This paper presents a method for pinpointing intervals for fracture stimulation in horizontal wells targeting unconventional oil plays. The observation of crossflow among fractures has been of great concern as this phenomenon affects the productivity of producing wells. The cause is related to the effectiveness of fracturing stages, which by itself depends on the rock lithology. We identified interaction among fractured intervals from diagnostic modeling of performance data that exhibited cross flows in the wellbore. On wells exhibiting the most prolonged duration of crossflow, we noted the disadvantages of equal space fracturing. We then used the drilling parameters from MWD data for individual wells and computed the d-exponent profiles and noted significant differences in rock brittleness as characterized by their d-exponent data. Out of the more than 60 wells studied, wells exhibiting minor changes in the d-exponent showed the least indications of cross flows from performance data while in wells with significant cross flows we see the nonuniformity of the d-exponent profile and the negative impact of equal space fracturing.
In modern logging practices, the Poisson ratio and the Young Modulus as measures of rock Brittleness may be estimated from the dipole sonic and bulk density logs. This is important when subterranean formations are considered for fracturing or when unintended fracturing can be a concern under high injection gradients in fluid injection processes. For most oilfields, the bulk of well log suites run in the past has been limited to basic lithology logs and occasionally some porosity logs. In particular, the sheer sonic velocity log, an important component for estimation of geomechanical properties, has not been a standard measurement on common log suits.
In this paper, we present the result of a study where shear travel time is correlated with measurements from caliper and shale content. The training set we used consisted of well logs for wells that included the shear travel time. We experimented with various approaches and developed a process for in cooperating DNN (deep neural network) to correlate the shear log data to other measurements.
Objectives/Scope: In order to maximize the recovery of hydrocarbons from liquids rich shale reservoir systems, the cause and effect relationships between production and the stimulation methods need to be clearly understood. In this study, we utilize multivariate regression models to narrow down the variables in flow simulation models and their range. We then use the flow simulation model to understand the fractured well production behavior and field wide well performance in a liquids rich petroleum system in the Duvernay Basin.
Methods, Procedures, Process: Statistical models assume no physical relationship between the model parameters and the response variable, which in this case is produced volumes over a period of time. On the other hand, simulation studies incorporate physical mechanisms of flow to model and predict the production behavior. The simulation models, however, fall short of incorporating all the mechanisms contributing to the production behavior in the complex shale gas reservoir. Thus there is a need for integration of statistical approaches of understanding production behavior along with physics based model and simulation approach. We use the statistical methods to identify the important physical mechanisms that control the production.
Results, Observations, Conclusions: Multivariate linear regression analysis of the 6 month produced volume and its relationship with parameters such as fracture fluid volumes used, proppant weight placed, number of stages fractured provides a model with reasonably good correlation. The 6 month produced volumes correlate with large proppant weights, lower fluid placements and greater density of fracture stages. Use of Random Forests machine learning algorithm on the dataset confirms that the total proppant placed, well length completed with fractures have high importance coefficients. In order to examine the well performance using full physical models, fractured well simulations are performed on particular wells using the trilinear model. The trilinear model predictions are then compared against other production analyses and the regression model results for consistency. The models showed that in the absence of stress dependent permeability, the production forecast was much higher. Thus, stress dependent permeability appears to be an important factor in the modeling and prediction of production from liquids rich shale reservoirs.
Novel/Additive Information: In this study we describe a method to understand the production data from a liquids rich shale reservoir, by integrating multivariate linear regression analysis, machine learning algorithms along with physical model simulations. The results are novel and offer a method to validate either approach to understand cause and effect relationships. This approach may be classified as a new hybrid modeling workflow that may potentially be used to optimize stimulation techniques in liquids rich shale reservoirs.
In this paper we describe a novel method for water unloading of natural gas wells in mature reservoirs experiencing low reservoir pressures. Current methods for water unloading from gas wells have at least one of the drawbacks of restricting gas production, requiring external energy, using consumable surfactants, or being labor intensive. The proposed design offers a new approach to water unloading that does not restrict or interrupt gas production. It can operate without external energy, and uses no consumables. Virtual and physical simulators have been developed and the full-scale version of the concept has been studied in test wells to demonstrate the feasibility and performance of the new water-unloading concept. An industrial-grade preproduction prototype was tested successfully in a test gas well to validate this study.
Development and management of oilfields involve several sources of uncertainty that complicate an already challenging decision-making process. Two main sources of uncertainty are related to geologic description of reservoirs and future development scenarios. While geologic uncertainty has been widely studied and robust optimization methods have been developed to account for it, the uncertainty in future development plans has not been considered in optimization problems. Future development strategies have been included as decision variables in field development optimization problems. However, in practice, future field development plans tend to deviate from the solutions obtained in past optimization problems. Therefore, a more prudent and realistic approach toward oilfield optimization is to consider the uncertainty in both geology and future development plans to obtain robust solutions. We develop a closed-loop stochastic field development optimization formulation to account for the uncertainty in geologic description and future infill drilling scenarios. The proposed approach optimizes the decision variables for current stage of planning (e.g. well locations and operational settings) while accounting for geologic and future development uncertainties, where the former uncertainty is represented by using several reservoir model realizations while the latter uncertainty is represented through drilling scenario trees and probabilistic description of future drilling events/parameters. In the developed method, prior to each decision-making stage the reservoir is operated based on the current optimal strategy until dynamic data becomes available to calibrate the geological models. After each data assimilation step, a new optimization is performed to adjust controllable decision variables for the current well configuration (e.g., well rates or BHPs) using the updated models and potentially revised future development scenarios. Using a multi-stage stochastic optimization workflow this process is repeated after each decision stage. Several numerical experiments are presented to discuss various aspects of the proposed closed-loop stochastic optimization formulation and to compare the solutions from different methods adopted for treatment of future development plans. The results indicate that stochastic treatment of future development events (1) can hedge against uncertain future development activities by obtaining optimization solutions that are robust against changes in future decisions, and (2) considerably reduces the performance losses that can result from field development when uncertainty is disregarded.
Multiple point statistical (MPS) simulation is a modern pattern-based geostatistical approach for describing and stochastically simulating geologic formations with complex connectivity patterns. In MPS geostatistical simulation, a template containing data patterns around each simulation cell is used to extract and store the local conditional probabilities from a training image (TI). To generate a simulated sample, a random path is generated to sequentially visit all unsampled grid cells and draw conditional samples from the corresponding stored conditional probabilities. The grid-based implementation of MPS simulation offers several advantages for integration of hard and soft data. In the Single Normal Equation SIMulation (SNESIM) implementation of MPS for facies simulation, it has been observed that the integration of soft data can result in many facies realizations that do not provide consistent patterns with the incorporated probability map. This is partly explained by the Markov property that only considers probabilities that are co-located with the simulation node, and hence ignoring spatial information from neighboring cells. In addition to this effect, we show another important mechanism is in play in the SNESIM algorithm that explains the observed behavior. Specifically, at the early stage of the simulation when the first few percentage of the simulation nodes on the random path are visited the local conditioning data are limited and the resulting conditional probabilities that are obtained from the TI are not strictly constrained. Hence the conditional probabilities cover a wide range of values in the range [0,1]. However, after this initial stage, as the simulated data populate more cells in the model grid, they tend to severely constrain the conditional probabilities to assume extreme values of 0 or 1. With these extreme values at the later stages of the simulation the probability values that are included in the soft data (as secondary source of information) tend to be disregarded and the facies types are predominantly determined by the TI. We demonstrate and discuss this behavior of the SNESIM algorithm through several examples and present strategies that can be adopted to compensate for this effect. The presented examples are related to indirect integration of the flow data by first inferring probabilistic information about facies types and using the results as soft data for integration into SNESIM algorithm.
Rate of penetration (ROP) in petroleum engineering refers to the speed of forward motion of the drilling tools during the drilling process. This is an important parameter that has long been optimized for maximization, keeping in mind human, safety, and environmental factors along with consideration to downhole tools. Its importance is validated by estimating the drilling. Longer estimates of drilling time translate to increased costs.
Drilling costs are affected mainly due to the following contributing factors: non-productive time, idle time, and invisible time. Attempts have been made to reduce these times to reduce costs. Simultaneously the time taken for drilling can also be reduced by effectively increasing the ROP. Drilling depths, on average are between 5,000 to 10,000 feet, coupled with a formation that has complex properties are major factors contributing to non-productive time covering a high proportion of drilling time. Thus, a large non-productive time leads to longer drilling cycles, and eventually, a low ROP.
In an attempt to reduce the non-productive time, there is a need to optimize the ROP. Higher ROP facilitates a decrease in time and thus costs.
In this paper, ROP is effectively predicted using artificial neural networks not at the surface, but at the bit. The artificial neural network has several advantages that overcome the limitations of the conventional models. By effectively predicting ROP, estimation of the whole drilling process time and cost, identification of specific reasons that slow down the drilling process are possible, and proper measures to avoid these issues can be implemented. The target of any ROP optimization strategy should be to have the highest ROP mechanically possible, considering human health, safety, and environment, and factoring in conditions of the well and drilling state.
Various methods that link a representative pore-throat size to permeability k and porosity ϕ have been proposed in the literature for rock typing (i.e., identifying different classes of rocks and petrofacies). Among them, the Winland equation has been used extensively, although when it was first proposed, it was based on experiments. Because of empiricism, the interpretation of the parameters of the Winland model and their variations from one rock sample or even one rock type to another is not clear. Therefore, the main objectives of this study are (1) to propose a new theoretical approach for identifying rock types that is based on the permeability k and the formation-resistivity factor F and (2) to provide theoretical insights into, and shed light upon, the parameters of the Winland equation, as well as those of other empirical models. We present a simple, but promising, framework and show that accurate identification of distinct petrofacies requires knowledge of the formation factor, which is measured routinely through petrophysical evaluation of porous rocks. We demonstrate that, although some rock samples might belong to the same type on the k-vs.1/F plot, they might appear scattered on the k-vs.-ϕ plot and, thus, could seemingly correspond to other types. This is because both k and F are complex functions of the porosity, whereas the porosity itself is simply a measure of the pore volume (PV), and does not provide information on the dynamically connected pores that contribute to both k and F. We also show that each rock can be represented by a characteristic pore size Λ, which is a measure of dynamically connected pores. Accurate estimates of Λ indicate that it is highly correlated with the permeability.
Xin, C. (BGP.CNPC) | Zhaowei, L. (CNODC,CNPC) | Zhaofeng, W. (PKKR.CNPC) | Wenyuan, T. (BGP, CNPC) | Yaliang, X. (BGP, CNPC) | Yanjing, L. (BGP, CNPC) | Xiaodong, W. (BGP, CNPC) | Hongmei, W. (BGP, CNPC) | Yu, J. (University of Southern California) | Xiaohuan, Y. (BGP, CNPC)
In order to improve the accuracy of reservoir prediction results, the conventional method usually include seismic inversion, and wei. Due to the limitation of the vertical resolution of seismic data, it is hard to identify the thin reservoir by seismic attributes directly. In order to improve the prediction accuracy of reservoir, this paper show a new reservoir characterization technique based on geological seismic conditioning. The new method mainly includes five steps. The first step is sedimentary facies classification based on the geological seismic analysis, such as core data, thin section analysis, FMI logging, NMR logging and conventional logging. The second step is modern sedimentary model optimization and forward modelling. In order to establish a reasonable sedimentary facies model, a similar barrier island modern sedimentary model was chose. To understand the geological significance of seismic data, two different dominant frequency were designed for forward modelling based on the sedimentary facies model and petrophysical analysis. The third step is seismic conditioning under the guide of sedimentary facies model forward modelling. The next step is seismic constraint stochastic inversion, and the last step is reservoir characterization and new well confirm. The application of this method in A oilfield shows that the techniques not only improved the identification ability of the reprocessing seismic data, but also improved the prediction accuracy of the reservoir characterization results. This new reservoir characterization technique can integrated multidisplinary information, such as modern sedimentary model, well data and seismic data, to establish a reasonable sedimentary model, to enhance the resolution of seismic data by conditioning, and get an reasonable reservoir characterization results based on the seismic inversion.
Infill and replacement drilling are effective ways to improve oil recovery as increasingly more wells are drilled in close proximity for fracturing. Presently, the approaches being employed are logging surveys, the moving window method, the rapid inversion method, and the customized type curve method. However, these methods are not suitable for reservoirs with high levels of heterogeneity in terms of geology, and require more expert knowledge and field survey, which can be time consuming and costly. Therefore, the present method developed is an economic and fast approach to determine infill and replacement drilling location from reservoir ranking maps generated in combination with machine learning methods.
During this project, production data and reservoir parameters were gathered from an old oil field with more than 2,500 wells where most of the field was under water injection. Bubble maps were created for each reservoir parameter for a better visual representation of reservoir conditions. Then, after data cleansing and normalization procedures, the standout attributes were identified from all given reservoir parameters and production history and a reservoir ranking rule was set. Next, five types of classification approaches were used for prediction. This paper additionally presents a regression method, artificial neural network (ANN), to compare with the prediction results from classification. For each machine learning technique, a reserve ranking map was generated for this test field to predict future infill drilling and replacement drilling opportunities. Thus, with only geographic coordinates, the reserve ranking level was obtained.
From cross-fold validation results, a quadratic support vector machine provides the highest prediction accuracy. From a practical standpoint, a decision tree offers a more realistic result. In addition to the ANN method outputs, the ranking result provides a smooth method between certain levels. This new approach of using artificial intelligence was used to provide the ranking level and ranking number to identify the best options for drilling the wells, which is different from the present traditional methods. This advanced reservoir ranking map allows operators to identify the best location for infill or replacement drilling. It can additionally help operators benefit from their previously gathered knowledge in a cost-effective way.