A Monte Carlo model is, in principle, just a worksheet in which some cells contain probability distributions rather than values. Thus, one can build a Monte Carlo model by converting a deterministic worksheet with the help of commercial add-in software. Practitioners, however, soon find that some of their deterministic models were constructed in a way that makes this transition difficult. Redundancy, hidden formulas, and contorted logic are common features of deterministic models that encumber the resulting Monte Carlo model. Likewise, presentation of results from probabilistic analysis might seem no different from any other engineering presentation (problem statement, summary and conclusions, key results, method, and details).
Risk analysis is a term used in many industries, often loosely, but we shall be precise. By risk analysis, we mean applying analytical tools to identify, describe, quantify, and explain uncertainty and its consequences for petroleum industry projects. Typically, there is money involved. Always, we are trying to estimate something of value or cost. Sometimes, but not always, we are trying to choose between competing courses of action.
Decision tree analysis and Monte Carlo simulation are the most commonly used tools in decision and risk analysis. But other tools such as optimization, options analysis, and combinations of these various tools can also be useful. This article examines the importance of data analysis and the nature and application of these other tools. Regardless of the principal tool used in risk analysis--Monte Carlo simulation or decision trees--empirical data may play an important role. Similarly, the input distributions selected for a Monte Carlo model are easier to justify when analogous data is available to support the choices of distribution type and value of defining parameters, such as mean and standard deviation.
In the context of risk, a decision tree is a sequence of nodes which are either a decision or an uncertainty, and outcomes associated with each mode. The purpose of a decision tree is to define the set of scenarios and the sequence of events that guide the evaluation of risk and return. It is displayed as a pictorial device, consisting of nodes and branches, that describes two or more courses of action and the resulting uncertainties with probabilities of occurrence, as well as possible subsequent actions and uncertainties. The solution to the tree consists of a preferred course of action or path along the tree, together with the resulting expected value.
The oil and gas industry invests significant money and other resources in projects with highly uncertain outcomes. We drill complex wells and build gas plants, refineries, platforms, and pipelines where costly problems can occur and where associated revenues might be disappointing. We may lose our investment; we may make a handsome profit. We are in a risky business. Assessing the outcomes, assigning probabilities of occurrence and associated values, is how we analyze and prepare to manage risk.
The effect of frac-hit among the stimulated horizontal wells located in the northwest of the State of New Mexico are identified by addressing how to predict whether or not a planned well caused frac-hit for older wells nearby, and in case of the frac-hit occurrence, how to predict the degree of impact. The machine learning method is used to find the relationship between well parameters such as distance and age difference, and frac-hit occurrence and the degree of impact. Determining the probability of frac-hit occurrence is considered as a classification problem, and random forest method is used to predict the occurrence of the frac-hit. Predicting the impact of the frac-hit is considered as a regression problem, and two machine learning methods, gradient boosting and adaptive boosting (AdaBoost), are used to solve this problem. In the pool of data, the data are randomly assigned to train and test set for unbiased machine learning.
The data of the training set are put into the random forest classifier to find whether the distance, age, age difference, and bearing have any impact on the occurrence of the frac-hit. Among these four factors, the bearing has the most significant impact, which means that the weight of bearing in classification process is higher than the other parameters, followed by the distance as the second important factor. Applying the trained random forest classifier on the test set data gives 78% correct outcomes compared to the actual frac-hit data in the test set.
Considering the change of oil production due to frac-hit as the indicator to measure the degree of impact in gradient boosting and AdaBoost algorithm shows that the bearing between wells is not an influential parameter in the regression problem compared to the classification problem. In other words, if the well has already experienced the frac-hit, the importance of bearing decreases, and the distance, age difference, and age of the wells become more prominent factors. The analysis shows that the average error between the actual data and the predicted results by gradient boosting and AdaBoost is about 40%.
The results of this paper can be used by the hydraulic fracturing operators to pre-determine the frac-hit probability and its impact on existing offset wells. It can also help to refine well design strategies to minimize the risk of potential well interferences.
Considering carbonate oil reservoirs, a rock fracture is a planar-shaped void filled with oil, water, gas and/or rock fines. These fractures vary in scale forming connected and complex networks of fractures. They have an effect on deliverability of fluids depending on their geometrical complexity, extent, matrix-fracture interaction, wettability, and orientation. In fractured reservoir rocks, relative to the rock matrix, fractures form highly permeable flow pathways that dominate fluid flow and transport in the reservoir which might have favorable or non-favorable effects on hydrocarbon production. It is crucial to characterize the fluid flow in the fracture networks to examine the root-cause relationships, the impact on hydrocarbon recovery and quantify the efficiency of enhanced recovery mechanisms.
This work describes the development of a machine learning model for history matching and predicting two-phase relative permeability. Capitalizing on the main principles of the 4th Industrial Revolution (IR 4.0), the development of this model was achieved by training machine learning (ML) algorithms and using advanced predictive data analytics on data collected from lab experiments as input. The model derived from the analysis describes two-phase flow of oil and water in a single discretized fracture taking into account fracture aperture, wall roughness, orientation and, flow rates and direction. It also accommodates fluids and fracture characteristics to match laboratory SCAL experimental of co-current oil and water flow in a mixed-wettability single fracture modeled as narrow gap in a Hele-Shaw cell.
The experimental data exhibit variations in shape and end-points that mainly reflect the effects of fracture aperture, roughness, inclination, and hysteresis effects. This in turn demonstrate the effects of phase interference, saturation changes, and major forces acting on two-phase flow in fractures like capillary and viscous forces.
The empirical relationship showed an acceptable match to the experimentally derived relative permeability in most of the cases as well as good predictive capabilities against the blind tests on other sets of experimental data and numerical simulation models. Having both fracture relative permeability data (describing the fluids flow) and detailed fracture characterization improves our understanding of the reservoir dynamics and fractured network impact on hydrocarbon recovery.
Alkinani, Husam H. (Missouri University of Science and Technology) | Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Alsaba, Mortadha T. (Australian College of Kuwait) | Amer, Ahmed S. (Newpark Technology Center/ Newpark Drilling Fluids)
As oil prices are fluctuating, decision makers are challenged to make the "best" decisions for field's developments. Decision Tree Analysis (DTA) can help decision makers to make the "best" decisions. DTA focuses on managerial decisions, such as whether to do workover or not, whether the additional information will be valuable or not. The aim of this work is to review the applications of DTA in petroleum engineering and provide a clear methodology on how to apply DTA for any petroleum engineering application.
The combination of Expected Monetary Value (EMV) and DTA is one of the most common methods used in the decision-making process. If EMV is positive, the decision is considered to be feasible. However, that doesn't mean the decision will be successful at all times. It simply means that if a similar decision is made for a larger number of cases, the decision will be successful. DTA will account for the uncertainty in the probability. A good number of papers about the applications of DTA in petroleum engineering were read and summarized into three categories. Also, a clear methodology on how to apply the DTA for any petroleum engineering application was established.
After reading and summarizing a good number of papers and case histories about the applications of DTA in petroleum engineering, it was concluded that the applications can be classified into three main categories; applications of DTA and EMV for the whole oil and gas prospect projects, applications of DTA and EMV for a specific operation or development, applications of DTA, EMV, Monte Carlo simulations, and other methods to assess the value of information. These applications were summarized into tables.
In addition, a clear methodology accomplished by a flowchart that explains how to successfully apply the EMV and DTA for any petroleum engineering application was provided. The method consists of three main steps: 1) how many scenarios need to be considered and what are they 2) collection of the required data 3) use the visual tool (DTA) or programming to find EMV. Each of the previous steps has its own challenges, thus these challenges were addressed and the solutions to overcome the challenges were provided. Finally, practical guidelines have were developed that when used with the accompanying flow chart will serve as a quick reference to apply the DTA for any petroleum engineering application.
As the petroleum engineering applications becoming more complicated nowadays, accomplished by the oil prices fluctuations, the decision-making processes becoming more difficult. The DTA is a very important tool for the decision makers to make the "best" decision. This paper provides a clear methodology on how to successfully apply the DTA which can serve as a reference for any future DTA applications in petroleum engineering.
Chesapeake Energy is partnering with RS Energy Group to improve operational efficiency and capital discipline by employing advanced analytics and machine learning. RS Energy is a Calgary-based energy research firm founded in 1998 covering more than 150 operators in the major North American and international oil and gas regions, including the US shale plays. It provides technical analysis of basins, including completions and production, as well as asset evaluations for operators considering acreage additions. All of this is done within the context of shifting capital markets. Chesapeake announced the pact fresh off its $4-billion merger with WildHorse Resource Development, which bolstered its position in the Eagle Ford Shale of South Texas.