Generative Models for Production Forecasting in Unconventional Oil and Gas Plays

Kuzma, Heidi Anderson; Arora, Neha; Farid, Kemal

doi:10.15530/urtec-2014-1928595

Cited by 5 publications

(2 citation statements)

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“…BetaZi (BZ) is a physio-statistical production forecasting algorithm which is gaining industry acceptance while being used as a forecasting engine for several commercial products [Kuzma et al, 2014]. The algorithm combines a physical model with a set of statistical distributions learned from big datasets.…”

Section: Physio-statisticsmentioning

confidence: 99%

Performance Evaluation of Automatically Generated Statistical Type Curves

Kuzma¹,

Conradson²

2015

SPE Annual Technical Conference and Exhibition

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Predicting the future production of Proved UnDeveloped (PUD) reserves is an important part of the evaluation of any asset. In the absence of production history, the most reliable way to make a PUD forecast is by analogy with production histories from similar nearby completions. This is most often done using a type curve. A traditional type curve is generated by normalizing to one the production histories of analogous wells and finding the shape of a curve which best fits their average decline. This is a tedious process when done by hand, and results are so difficult to test that in practice they rarely are. Our study introduces a methodology for testing statistical type curves based on 5-fold cross validation (5FXV), which rigorously evaluates the ability of an algorithm to produce accurate type curves, giving confidence that those type curves can be used to make careful and informed decisions. We offer two case studies to demonstrate the strength of the method and introduce a useful extension of the method.A physio-statistical algorithm named BetaZi (BZ) automatically generates type curves which are considerably richer than a simple shape. They include information about actual production ranges abstracted as percentiles (90% of the time, actual production from a new well is expected to exceed the BZ p90 bound; 50% of the time it should exceed the p50, and so on.) 5-fold cross validation (5FXV) is used to test the performance of the algorithm. In 5FXV, a random 10% of the data from analogous wells is held out for testing and the remaining 90% is used to generate a type curve. A quantile-quantile (Q-Q) evaluation is made which compares the number of times the test data exceeded the bounds predicted by the type curve. The process is repeated 5 times on different subsets of data.For this paper, we use 5FXV to estimate type curves for several plays in the Bakken and Kansas. The results show that excellent performance can be achieved if enough analogous wells are available to generate the type curve. Type curves that are made with wells that are not analogous can lead to overinflated estimations of value, as is shown with the Kansas wells. Finally, a p-score is computed for each well using the type curve, allowing production from wells of different histories to be conveniently plotted on one map or cross-plotted with other parameters.

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Section: Physio-statisticsmentioning

confidence: 99%

Performance Evaluation of Automatically Generated Statistical Type Curves

Kuzma¹,

Conradson²

2015

SPE Annual Technical Conference and Exhibition

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“…Mollaiy and Shahbazian (2011) proposed a new method based on a feed-forward artificial neural network and imperialist competitive algorithm to predict the oil flow rate of wells. Kuzma et al (2014) constructed a prediction model that can capture oil and gas seepage rules using a small amount of actual information combined with a generative model and statistical methods to make accurate predictions based on production experience. Jia and Zhang (2016) applied time series analysis and neural network models to forecast the future production of a gas well in the Barnett shale field, and compared it with the Arps decline curve.…”

Section: Introductionmentioning

confidence: 99%

Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Xue,

Wang,

Han

et al. 2023

Adv. Geo-Energy Res.

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Big Data Every Day: Predictive Analytics Used to Improve Production Surveillance

Raphael

Fuge

Gutierrez

et al. 2015

Day 1 Tue, March 03, 2015

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Terabytes of data are being collected every day in the oilfield. Intuition suggests that the more data the better and that if a little bit of information is valuable, then a lot must be incredibly more so. And yet, is it? It is not the data itself, but the ability to put it toward some useful task which matters. One of the most concrete ways in which Big Data can be brought to bear on the everyday is through predictive analytics, the science of using facts from the past to analyze the present and predict the future. In the oil field, a simple and powerful way of making use of data and predictive analytics is for automatic surveillance by exception. By learning from the past it is possible to predict what should be happening today and compare the prediction to what is actually going on. If the two do not match then a problem has been identified and a solution found. Addressing problems early is one of the cheapest ways to boost production.

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Generative Models for Production Forecasting in Unconventional Oil and Gas Plays

Cited by 5 publications

References 0 publications

Performance Evaluation of Automatically Generated Statistical Type Curves

Performance Evaluation of Automatically Generated Statistical Type Curves

Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Big Data Every Day: Predictive Analytics Used to Improve Production Surveillance

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