimes also requires enough production history (often in offset wells) to have observed them. Hart Energy | Terminal Decline’ in Decline Curve Analysis of Oil Wells; Steve Hendrickson, Ralph E. Davis Associates; 7/28/21.
[5] This is particularly true in current unconventional developments, for which the completion design, including very large hydraulic fracturing jobs, brings the vast majority of the production forward into the first several years of production.
[6] As considered in the Discussion section below, it appears that larger wells with production history in excess of 30 years may not have reached their Terminal Decline rate state as quickly as smaller wells, meaning that including them in this dataset resulted in higher (more conservative) average Terminal Decline rates than we would have seen had we included the additional years of production beyond 30.

ABOUT THE AUTHORS
Brad Handler, Payne Institute Program Director, Energy Finance Lab, and Researcher
Brad Handler is a researcher and heads the Payne Institute’s Energy Finance Lab. He is also the Principal and Founder of Energy Transition Research LLC. He has recently had articles published in the Financial Times, Washington Post, Nasdaq.com, Petroleum Economist, Transition Economist, WorldOil, POWER Magazine, The Conversation and The Hill. Brad is a former Wall Street Equity Research Analyst with 20 years’ experience covering the Oilfield Services & Drilling (OFS) sector at firms including Jefferies and Credit Suisse. He has an M.B.A from the Kellogg School of Management at Northwestern University and a B.A. in Economics from Johns Hopkins University.
Vandan Bhalala, MS Petroleum Engineering, Colorado School of Mines
Vandan Bhalala is a student researcher at The Payne Institute at the Colorado School of Mines. Currently pursuing an M.E. in Petroleum Engineering, His work centers on addressing methane emissions from orphaned oil and gas wells, with a focus on decline curve analysis (DCA), production forecasting, and reservoir engineering. With a B.Tech in Petroleum Engineering specializing in upstream exploration from UPES, Dehradun, Vandan joined The Payne Institute in January 2024. Passionate about sustainable practices in energy, I aim to leverage my expertise to contribute to responsible resource management and mitigation of environmental impacts within the petroleum sector.
Liam O’Byrne, BS Computer Science, Colorado School of Mines
Liam O’Byrne is a student researcher at the Payne Institute. He is pursuing his B.S. in Computer Science at the Colorado School of Mines.
Jim Crompton, Affiliate Professor, Petroleum Engineering, Colorado School of Mines
Jim Crompton retired from Chevron in 2013 after 37 years with the major international oil & gas company. After moving from Houston to Colorado Springs, Colorado, Jim established the Reflections Data Consulting LLC to continue his work in data management and analytics for Exploration and Production industry. From 2019 to 2023, Jim was a teaching faculty member and a Professor of Practice in the Petroleum Engineering Department lecturing on petroleum data analytics and the Digital Oilfield 2.0. He retired from active teaching in 2023 but still continues as a guest lecturer in several courses.
Jim was a Distinguished Lecturer for the Society of Petroleum Engineers in 2010-2011 speaking on the topic of “Putting he Focus on Data”. In 2024 he received a “Distinguished Member” award from the SPE. He is a frequent speaker a SPE conferences on digital/Intelligent Energy and the Data Foundation. His interests lie in the full spectrum of the information value chain from data capture, data management, data visualization, data access modeling and analytics, simulations, and serious gaming.
Jim graduated from the Colorado School of Mines (BS in Geophysical Engineering in 1974 and MS in Geophysics in 1976) before joining Chevron in Denver, Colorado. He later earned an MBA degree (1976) from Our Lady of the Lake University in San Antonio Texas.
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