Limitless Delivery frees up dispatcher’s time

With Revonos – Limitless Delivery, your oil and water hauling dispatcher can get a half day’s work completed in minutes. Operators and liquid haulers leverage our operations research based optimization tool to automate timely liquid hauling prioritization. This will enable dispatchers to work on more valuable production optimization, business development or other meaningful tasks.

Revonos Launches Limitless Delivery

When your automation room aerial view looks clear, yet the actual location is socked in with the latest snow storm, are your oil and water haulers ready for routing prioritization? How do you prepare for the next weather event?

Limitless Delivery will optimize oil and water hauling, truck routing and dispatch to keep your production and hauling operations heading in the right direction at the right time to minimize miles and maximize loads hauled before the next storm sets in.

Contact Revonos for more information and a liquid hauling optimization trial.

Upstream E&P Needs Operations Research

Operations Research (OR) is not new. The development of the field took off following World War II. Industry took up the pursuit not long after. Today, the Franz Edelman Award finalists for 2018 were announced. This award presented by INFORMS is named to honor Franz Edelman who was a pioneer of OR/MS. He led the Operations Research department at RCA Corporation, where he envisioned using mathematical optimization to manage business better.

A petroleum entity, China National Petroleum Corporation, is included in the finalists this year. Previous years have had petroleum companies make the list of finalists, like Chevron in 2013. Last year’s inclusion of BHP Billiton is the finalist that I know of nearest to E&P operations, but it is in mining production, not the petroleum business. The common thread in the petroleum sector finalists has been midstream applications. CNPC applied ORMS to natural gas pipeline networks. Chevron optimized refinery operations.

Optimization methods are applied to E&P operations by companies such as ExxonMobil and BP. I applied OR to hauling at Noble Energy and discussed the models in my thesis. I believe this is rare. The reason I propose has to do with the common dialog that accompanies the historically volatile oil & gas markets. Producers are too happy to keep business as usual when prices are high. The most common response to low prices is to renegotiate contracts, drill less or not at all, pinch pennies and await the “recovery”. The shale explosion has been fueled by technological improvements that make development economic at lower prices. OR can help to make production operations economic at lower prices. A nice side-effect to making production operations more efficient is lowering the energy input to get the production output, reducing pollution.

I want to see OR topics become common in the conference rooms of E&P companies of all sizes, not only the majors. Let’s apply mathematical optimization to production operations and reduce LOE to make more profit.

Data Science is a Key Component of Operations Research

Description of Operations Research

Operations research (OR), operational research, management science and decision science are just some of the names used for this discipline that seeks to arrive at answers to complex decisions faced by organizations of all sizes. OR is characterized by seeking efficiency in allocation of scarce resources. This means that OR applies to everything.

When trying to solve problems or make decisions, OR provides an approach that can be applied to deliver results. This is in contrast to the definition of Data Science which primarily focuses on gaining insight from data. Individuals can choose to make decisions based on this insight, but the process of improvement is not a definitive part of data science. OR incorporates the process of improvement and is commonly known as the “science of better”.

OperationsResearchOperations Research Methodology in (Data) Science Terms

The Operations Research Methodology approach to decision-making is familiar to the data scientist. The methodology is an application of the scientific method. The OR professional is presented with a problem, gathers data, formulates hypotheses, tests the hypotheses and generates useful solutions.  There are three major disciplines or processes within the Operations Research framework.

Data Collection, Gathering and Exploration is an integral process in OR. An OR professional may not be given any raw data to begin with, but must seek data for input to define the problem mathematically.

Exploration of the data overlaps into the second discipline within OR.  This is the Data Science portion of OR.  Data Science’s main purpose is to gain additional insight into a process. Data Science is the practice of finding insights in the data.

In order to find insights in the data, the data scientist must formulate a hypothesis utilizing the data at hand.  Manual or semi-automated modeling is used to formulate hypotheses. Combining the raw data and additional insights from the data exploration step, mathematical models can be built for simulation, optimization or other decision support purposes. These models become the hypotheses to be tested.

Hypothesis testing is regular practice in the data scientist’s day-to-day work. For effective hypothesis testing, the OR professional should utilize a mathematical model that is a simplification of the actual, real-world process. A test of these models can be functional tests, like validation used in regression or classification models, or alternately running simulations or implementing real-world tests.

The final key portion of OR is Decision and Outcome Management. The field of Decision Management offers methods and systems to track of decision points and the choices made. It is important to provide actionable information through data science, but it is equally important to track the recommendations or decisions available as well as the actual decision that is made, and the effectiveness and results of the decision outcome.  In this way, the results can be reincorporated into data collection, and subsequently model improvements.


Operations Research is composed of multiple fields and disciplines. Effective Operations Research leverages the skills of these different fields to improve processes and outcomes. The fields and terms used in OR are maturing, and this is leading to greater definition and understanding of the various components of an effective OR outcome.  For example, Data Science is a key component of OR, but does not capture all of the components of that methodology.  Statistics and business analytics are a key components of Data Science, but non-statistical methods like machine learning are key tools in the Data Science discipline.  In order to find the most efficiency in allocation of scarce resources, all components of Operations Research should be employed.


Three Categories of Analytics

The different levels of analytics can be summarized into three categories. These are descriptive, predictive and prescriptive analytics.

The first category, descriptive analytics, is necessary to be able to perform analysis. The tools and processes that belong in this category are not necessarily simple, but are sometimes only simple statistics describing data. Traditional business intelligence (BI) falls under this category. The products of data exploration during data science does as well. Interestingly enough, even “alarming” falls under this category. Many times, alarms are considered advanced tools. They are certainly valuable, but really only tell a business what has already happened based on hard data and rules. The reason analytics in this category are necessary for being able to perform analytics in either of the other two categories is that achieving descriptive analytics prepares the organization’s data for consumption. Even if analytics implemented in systems are not part of an organizations regular business, personnel take in the descriptive analytic products and perform additional processing themselves to determine decisions. Another area that falls under descriptive analytics is data exploration. Data exploration is a manual, iterative process that generates statistics, which lead to insight on how to build models useful for implementation in a process. This is the data scientist’s first step.

Predictive analytics are the more common of proactive analytics. This category is vast. In statistics, prediction is also known as estimation and not necessarily forecasting. Predictive analytics are concerned with unknown quantities and factors. Factors can be flags, levels, classification or clustering. Predictive analytics are performed with any of a multitude of tools. A simple example is ordinary least squares (OLS) estimation. The machine learning wave mostly lives in this category. Hierarchical & k-means clustering, random forest regression & classification, support vector machines and ridge regression are some of the tools that exist in this category. These tools each have their strengths and weaknesses and it takes a learned person (or automated evaluation of each one) to figure out which is the best to apply to a specific problem. These problems include finding production excursions, detecting fraud, estimating power output, demand forecasting, risk estimation and claims prediction. This list is barely a scratch on the surface of the use cases for predictive analytics.

The final category, prescriptive analytics, is the other proactive category of analytics shared by two general classes of tools. Machine learning is one of these. Operations research is the other. Each of these classes makes extensive use of mathematical optimization techniques. Artificial neural networks and deep learning methods are machine learning techniques which are capable of recommending decisions in certain applications. The problem domain for deep learning is continually expanding. This technology is what makes autonomous vehicles possible. There are other techniques that can be applied to recommend decisions through mathematical optimization, such as stochastic gradient descent and other unconstrained minimization techniques. When the rules of the decisions are known ahead of time, a machine learning method can be applied to recommend a decision. Many times, decisions must be made within more general constraints instead of rules. For instance, a supplier might need to deliver product from different plants to a variety of depots. This scenario has demands for product at each depot and maximum supply at each plant. The transportation costs between plants and depots can be taken into account and a minimum-cost solution found using linear programming. This use case is one that has had an efficient mathematical solution since World War II. The applications of linear programming have been explored and expanded throughout many industries. It can be applied to manufacturing (blending, for instance) and finance (using portfolio balancing) for two examples.

These categories of analytics can be regarded as a progression, but they need not be, with the exception of having a handle on descriptive analytics before beginning any proactive analytics. The two proactive categories can overlap some as far as techniques, but one distinguishing characteristic is the level of additional insight that must be obtained to make a decision. For instance, if a rule is applied to results of a machine learning method, then the method is not prescriptive. Whereas, if the results of a technique are decision points by themselves, then it is prescriptive.


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