
 Forecasting production from unconventional reservoirs is a slippery slope that could lead to unrealistic results even though a model has been history matched with production history and vetted by experienced engineers. The reasons for failing conventional decline curves lie in convoluting and heterogeneous reservoir properties, advancing drilling and completion techniques, and dynamic production and operations management. However, the initial rates, declining rate, and ultimate recovery of a well can be viewed as relatively static and predetermined properties of a declining profile. This paper will propose a machine learning-based framework to determine these properties for unconventional reservoir development.
 In the proposed algorithm, instead of directly data mining on the raw data from different categories and scales, we propose to convert these data into dimensionless variable groups to reduce the dimension of the problem. The dimensionless variables are developed using inspection analysis; most have physical meanings and are easy to upscale. In the case study, we used the production, completion, and petrophysical data to generate new type curves and developed a step-by-step process to explain the aspect of "engineering" code that incorporates physics into the machine learning (ML) process.
 Dimensionless variables are used in the machine learning process giving physical meaning and reducing the number of predictors, thus improving the speed and efficiency of the code. The results show that the quantity of cumulative oil production over time can be determined using machine learning models with R2 >= 0.90 for individual wells and R2>=0.80 for cross-validated cumulative production forecasts. We can use these determined values to assess the quality of initial rates, declining rates, and ultimate recovery to derive new type curves that incorporate physics and engineering practices. The work emphasizes the importance of accounting for completion parameters, fluid properties and rock quality, thus improving the confidence in results obtained through traditional engineering methods. The machine learning model results provide credibility and support to rates and recoveries for DCA forecasted wells. When modeling hundreds if not thousands of wells, this work shows the importance of utilizing machine learning to harness the power of the data that has been collected on them.
 The machine-learning-based declining profile is a promising technique and has some advantages over the classical methods based on averaging historical data. First, the determining parameters are highly scalable for newly drilled wells as the main input parameters are dimensionless variables derived from reservoir properties and well completions. Secondly, this algorithm explores not only the production data but also reservoir properties and completion data to capitalize on the advancing techniques.

Deriving New Type Curves through Machine Learning in the Wolfcamp Formation


 Generating production-type curves for new horizontal wells in unconventional reservoirs is an evolving process that requires continuous calibration to maintain the most accurate forecast over time. History matching production alone is no longer sufficient to maintain such models. Obstacles to creating production type curves are attributed to the complexities in heterogeneous reservoir properties, improved drilling and completion techniques, and evolving production and operation procedures. This paper will highlight improvements to a proposed machine-learning algorithm to generate production type curves for new wells in oil and gas unconventional reservoirs.
 The algorithm utilizes dimensionless groups created from the raw data in different categories and scales, thus reducing the dimension of the problem, decreasing the processing time, and improving the efficiency of the machine-learning model. The dimensionless groups are developed using inspectional and dimensional analysis depending on the data available for feature inputs. Many of the dimensionless groups have physical meanings and can be upscaled. We advanced the ability of the previously developed algorithm utilizing production, completion, and petrophysical data from both oil and gas reservoirs to generate new type curves by using the "engineering" code that was laid out in our previous case study. The algorithm incorporates physics into the machine learning (ML) process supporting the outputs with math and science.
 When using multiple reservoirs from different formations in the data, the algorithm utilizes logic in the code to determine between oil and gas wells. The quality of the results is impacted when using data from reservoirs with phase envelopes that are not similar, for example, a heavy oil and a dry gas reservoir. The algorithm is updated to include logic that can determine the major phase to predict oil and gas production more accurately. The quantity of oil and gas production is more accurately predicted using cumulative production rates rather than over time. The machine learning model maintains an R2 >= 0.8 when cross-validating both cumulative oil and gas production. The algorithm consistently predicts cumulative production over time on test data with R2 >=0.8. The predicted rates for new type curves are compared to conventional production type curves, thus validating the quality and goodness of fit for production rates, decline profile, and ultimate recovery. The results demonstrate how late-time production can be either extrapolated using the machine learning algorithm or combining traditional methods by utilizing hyperbolic and exponential declines where training data is unavailable for the machine learning model to perform late-time forecasting.
 The algorithm of the ML model is proving to be a supplementary tool when generating new production type curves. The speed and efficiency provide support to the DCA generated type curves. It is versatile in its ability to combine data from multiple formations and discern between the major phase, thus providing production type curves we have confidence. The scalability of the dimensionless input parameters can account for changes in completions and reservoir properties within minutes of updating the database hence providing insight in near real-time for engineers.

Generating New Production Type Curves through Machine Learning Utilizing Dimensional Analysis


 Hydrocarbon production optimization is essential in pursuing the best scenarios for economic outcomes. But because of complex and multi-dimensional nature of production processes, thousands of scenarios are possible. Extensive data collection may allow uncovering patterns still unidentified. With on-site computing power increasing, cloud availability, and artificial intelligence evolution, mathematical optimization methods are becoming powerful and accessible. Data type-tailored models are implemented for history matching and prediction of operational efficiency of the asset. This paper presents a comprehensive analysis and comparison of three data type-tailored reservoir modeling methods and their optimization process for waterflooding field cases.
 The mathematical techniques used were Data-Driven Capacitance Resistance Model (CRM), Numerical Simulators (Data-Physics) coupled to Smart Algorithms Optimizers, and Hybrid Model (Machine Learning Physics-Based). They were compared to 1-identify the benefits of mathematical optimization techniques, 2-illustrate the methods developed to sort out time and computing capacity restrictions, and 3-validate the techniques by comparing the forecast with actual results.
 The six study cases of different reservoir types in Argentina, Venezuela, and the USA, had different types data availability. Four had no static model. In two cases, field results were available to confirm the accuracy of the forecasted injection and production. The forecasted increase in Net Present Value (NPV) and cumulative oil production (Np) ranged to 30%, and optimized water injection rates decreased by 50%.
 Traditional modeling techniques yielding unreliable result in one field with hundreds of producing layers and unknown lateral and vertical continuity were solved using a machine learning technique. In some cases, they pointed toward non-intuitive infill drilling sequence and injection water redistribution. Also, they pointed to options that reduce economic risk. The methods yielded many better economic scenarios and increased the flexibility of operationalizing plans. In one field requiring excessive computing power, using time horizons reduction and successive year-by-year optimization yielded 4 times the NPV of the base case. This approach solves objections related to long computing time and system instability. With the three mathematical techniques, the asset value could be continually maximized by a novel implementation of a heuristic decision-making approach that continuously challenge the current scenario. It makes a systematic formulation of conceivable new scenarios, competing through an objective function determining the probity of compared scenarios.
 The optimization also resulted in an up to 50% decrease in water injection requirements and the same percentual CO2 emissions reduction.

Comparative Analysis of Data-Driven, Physics-Based and Hybrid Reservoir Modeling Approaches in Waterflooding


 Various types of predictive models have been applied over the years to make quantitative decisions for unconventional development plans. These models are either very simple (e.g., type-curves) which ignore the reservoir physics or are too complex (e.g., simulation models) to be able to run for an entire field efficiently. In this paper, we propose a model for design, prediction and optimization of unconventional wells efficiently using a combination of reservoir physics with machine learning methodologies. The proposed model is the amalgamation of the state-of-the-art in machine learning and reservoir physics into a seamless full field model. The physical model ensures that model predictions are always realistic and reliable while the machine learning algorithm allows us to utilize different types of data to make a prediction which cannot be directly integrated into the physical model. The model uses a probabilistic approach to estimate P10-P50-P90 production curves to account for uncertainty in predictions.
 The data from more than 1800 unconventional wells in a real field is used to train and test our proposed model. The input features are completion design parameters like lateral length, proppant concentration, well spacing, etc., and the output in a full time series of expected oil production from the well. The results show that our modelʼs prediction leads to correlations of more than 0.75 for the test set which is indicative of its good predictive accuracy. The sensitivity analysis of the parameters of the model on the cumulative production shows that volume of injection fluid, length of the lateral and the proppant concentration are among the most important parameters.

Combining Machine Learning and Physics for Robust Optimization of Completion Design and Well Location of Unconventional Wells


 As the oil and gas industry embarks on the path to energy transition, pressure from government regulators, investors, and the public in general demand that companies have clear and transparent net-zero goals and that their operational initiatives and plans support such transition efforts. Mature fields present an opportunity to increase production through operational optimization, which at the same time, can also lead to greenhouse gas (GHG) emissions efficiency.
 This paper presents the application of a novel modeling and optimization technique in a mature waterflood environment. Data Physics is the amalgamation of the state-of-the-art in machine learning and the same underlying physics present in reservoir simulators. These models can be created as efficiently as machine learning models, integrate all kinds of data, and can be evaluated orders of magnitude faster than full scale simulation models, and since they include similar underlying physics as simulators, they have good long term predictive capacity and can even be used to predict performance of new wells without any historical data. The technology was applied to a mature field in the Neuquen basin in Argentina to effectively reduce the amount of water injected into the reservoir with no negative impact on the production. Additionally, a new Carbon Intensity (CI) modeling tool was used to compare the emissions intensity before and after optimization showing a significant improvement in CI achieving three objectives in one single decision: 1) obtain significant water injection reduction with its corresponding impact in injection and water treatment costs; 2) maintaining production compared to the initial decline of the field, improving the top line; and 3) improving the GHG emissions intensity hence the long term benefit to the environment.
 The paper deals more with the implementation of the technologies than the technologies themselves, assuming that readers unfamiliar with both Data Physics and Carbon Intensity tools will refer to the references section to gain familiarity with these.

Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity

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Combining Machine Learning and Physics for Robust Optimization of Completion Design and Well Location of Unconventional Wells

Optimizing a Waterflood Using a Combination of Machine Learning and Reservoir Physics. A Field Application for Reducing Fresh Water Injection with no Impact on Oil Production and Improved Carbon Intensity