Tight Oil Wells Research Articles

Evaluation of recoverable reserves based on dynamic data for horizontal wells in unconventional oil reservoir

Unconventional reservoirs are often exploited by horizontal well volumetric fracturing. Recoverable reserves are an important basis for evaluating the development effect of oil reservoirs. In this study, an evaluation index of recoverable reserves was proposed which based on the mathematical statistics method. A mathematical model for the index and the standardized calculation process were established. The dynamic data in the study was from the RA tight reservoir of Songliao basin. The results show that: (1) Horizontal wells production can be categorized into three distinct groups based on daily oil production and cumulative oil production (Np), showing no obvious "L"-shaped decrease. (2) A power function is presented by the correlation between Np logarithm (logNp) and production time, with a distinct inflection point in the curve. As the evaluation index of recoverable reserves, the Np corresponding to the inflection point is taken. (3) After verification, the derivative value of logNp corresponding to the inflection point is approximately 0, and the derivative parameter m represents the characteristics of different regions. Statistically, the study area m is 0.0003. (4) The recoverable reserves evaluation indexes of all known horizontal wells that can reflect the real production capacity were calculated. The recoverable reserves of the three blocks in the study area were successively S1 block, S3 block and S2 block from high to low. (5) According to the relationship between cumulative oil production, initial daily production and the evaluation index, the evaluation index is only suitable for the characterization of recoverable reserves in the middle and late production of tight oil wells, but not suitable for the characterization of recoverable reserves in the early production of wells.

A New Approach to Apply Decline-Curve Analysis for Tight-Oil Reservoirs Producing Under Variable Pressure Conditions

Summary Decline-curve models inherently assume that the bottomhole flowing pressure (BHP) is constant. This is a poor assumption for many unconventional wells. For this reason, the application of decline-curve models might lead to incorrect flow regime identification and estimated ultimate recovery (EUR). This work presents a novel technique that combines variable BHP conditions with decline-curve models and compares its results with traditional decline-curve analysis (DCA) for both synthetic and tight-oil wells. Using superposition, we generate a synthetic rate example using the constant-pressure solution of the diffusivity equation for a slightly compressible fluid (decline-curve model) along with a BHP history. However, we validate the technique using bottomhole and initial reservoir pressures that contain errors. The algorithm consists of three sequential optimizations. In each optimization, the algorithm estimates (1) the decline-curve model parameters, (2) the BHP, and (3) the initial reservoir pressure. The result of the synthetic example leads to an accurate production history match and corrected estimates of the initial reservoir pressure and the BHP history. Finally, we compare the results of the technique with traditional DCA in terms of (a) the model parameters, (b) flow regime identification, (c) production history matches, and (d) EUR for tight-oil wells using three decline-curve models: 1D single-phase constant-pressure solution of the diffusivity equation for a slightly compressible fluid, logistic growth model, and Arps hyperbolic relation. For the synthetic case, the algorithm estimates the model parameters and the true initial reservoir pressure within a 2% error. In addition, the method regenerates the true BHP history and provides an excellent production history match. The analysis of the tight-oil wells shows that the new approach clearly identifies the flow regimes present in the well, which can be difficult to detect using traditional DCA when the BHP varies. In contrast, the application of traditional DCA shows considerable errors in the estimation of the model’s parameters and a poor history match of the production data. Finally, this work shows that incorporating variable BHP into the decline-curve models leads to more accurate production history matches and EUR values compared to using only rate-time data. This paper illustrates a workflow that incorporates variable BHP conditions for any decline-curve model. Moreover, the approach can handle errors in both the BHP and the initial reservoir pressure and provides corrected estimates of these variables. The technique is computationally fast and history matches and forecasts the production of unconventional wells more accurately than traditional DCA. The major contribution of this work is the remarkable simplicity yet robustness of our solution to variable-pressure DCA. Finally, we developed a web-based application to provide the readers with a hands-on experience of this new technique.

An approximate analytical model for unconventional reservoir considering variable matrix blocks and simultaneous matrix depletion

In regard to unconventional oil reservoirs, the transient dual-porosity and triple-porosity models have been adopted to describe the fluid flow in the complex fracture network. It has been proven to cause inaccurate production evaluations because of the absence of matrix–macrofracture communication. In addition, most of the existing models are solved analytically based on Laplace transform and numerical inversion. Hence, an approximate analytical solution is derived directly in real-time space considering variable matrix blocks and simultaneous matrix depletion.To simplify the derivation, the simultaneous matrix depletion is divided into two parts: one part feeding the macrofractures and the other part feeding the microfractures. Then, a series of partial differential equations (PDEs) describing the transient flow and boundary conditions are constructed and solved analytically by integration. Finally, a relationship between oil rate and production time in real-time space is obtained.The new model is verified against classical analytical models. When the microfracture system and matrix–macrofracture communication is neglected, the result of the new model agrees with those obtained with the dual-porosity and triple-porosity model, respectively. Certainly, the new model also has an excellent agreement with the numerical model. The model is then applied to two actual tight oil wells completed in western Canada sedimentary basin. After identifying the flow regime, the solution suitably matches the field production data, and the model parameters are determined. Through these output parameters, we can accurately forecast the production and even estimate the petrophysical properties.

Evaluation of Fracture Volume and Complexity of Tight Oil Wells Based on Flowback Data

For tight reservoirs, horizontal wells and multi-stage fracturing can generate a complex fracture network that realizes economic and effective development. The volume and complexity of the fracture network are of great significance to accurately predicting the productivity of tight oil wells. In this work, a mathematical model of a multiphase flow is proposed to evaluate the stimulation effect based on the early flowback data. The model showing the early slope of the material balance time (MBT) and production balance pressure (RNP) can help estimate the effective stimulated volume of the horizontal well. The linear flow region can be determined from the slope of the log–log plot of the MBT versus RNP curve, which equals 1. The method is verified by commercial simulation software, and the calculated stimulated volume is consistent with the statistical results of simulation results. Results also show that the flow pattern of the fracture–matrix system can be judged by the slope of the flowback characteristic curve in the early stage of flowback, and then the complexity of the fracture network can also be obtained. The proposed method can provide an avenue to evaluate the fracturing work using the flowback data quickly.

Sensitivity and application of pseudo-steady-state constant for refracturing horizontal wells with fracture reorientation in anisotropic tight oil reservoirs

Fetkovich or Blasingame type rate decline analysis is a common and practical method to obtain reservoir parameters and evaluate well productivity. Pseudo-steady-state constant is an indispensable parameter for establishing these new type rate decline curves and works as a bridge linking conventional productivity and new type productivity. Refracturing is widely used to enhance tight oil wells’ productivity and improve their economic benefits, the pseudo-steady-state constant of refracturing horizontal wells has been presented in our previous research, but an in-depth discussion on the definition, accuracy, sensitivity, and application of this constant has not been conducted. It results in the insufficient understanding of the physical meaning, characteristics, and functions of pseudo-steady-state constant at present. In this study, taking the derived pseudo-steady-state constant for refracturing horizontal wells with fracture reorientation as an example, its accuracy was verified by an equivalent model presented in the literature, and the sensitivity of relevant key parameters on this constant was investigated. For the refracturing horizontal well defined in this study, pseudo-steady-state constant is independent of time, and related to fracture conductivity, fracture face damage, reorientation fracture number and permeability anisotropy. Results show that this constant decreases with the increase of fracture conductivity, but tends to remain unchanged when fracture conductivity increases to a certain extent. Meanwhile, this constant shows a positive correlation with fracture face damage and permeability anisotropy, but an inverse correlation with reorientation fracture number. Blasingame type rate decline curves of refracturing horizontal wells with fracture reorientation were also established, regarding as a practical application of this pseudo-steady-state constant and a concrete manifestation of its bridge-linking function. These type curves are directly conducive to the inversion of reservoir properties and fracturing parameters and the prediction of future productivity for refracturing horizontal wells. More importantly, this study is helpful to understand and strengthen the role and importance of pseudo-steady-state constant, and also beneficial to the establishment of new type rate decline curves of other similar models.

Using Bayesian Leave-One-Out and Leave-Future-Out Cross-Validation to Evaluate the Performance of Rate-Time Models to Forecast Production of Tight-Oil Wells

Summary Production forecasting is usually performed by applying a single model from a classical statistical standpoint (point estimation). This approach neglects: (a) model uncertainty and (b) quantification of uncertainty of the model’s estimates. This work evaluates the predictive accuracy of rate-time models to forecast production from tight-oil wells using Bayesian methods. We apply Bayesian leave-one-out (LOO) and leave-future-out (LFO) cross-validation (CV) using an accuracy metric that evaluates the uncertainty of the models’ estimates: the expected log predictive density (elpd). We illustrate the application of the procedure to tight-oil wells of west Texas. This work assesses the predictive accuracy of rate-time models to forecast production of tight-oil wells. We use two empirical models, the Arps hyperbolic and logistic growth models, and two physics-based models: scaled slightly compressible single-phase and scaled two-phase (oil and gas) solutions of the diffusivity equation. First, we perform Bayesian inference to generate probabilistic production forecasts for each model using a Bayesian workflow in which we assess the convergence of the Markov chain Monte Carlo (MCMC) algorithm, calibrate, and evaluate the robustness of the models’ inferences. Second, we evaluate the predictive accuracy of the models using the elpd accuracy metric. This metric provides a measure of out-of-sample predictive performance. We apply two different CV techniques: LOO and LFO. The results of this study are the following. First, we evaluate the predictive performance of the models using the elpd accuracy metric, which accounts for the uncertainty of the models’ estimates assessing distributions instead of point estimates. Second, we perform fast CV calculations using an important sampling technique to evaluate and compare the results of the application of two CV techniques: leave-one-out cross-validation (LOO-CV) and leave-future-out cross-validation (LFO-CV). While the goal of LOO-CV is to evaluate the models’ ability to accurately resemble the structure of the production data, LFO-CV aims to assess the models’ capacity to predict future-time production (honoring the time-dependent structure of the data). Despite the difference in their prediction goals, both methods yield similar results on the set of tight-oil wells under study. The logistic growth model yields the best predictive performance for most of the wells in the data set, followed by the two-phase physics-based flow model. This work shows the application of new tools to evaluate the predictive accuracy of models used to forecast production of tight-oil wells using: (a) an accuracy metric that accounts for the uncertainty of the models’ estimates and (b) fast computation of two CV techniques, LOO-CV and LFO-CV. To our knowledge, the proposed approach is novel and suitable to evaluate and eventually select the rate-time model(s) with the best predictive accuracy of models to forecast hydrocarbon production.

Development of Decline Curve Analysis Parameters for Tight Oil Wells Using a Machine Learning Algorithm

To obtain a reliable production forecast, one has to establish a geological model with well logs and seismic data. The geological model usually has to be upscaled using certain upscaling techniques. Then, a dynamic reservoir model is constructed with another dataset, including completion data, production data, fluid properties, and relative permeability curves. At last, the dynamic model needs to be validated by a history matching process. This approach is data-intensive, time-consuming, and often not rigorously accomplished due to the lack of skillset and time. In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. Overall coefficients of determination ( R 2 ) of the three Arps decline curve factors were 0.966, 0.990, and 0.945. The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. As a result of the ML-DCA regression model, a complete understanding can be established of the impact of reservoir properties on the DCA model. The proposed ML-DCA model not only provides a quick and robust method for petroleum engineers to estimate production performance for unconventional reservoirs from reservoir and completion properties without full-field geocellular modeling but also can be used to optimize the completion and operation parameters for wells of interest.

Fully coupled fluid-solid productivity numerical simulation of multistage fractured horizontal well in tight oil reservoirs

A mathematical model, fully coupling multiple porous media deformation and fluid flow, was established based on the elastic theory of porous media and fluid-solid coupling mechanism in tight oil reservoirs. The finite element method was used to determine the numerical solution and the accuracy of the model was verified. On this basis, the model was used to simulate productivity of multistage fractured horizontal wells in tight oil reservoirs. The results show that during the production of tight oil wells, the reservoir region close to artificial fractures deteriorated in physical properties significantly, e.g. the aperture and conductivity of artificial fractures dropped by 52.12% and 89.02% respectively. The simulations of 3000-day production of a horizontal well in tight oil reservoir showed that the predicted productivity by the uncoupled model had an error of 38.30% from that by the fully-coupled model. Apparently, ignoring the influence of fluid-solid interaction effect led to serious deviations of the productivity prediction results. The productivity of horizontal well in tight oil reservoir was most sensitive to the start-up pressure gradient, and second most sensitive to the opening of artificial fractures. Enhancing the initial conductivity of artificial fractures was helpful to improve the productivity of tight oil wells. The influence of conductivity, spacing, number and length of artificial fractures should be considered comprehensively in fracturing design. Increasing the number of artificial fractures unilaterally could not achieve the expected increase in production.

Techniques for History-Matching and Forecasting Tight Oil Reservoirs Compared

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTEC 208352, “Assessment of Rate-Normalization and Pressure-Deconvolution Techniques To History-Match and Forecast Production of Tight Oil Reservoirs Using a Physics-Based Rate-Time Model,” by Leopoldo M. Ruiz Maraggi, SPE, Larry W. Lake, SPE, and Mark P. Walsh, SPE, The University of Texas at Austin. The paper has not been peer reviewed. Rate transient analysis (RTA) is used routinely to analyze the production history of unconventional wells. Specifically, it is used to determine the time at the end of linear flow (time to depletion), estimate drainage volume, and predict the estimated ultimate recovery (EUR) of wells. RTA uses a rate-normalization technique to characterize how the well rate changes with a changing bottomhole flow pressure. However, rate normalization approximates a more-general technique known as pressure deconvolution. The complete paper evaluates and compares the performance of rate normalization and pressure deconvolution for both synthetic and tight oil examples. Overview The complete paper first presents the solution of the single-phase slightly compressible fluid-diffusivity equation for a certain set of idealized conditions. Second, the concepts of time superposition, convolution, deconvolution, and rate normalization are presented. Third, a numerical regularized exponential basis function inverse scheme is proposed for use in deconvolution. Fourth, the comparison of the rate-normalization and pressure-deconvolution techniques for a synthetic case are presented. Finally, the two methods are evaluated for tight oil wells. This synopsis will not include the many equations involved in these processes or detailed discussions thereof; instead, it concentrates on the work flow that the authors have developed, specifically its application to synthetic cases.

A Bayesian Framework for Addressing the Uncertainty in Production Forecasts of Tight-Oil Reservoirs Using a Physics-Based Two-Phase Flow Model

Summary Extrapolation of history matched single-phase flow solutions is a common practice to forecast production from tight-oil reservoirs. Nonetheless, this approach (a) omits multiphase flow effects that exist below bubblepoint conditions and (b) has not included the quantification of the uncertainty of the estimated ultimate recovery (EUR). This work combines a new two-phase (oil and gas) flow solution within a Bayesian framework to address the uncertainty in the EUR. We illustrate the application of the procedure to tight-oil wells of west Texas. First, we combine the oil and the gas flow equations into a single dimensionless two-phase flow equation. The solution is a dimensionless flow rate model that can be easily scaled using two parameters: hydrocarbon pore volume and characteristic time. Second, this study generates the probabilistic production forecasts using a Bayesian approach in which the parameters of the model are treated as random variables. We construct parallel Markov chains of the parameters using an adaptative Metropolis-Hastings (M-H) Markov chain Monte Carlo (MCMC) for this purpose. Third, we evaluate the robustness of our inferences using posterior predictive checks (PPCs). Finally, we quantify the uncertainty in the EUR percentiles using the bootstrap method. The results of this research are as follows. First, this work shows that EUR estimates based on single-phase flow solutions will consistently underestimate the ultimate oil recovery factors in solution-gas drives where the reservoir pressure is less than the bubblepoint. The degree of underestimation will depend on the reservoir and flowing conditions as well as the fluid properties. Second, the application of parallel Markov chains using an adaptative M-H MCMC scheme that addresses the correlation between the model’s parameters solves the issues of mixing and autocorrelation of Markov chains and, thus, it speeds up speeding up the convergence of the Markov chains. Third, we generate replicated data from our posterior distributions to assess the robustness of our inferences (PPCs). Finally, we use hindcasting to calibrate and strengthen our inferences. To our knowledge, all these approaches are novel in EUR forecasting. Using a Bayesian framework with a low-dimensional (two-parameter) physics-based model provides a fast and reliable technique to quantify the uncertainty in production forecasts. In addition, the use of parallel chains with an adaptative M-H MCMC accelerates the rate of convergence and increases the robustness of the method.

The Mechanism and Effect of Drilling Fluid Loss Damage on the Production of Tight Oil Wells

The Mechanism and Effect of Drilling Fluid Loss Damage on the Production of Tight Oil Wells

The paradox of increasing initial oil production but faster decline rates in fracking the Bakken Shale: Implications for long term productivity of tight oil plays

In the US, tight oil is the largest source of liquid hydrocarbons and has enabled the country to become the world's largest oil producer. The estimated ultimate recovery (EUR) and rate of production decline are key metrics in the evaluation of the future productivity of tight oil wells. We chose the Bakken Shale because of its high quality, publicly available data. Traditionally, well operators have estimated the EUR for each well from the initial production (IP), using empirical type curves to extrapolate to the ultimate production. From 2015 to 2018 the IP of the average Bakken well increased by approximately 50%. This increase resulted in claims by operators and in the academic literature that more intense hydraulic fracturing was increasing the average EUR of the Bakken play. At the same time, other observers claimed the wells declined much more rapidly than previous wells. This faster decline provided evidence for lower ultimate production from newer wells. The aim of this study was to understand the origin of these seemingly conflicting observations. A physics-based, scaling model was used to predict production from horizontal multi-stage fractured wells. The model for oil recovery is based on pressure diffusion through fractured porous media. The model assumes that the rock is incompressible, the permeability and oil saturation are constant, the water is incompressible, and the viscosity is slowly varying with space. The scaling model was applied to 13,444 wells. The EUR and terminal decline rate (TDR) were estimated from fitting production to our scaling model. Our study found that implementation of more intensive hydraulic fracturing resulted in higher IP and steeper terminal-production declines. Recently published results estimating the total production from the Bakken that include increased lifetime production commensurate with observed increases in average IP, significantly overestimate the long-term production potential of tight oil, both in the US and globally.

Influence of proppant physical properties on sand accumulation in hydraulic fractures

The proppant accumulation form in fractures is related to the formation conductivity after fracture closure, also closely related to the production rate of oil/gas wells. In order to investigate the influence of proppant physical properties on sand accumulation in fractures, a particle–fluid coupling flow model is established based on the Euler two-fluid model. Geometric parameters of a fracture in tight oil wells are approximately scaled in equal proportion as the physical model, which is solved by the finite volume method. And the model accuracy is verified by comparing with the physical experimental simulation in the literature. Results show that the higher proppant concentration corresponds to the faster particle sedimentation rate, and the greater sand embankment accumulation as well. However, the fluid viscosity will increase, inhibiting proppant migration to the deep part of the fracture. Reducing proppant density and particle size will enhance the fluidization ability of particles, which is conducive to the migration to the deep fracture at the initial stage of pumping. But, it is not beneficial to have a desirable accumulation state in the middle and later pumping stage, so it is difficult to obtain a higher comprehensive equilibrium height.

A Two-Phase Flow Model for Reserves Estimation in Tight-Oil and Gas-Condensate Reservoirs Using Scaling Principles

Summary A common approach to forecast production from unconventional reservoirs is to extrapolate single-phase flow solutions. This approach ignores the effects of multiphase flow, which exist once the reservoir pressure falls below the bubble/dewpoint. This work introduces a new two-phase (oil and gas) flow solution suitable to extrapolating oil and gas production using scaling principles. In addition, this study compares the application of the two-phase and the single-phase solutions to estimates of production from tight-oil wells in the Wolfcamp Formation of west Texas. First, we combine the oil and the gas flow equations into a single two-phase flow equation. Second, we introduce a two-phase pseudopressure to help linearize the pressure diffusivity equation. Third, we cast the two-phase diffusion equation into a dimensionless form using inspectional analysis. The output of the model is a predicted dimensionless flow rate that can be easily scaled using two parameters: a hydrocarbon pore volume and a characteristic time. This study validates the solution against results of a commercial simulator. We also compare the results of both the two-phase and the single-phase solutions to forecast wells. The results of this research are the following: First, we show that single-phase flow solutions will consistently underestimate the oil ultimate recovery factors (URFs) for solution gas drives. The degree of underestimation will depend on the reservoir and flowing conditions as well as the fluid properties. Second, this work presents a sensitivity analysis of the pressure/volume/temperature (PVT) properties, which shows that lighter oils (more volatile) will yield larger recovery factors for the same drawdown conditions. Third, we compare the estimated ultimate recovery (EUR) predictions for two-phase and single-phase solutions under boundary-dominated flow (BDF) conditions. The results show that single-phase flow solutions will underestimate the ultimate cumulative oil production of wells because they do not account for liberation of dissolved gas and its subsequent expansion (pressure support) as the reservoir pressure falls below the bubblepoint. Finally, the application of the two-phase model provides a better fit when compared with the single-phase solution. The present model requires very little computation time to forecast production because it only uses two fitting parameters. It provides more realistic estimates of URFs and EURs, when compared with single-phase flow solutions, because it considers the expansion of the oil and gas phases for saturated flow. Finally, the solution is flexible and can be applied to forecast both tight-oil and gas condensate wells.

Research of tight gas reservoir simulation technology

The stimulation of tight gas reservoir in Linxing block of Shanxi Province has always been a difficult problem. This paper describes and analyzes the formation lithology, physical properties and sensitivity of the reservoir. And introduces the pulse power technology and controllable shock wave technology, and focuses on the research and analysis of the latter. The results of field test show that: In the application of plug removal and permeability enhancement of low permeability tight gas oil and gas wells in medium and shallow wells (well depth less than 3000m), the controllable shock wave technology has achieved good results in increasing production and injection. Field application shows that the technology is basically mature and can be further promoted. Compared with traditional technologies such as acidizing and plugging removal, controllable shock wave technology has the advantages of layered implementation, strong pertinence, simple operation, wide adaptability and better effect.

Production optimization and economic analysis for hydraulic fracturing operations in tight oil wells

A technical and economic analysis is done from simulations of propped hydraulic fracturing in low permeability reservoirs, as tight oil, in which the production from the well varies due to different well completion, each one is evaluated to determine the greatest economic performance. Working with a defined semi-integral system from the reservoir to the wellhead and focusing on well productivity; a method of analytic solution for diffusivity equation, alongside the nodal analysis method, and a multiphasic flow correlation for inclined tubing; allows estimating production on the wellhead. The cases studied demonstrate that hydraulic fracturing operations are very profitable before taxes, also, due to the high costs of these stimulation techniques, it is important to have an analytical tool that allows for a quick and precise evaluation of diverse scenarios, based on production analysis and its corresponding profit calculated by an economic analysis. This study could be adapted to analyze various tight oil reservoirs, providing results that justify investing in exploration and/or reactivation of them.

Mud-Gas Breakthrough Equinor Develops Real-Time Reservoir-Fluid Identification

For all that logging-while-drilling has provided since its wide-spread adoption in the 1980s, there is one thing on the industry’s wish list that it could never offer: an accurate way to tell the difference between oil and gas. A new technology created by petrotechnicals at Equinor, however, has made this possible. The innovation could be thought of as a pseudo-log, but Equinor is describing it as a reservoir-fluid-identification system. Using an internally developed machine-learning model, it compares a database of more than 4,000 reservoir samples against the real-time analysis of the mud gas that flows up a well as it is drilled. Crunched out of the technology’s various hardware and software components is a prediction on the gas/oil ratio (GOR) that the rock being drilled through will have once it is producing. Since this happens in real time, it boils down to an alert system for when drillers are tapping into uneconomic pay zones. “This is something people have tried to do for 30 years - using partial information to predict entire oil and gas properties,” said Tao Yang. He added that “the data acquisition is rather cheap compared with all the downhole tools, and it doesn’t cost you rig time,” highlighting that the mud-gas analyzer critical to the process sits on a rig or platform without interfering with drilling operations. Yang is a reservoir technology specialist at Equinor and one of the authors of a technical paper (SPE 201323) about the new digital technology that was presented at the SPE Annual Technical Conference and Exhibition in October. He and his colleagues spent more than 3 years building the system which began in the Norwegian oil company’s Houston office as a project to improve pressure/volume/temperature (PVT) analysis in tight-oil wells in North America. It has since found a home in the company’s much larger offshore business unit in Stavanger. Offshore projects designed around certain oil-production targets can face harsh realities when they end up producing more associated gas than expected. It is the difference between drilling an underperforming well full of headaches and one that will pay out hundreds of millions of dollars over its lifetime. By introducing real-time fluid identification, Equinor is trying to enforce a new control on that risk by giving drillers the information they need to pull the bit back and start drilling a side-track deeper into the formation where the odds are better of finding higher proportions of oil or condensates. At the conference, Yang shared details about some of the first field implementations, saying that in most cases the GOR predictions made by the fluid-identification system were confirmed by traditional PVT analysis from the trial wells. Unlike other advancements made on this front, he also said the new approach is the first of its kind to combine such a large database of PVT data with a machine-learning model “that is common to any well.” That means “we do not need to know where this well is located” to make a GOR prediction, said Yang.

Investment and production dynamics of conventional oil and unconventional tight oil: Implications for oil markets and climate strategies

Unconventional tight oil production has increased fast in recent years. This development can appear threatening to climate change mitigation efforts as more available supply might lead to continuing or increasing consumption, contributing to ‘carbon lock-in’, possibly making a low-carbon energy transition more difficult. However, the investment and production cycles of tight oil differ from conventional oil. This study investigates such differences and discusses whether it has any implications for the oil market and for energy and climate strategy in regard to carbon lock-in and stranded assets.Production and capital investment data for conventional oil fields and unconventional tight oil wells are analysed and standard production profiles are derived. Through a bottom-up approach aggregate investment and production dynamics are then investigated.The analysis shows that in conventional production, once initial investments are made, decades of high production rates can follow. In contrast, tight oil production declines fast with the dominating part of cumulative production produced within the first few years. Both kinds of production are characterised by large initial capital costs and lower operating costs. This cost structure incentivises continued production once initial costs are spent, even if oil prices fall, but since tight oil wells decline faster the amount of locked-in production is significantly lower than for conventional projects.The different dynamics of conventional oil and tight oil needs to be considered when formulating energy and climate strategies. Since tight oil production can entail lesser lock-in effects and stranded asset risks, it might provide a beneficial and more flexible pathway in face of future uncertainty in the development of substitute technology and in climate policy commitments.

Variation in b-sigmoids with flow regime transitions in support of a new 3-segment DCA method: Improved production forecasting for tight oil and gas wells

This study uses a commercial reservoir simulator to generate production rate data for shale wells with systematic variations in fracture treatment design parameters using a plausible range of reservoir properties. The synthetic well data is then used to generate diagnostic log-rate log-time plots, which allow for a distinction of four types of flow regimes. Such a distinction of flow regimes is of practical relevance, because of the inverse link with reservoir properties and fracture treatment design parameters. Each flow regime has a characteristic slope, which traditionally has been attributed to typical flow conditions in the reservoir. The synthetic production data from the reservoir simulator are used to constrain b-sigmoid patterns for narrow discrete time increments of production (1 month) throughout the various flow regimes occurring during the economic life of each synthetic well model. Nearly a hundred different models were generated as a basis for our analysis. From the analysis of the DCA parameters for time series, using the difference in inverse of decline rates (loss ratio) for two adjacent time steps, it appears possible to recognize systematic shifts in the instantaneous decline rates and temporal b-values. In particular, the generated b-value patterns (b-sigmoids) appear diagnostic for flow regime changes recognized on diagnostic log-rate log-time plots. The results demonstrate that it is possible, based on well design and reservoir parameters, to establish correlation trends between b-sigmoids and flow regime changes using time series of the DCA parameters. The results are subsequently used to improve the accuracy of a modified 3-segment DCA method based on Arps equation. The proposed method is applied to synthetic production data, generated based on an Eagle Ford shale oil well (with known reservoir and fracture properties), to demonstrate the practical value for production forecasting and reserves estimation. This research paper paves the way for wider, future application of the methods described.

Life After 5: How Tight-Oil Wells Grow Old

Tight-oil production in the US set new records in 2019, but the banner year ended with an important caveat: more than two-thirds of crude production from shale plays flowed from wells drilled in the past 2 years. The figure reflects the fast-paced life of horizontal wells while also highlighting that older assets are not contributing as originally expected. Wells brought online since the start of 2019 represented 45% of total US onshore production—3.5 million B/D out of an all-time high 7.74 million B/D. As more late-year production data flows in, the 2019 wells might represent closer to 50% of the total, according to information from Shale Profile, an analytics firm that aggregates public data from US oil and gas regulators. Another 25%, or about 1.9 million B/D, came from wells that started producing in 2018. The remainder of US onshore supply came mostly from the 93,000 wells that were completed between 2010–2017. The importance of this older stock of horizontal wellbores is coming into focus as the early-time-decline rate of new wells accelerates, prompting upstream analysts at IHS Markit to forecast that US tight-oil production will peak in 2021. Unconventional wells in the most prolific shale plays tend to see decline rates of more than 50% in their first year, and another 30% in their second. One of the key assumptions that justified the economics of such production profiles held that, after about 5 years, those steep declines would transition to the modest 5–10% annual drops seen in conventional wells, and remain there for many years. The most recent production data reveal that tight oil wells are not living up to this standard. At 5 years of age, horizontal wells in the Permian Basin and Williston Basin’s Bakken Shale share average terminal decline rates of around 17%. The figure for wells of the same age in the Eagle Ford Shale is 23.5%. On average, it takes 8–10 years for these wells to fall below an annual decline threshold of 10%. These terminal decline rates represent a clear challenge for current reserves and ultimate recovery estimates from wells that were expected to produce economically for 30 or more years. As a result, shale producers are now advised to not use conventional wells as a guide, or else run the risk of overstating and overvaluing unconventional assets (URTeC 432). The sector’s awareness of long-term production shortfalls is also considered to be the driving force behind a new wave of mergers and acquisitions that has left constrained operators seeking to combine with peers holding proportionally large positions of undrilled acreage.