The QQ Field in the Gulf of Suez is a mature, geologically complex with multiple stacked, faulted reservoirs, with commingled production between different reservoirs. This paper illustrates the power of an automated tool to perform systematic, rapid, and detailed assessment of the reservoir performance, identify the key recovery obstacles and prepare remedial plans to enable the reservoir to produce to its full potential. The well and reservoir data were processed to compute a series of metrics and key performance indicators at various levels (well, layer, reservoir, well groups, etc.). The tool has several automated modules to facilitate rapid, metric-driven reservoir assurance and management. These modules include: (i) well production/injection allocation, (ii) wells decline curve analysis including event-detection, (iii) pressure and voidage analysis, and (iv) Contact analysis. Using performance analytics, the study quickly identified ways to improve the health of the reservoir and maximize its value. The QQ Field predominantly produces from two formations: Nubia and Nezzazat. Furthermore, there are multiple sub-layers in each formation. Reliable flow unit allocation is critical to gauge contribution of each layer, identify the undrained areas of the reservoir, and locate future development opportunities. The flow unit allocation module incorporates all available data such as PLT/ILT data, completion history, permeability of each flow unit at well level, relative pressures, and water influx model. Based on the allocated production, the current recovery factors in Nubia and Nezzazat are approximately 60% and 20% respectively. Analysis of RFT data reveals good vertical communication across Nubia. However, in some areas there is clear pressure discontinuity across layers. The reservoir pressure has dropped below the bubble point in both formations. As a result, water injection was initiated. The pressure in all parts of Nubia was restored above bubble point. Aquifer influx is sufficient to support the current withdrawal rates and further water injection is unnecessary. However, Nezzazat has a significantly higher reservoir complexity and therefore, shows a large variation in pressure behavior. It needs water injection to maintain the reservoir pressure above the bubble point in all parts of the reservoir. Based on the flow-unit allocation, the voidage replacement ratio (VRR) was calculated for each area and each layer. Even though the overall VRR in the waterflooded areas is above one, the distribution of the injected water is uneven. Redistributing injected water and ensuring that all the areas and all the layers are adequately supported will help to maximize recovery. The prolonged dip in oil price demands extreme efficiency. Sound reservoir management must not require unreasonable time or manpower. The rapid, automated analysis enables quick identification of the key areas for improvement in reservoir management practices and maximize the value of the asset.

At the early stages of field life, the subsurface project team operates under lack of information. Due to the high uncertainties, decisions at the exploration and appraisal stages are often influenced by cognitive distortion that leads to overestimation or underestimation of hydrocarbon reserves and, as a result, to suboptimal investment decisions. World practice allows us to identify the most common causes of cognitive bias: the team focus on the most provable according to their view scenario and may ignore data that contradicts the chosen scenario, the opinions of the team members differ in the choice of the most likely scenario, the team members work with geological and geophysical (G&G) data performing separate tasks and may miss important connections between various sources of information. The consequences of these cognitive distortions cause an increase in risk capital, the duration of exploration activities, and the choice of suboptimal field developmentstrategy resulting in a decrease in the effectiveness of the exploration program and the project as a whole. To reduce such risks, it is possible to attract subject matter experts with extensive experience to support the project team. But the amount of experts is limited and this approach cannot be implemented for the entire portfolio of exploration projects. As result of a research project of Gazpromneft in a partnership with IBM Research, an innovative approach was developed for the objective integration of geological and geophysical data. The main idea of this approach is to support the geologist's decisions by an intelligent assistant working on the principles of the modern theory of knowledge engineering. Using the generalized expert knowledge, the intelligent assistant impartially integrates disparate geological information into a set of conceptual geological models (scenarios, objectively evaluates their probabilities, and helps to plan optimal exploration/appraisal activities.

The objective of this work is to numerically estimate the fraction of Reserves assigned to each Reserves category of the PRMS matrix through a cumulative distribution function. We selected 38 wells from a Permian Basin dataset available to Texas A&M University. Previous work has shown that Swanson's Mean, which relates the Reserves categories through a cdf of a normal distribution, is an inaccurate method to determine the relationship of the Reserves categories with asymmetric distributions. Production data are lognormally distributed, regardless of basin type, thus cannot follow the SM concept. The Gaussian Quadrature (GQ) provides a methodology to accurately estimate the fraction of Reserves that lie in 1P, 2P, and 3P categories – known as the weights . Gaussian Quadrature is a numerical integration method that uses discrete random variables and a distribution that matches the original data. For this work, we associate the lognormal cumulative distribution function (CDF) with a set of discrete random variables that replace the production data, and determine the associated probabilities. The production data for both conventional and unconventional fields are lognormally distributed, thus we expect that this methodology can be implemented in any field. To do this, we performed probabilistic decline curve analysis (DCA) using Arps’ Hyperbolic model and Monte Carlo simulation to obtain the 1P, 2P, and 3P volumes, and calculated the relative weights of each Reserves category. We performed probabilistic rate transient analysis (RTA) using a commercial software to obtain the 1P, 2P, and 3P volumes, and calculated the relative weights of each Reserves category. We implemented the 3-, 5-, and 10-point GQ to obtain the weight and percentiles for each well. Once this was completed, we validated the GQ results by calculating the percent-difference between the probabilistic DCA, RTA, and GQ results. We increase the standard deviation to account for the uncertainty of Contingent and Prospective resources and implemented 3-, 5-, and 10-point GQ to obtain the weight and percentiles for each well. This allows us to also approximate the weights of these volumes to track them through the life of a given project. The probabilistic DCA, RTA and Reserves results indicate that the SM is an inaccurate method for estimating the relative weights of each Reserves category. The 1C, 2C, 3C, and 1U, 2U, and 3U Contingent and Prospective Resources, respectively, are distributed in a similar way but with greater variance, incorporated in the standard deviation. The results show that the GQ is able to capture an accurate representation of the Reserves weights through a lognormal CDF. Based on the proposed results, we believe that the GQ is accurate and can be used to approximate the relationship between the PRMS categories. This relationship will aid in booking Reserves to the SEC because it can be recreated for any field. These distributions of Reserves and resources other than Reserves (ROTR) are important for planning and for resource inventorying. The GQ provides a measure of confidence on the prediction of the Reserves weights because of the low percent difference between the probabilistic DCA, RTA, and GQ weights. This methodology can be implemented in both conventional and unconventional fields.

Oklahoma is known for having ample sources of CO 2 , pipelines and sinks where for many decades, oil and gas operators were injecting CO 2 into geological formations for EOR purposes. We utilized SimCCS, an economic-engineering software tool (DOE-NETL), to integrate infrastructure related to CO 2 sources, pipeline, and geological formations. The approved tax incentive program by IRS (45Q) has motivated many oil and gas operators to participate in reducing CO 2 concentration and minimizing global warming effect by collecting CO 2 from various sources, select the best pipeline route and the safest location to inject into geological formation for EOR purpose or deep saline aquifer for sequestration. This paper presents an economic evaluation of CO 2 capture, utilization, storage (CCUS) into geological formation in the state of Oklahoma. Under this comprehensive approach, the process of capturing, transporting, and storing CO 2 into geological or saline formations has been economically evaluated for different sites and routes utilizing an ad hoc simulation software (SimCCS) for integrated modeling of CCUS. The outcome of this paper determines the most optimal scenario using optimization algorithms embedded in SimCCS. This case study will mitigate the CO 2 sequestration approval process when operator apply for tax credit under 45Q program. Our work will assist oil and gas operators by comparing different scenarios based on utilizing existing infrastructure, making decision in building new transportation system or new injection well to benefit the approved tax incentive program at its maximum capacity. Moreover, the outcome of this work will shed lights into future legislation demands (locally and nation-wide) to maintain CCUS momentum after its initial implementation phase is concluded.

After the oil price crashes in 2014 & 2020 several M&A deals ended up in legal debates because operators cancelled major projects or infills wells that were booked in the "probable" reserves only. This document challenges the compatibility between the deterministic incremental reserve assessment method (PRMS2018, chapter 4.2.1.3), and the concept of split condition (PRMS2018 chapter 2.2.0.3), which is not allowed for reserves booking under PRMS. With a few examples, we explain why the incremental method may be misleading investors, if used wrongly. Policies, stock market requirements, or simply the understanding of reserves guidelines may differ from one company to another. Many filers and auditors are still keen on using the deterministic incremental approach. This method consists in "defining discrete parts or segments of the accumulation that reflect high, best, and low confidence regarding the estimates of recoverable quantities under the defined development plan". In principle, this should give similar result to the widely accepted scenario method (PRMS2018, chapter 4.2.1.3) but in reality, major discrepancies are observed. Some reserve evaluation may also become misleading for banks, investors, or even for good asset management. I many cases, the estimation of recoverable volumes is reasonable, but it does not match the company CAPEX requirements, affecting corporate cash flow as well as potential Reserves Based Lending (RBL) requirements. In another case, the 1P case will be robust, but the 2P may be grossly overestimated, affecting M&A or share price. "Reserves guidelines are principle based" this has recently become a very fashionable statement in the context of SEC bookings. Similar discussions will also occur when reviewing PRMS reports. However, different interpretations for keywords such as "Project", "Spit condition", or "FID" should not prevent the evaluator to provide a reliable reserves estimation to investor or company management. This document questions the threshold where ethics disappears, and a Madoff scheme may become legal.

This study presents a novel metaheuristic algorithm that uses a physics-based model for multi-fractured horizontal wells (MFHW) to accurately predict the estimated ultimate recovery (EUR) for unconventional reservoirs. The metaheuristic algorithm creates a sizeable number of stochastic simulations and keeps the simulation results from those random models that closely reproduce observed production data. Unlike other optimization methods, the proposed algorithm does not aim at finding the exact solution to the problem but a group of sufficiently accurate solutions that help to construct the partial solution to the optimization problem as a function of production history. Results from this work provide sufficient evidence as to why traditional decline curve analysis (DCA) is not a suitable solution for production forecasting in unconventional reservoirs. Two case studies are discussed in this work where results from both modeling strategies are compared. Evolutionary prediction of EUR over time using DCA behaves erratically, regardless of the amount of historical production data available to the regression model. Such erratic behavior can, in turn, yield an erroneous estimation of key economic performance indicators of an asset. In contrast, the proposed metaheuristic algorithm delivers precise and accurate results consistently, achieving a significant reduction of uncertainties as more production data becomes available. In conclusion, the proposed partial optimization approach enables the accurate calculation of important metrics for unconventional reservoirs, including production forecasting and expected productive life of an asset.

The oilfield is in many ways the backbone of the world economy. Historically economic recessions and booms were linked directly or indirectly to the oil price, itself governed by supply and demand. Since the start of this industry, it has experienced multiple scandals and major accidents that have affected its path to date. In 2001 the Enron scandal of poor accounting practices shook the entire stock market, and it contributed to the complete recession in the same year. Not too long after, a supermajor revealed in 2004 a reserves accounting error that surprised the world. In 2010 the Deepwater Horizon blow out known as the "Macondo Incident" caused serious damage to the environment and significant fines to be paid in compensation due to poor engineering practice. Incidents like these arise concurrent with times of high demand and are instructive as we study their causes and effects on the oil industry and therefore the global economy. Areas of study for this paper include reserves/production estimation as well as ethical considerations for the oil industry at large. Our study is based on the review of uncertainty in both the subsurface and surface parts of oil and gas production. We refer to the previous classifications of uncertainty for the subsurface part and focus on the uncertainty currently existing within the surface production system. Current surface production monitoring methods are discussed. We propose a more automated process to reduce the current uncertainty about hydrocarbon production. Artificial intelligence applicability in the surface production system is investigated. Additionally, in this paper we review the previously mentioned poor practices and accidents briefly and use them to guide our suggestions about preventive actions to avoid future wrongdoing and incidents. We attempt to provide an objective for oilfield ethics and advocate for a unified oilfield industry code of ethics. However due to the large diversity that exists in the wide range of oilfield disciplines, a single code of ethics may not be applicable. Accordingly, we propose a guideline that may be used by different parts of the oilfield industry to develop an application-specific code of ethics. It is hoped that this guideline will guard against the gaps that may foster errors that affect the oilfield industry and global markets.

The Petroleum Working Group (PWG) of the United Nations Economic Commission for Europe (UNECE) has developed the Petroleum Resource Specifications and Guidelines (PRSG) to facilitate the application of the United Nations Framework Classification for Resources (UNFC) for evaluating and classifying petroleum projects. The UNFC was developed by the Expert Group on Resource Management (EGRM) and covers all resource sectors such as minerals, petroleum, renewable energy, nuclear resources, injection projects, anthropogenic resources and groundwater. It has a unique three- dimensional structure to describe environmental, social and economic viability (E-axis), technical feasibility and maturity (F-axis) and degree of confidence in the resource estimates (G-axis). The UNFC is fully aligned to holistic and sustainable resource management called for by the 2030 Agenda for Sustainable Development (2030 Agenda). UNFC can be used by governments for integrated energy planning, companies for developing business models and the investors in decision making. Internationally, all classification systems and their application continue to evolve to incorporate the latest technical understanding and usage and societal, government and regulatory expectations. The PRSG incorporates key elements from current global petroleum classification systems. Furthermore, it provides a forward-thinking approach to including aspects of integrity and ethics. It expands on the unique differentiator of the UNFC to integrate social and environmental issues in the project evaluation. Several case studies have been carried out (in China, Kuwait, Mexico, Russia, and Uganda) using UNFC. Specifically, PRSG assists in identifying critical social and environmental issues to support their resolution and development sustainably. These issues may be unique to the country, location and projects and mapped using a risk matrix. This may support the development of a road map to resolve potential impediments to project sanction. The release of the PRSG comes at a time of global economic volatility on a national and international level due to the ongoing impact and management of COVID-19, petroleum supply and demand uncertainty and competing national and international interests. Sustainable energy is not only required for industries but for all other social development. It is essential for private sector development, productive capacity building and expansion of trade. It has strong linkages to climate action, health, education, water, food security and woman empowerment. Moreover, enduring complex system considerations in balancing the energy trilemma of reliable supply, affordability, equity, and social and environmental responsibility remain. These overarching conditions make it even more essential to ensure projects are evaluated in a competent, ethical and transparent manner. While considering all the risks, it is also critical to reinforce the positive contribution a natural resource utilization project provides to society. Such an inquiry can focus on how the project contributes to the quality of life, environment, and the economy – the people, planet, and prosperity triad. Such an approach allows consistent, robust and sustainable investment decision making and energy policy development.

We extend the numerically-assisted RTA workflow proposed by Bowie and Ewert (2020) to (a) all fluid systems and (b) finite conductivity fractures. The simple, fully-penetrating planar fracture model proposed is a useful numerical symmetry element model that provides the basis for the work presented in this paper. Results are given for simulated and field data. The linear flow parameter (LFP) is modified to include porosity (LFPꞌ=LFP√ φ ). The original (surface) oil in place (OOIP) is generalized to represent both reservoir oil and reservoir gas condensate systems, using a consistent initial total formation volume factor definition ( B ti ) representing the ratio of a reservoir HCPV containing surface oil in a reservoir oil phase, a reservoir gas phase, or both phases. With known (a) well geometry, (b) fluid initialization (PVT and water saturation), (c) relative permeability relations, and (d) bottomhole pressure (BHP) time variation (above and below saturation pressure), three fundamental relationships exist in terms of LFPꞌ and OOIP. Numerical reservoir simulation is used to define these relationships, providing the foundation for numerical RTA, namely that wells: (1) with the same value of LFPꞌ, the gas, oil and water surface rates will be identical during infinite-acting (IA) behavior; (2) with the same ratio LFPꞌ/OOIP, producing GOR and water cut behavior will be identical for all times, IA and boundary dominated (BD); and (3) with the same values of LFPꞌ and OOIP, rate performance of gas, oil, and water be identical for all times, IA and BD. These observations lead to an efficient, semi-automated process to perform rigorous RTA, assisted by a symmetry element numerical model. The numerical RTA workflow proposed by Bowie and Ewert solves the inherent problems associated with complex superposition and multiphase flow effects involving time and spatial changes in pressure, compositions and PVT properties, saturations, and complex phase mobilities. The numerical RTA workflow decouples multiphase flow data (PVT, initial saturations and relative permeabilities) from well geometry and petrophysical properties ( L, x f , h, n f , φ, k ), providing a rigorous yet efficient and semi-automated approach to define production performance for many wells. Contributions include a technical framework to perform numerical RTA for unconventional wells, irrespective of fluid type. A suite of key diagnostic plots associated with the workflow is provided, with synthetic and field examples used to illustrate the application of numerical simulation to perform rigorous RTA. Semi-analytical models, time, and spatial superposition (convolution), pseudopressure and pseudotime transforms are not required.

Is this the end of petroleum engineering as we know it? This prescient question led to the most downloaded paper from onepetro.org in 2019. The events of 2020 resulted in massive layoffs, decreased hiring and many fewer students studying petroleum engineering. In the 2019 paper the authors claimed that the future would hold fewer petroleum engineering jobs and very different types of jobs. This paper incorporates a broader range of data and proposes some specific ways to improve prospects for the discipline of petroleum engineering. The opportunity for a near-term recovery is very high as the world overcomes COVID-19 issues, oil demand recovers and the impact of chronic underinvestment in oil and gas production looms. The world's largest producers have very different abilities to respond to a near-term uptick in demand. Energy transition pressures continue to cap growth in demand; however, demand for petroleum engineers is expected to grow under almost every scenario, but not to pre-2015 levels. Increased demand in CCUS and jobs that improve sustainability of oil and gas will continue to outpace conventional jobs. Data analytics will play an increasingly large role in engineering activities. The "Is it the end?" paper started with a question, a question that I first heard asked in 1977 at the SPE Annual Fall Technical Conference and Exhibition in Denver to 1972 SPE President M. Scott Kraemer. I have heard it many times since then and asked it many times. "Would you recommend that your son or daughter study petroleum engineering?" The answer to that question was pretty easy and unanimously positive in 1977. Keep this question in mind as we review what has happened since the prior paper came out.

Vaca Muerta is an unconventional reservoir located in Argentina. Since 2014, 397 horizontal wells have been drilled in this formation. The scope of this paper is to summarize the evolution of the productivity and the economics of these wells from 2014 until 2020. The methodology consisted in analyzing the results of the wells grouped by drilling campaign extracting the main parameters such as peak oil rate, cumulative oil at 365 days, number of fracture stages and length of the drain. After the extraction of these parameters we calculated each well's EUR using a modified hyperbolic function. A statistical analysis of the results was performed in order to calculate a "Type Well" for each drilling campaign. An economic evaluation for each campaign was then generated in order determine the "Type Well" economics. Once this was achieved we calculated the production profile of an "Economic Type Well" for each campaign defining this as the well which would generate an NPV=0$ at a discount rate of 10%. This was used to determine how many wells were economical for each year. The results of this study show the big impact on the productivity generated by the increase in well length and the number of frac stages revealing the progression of the "Learning curve" for the Vaca Muerta basin including the reduction in Capital Expenditures per well. At present, Vaca Muerta represents an opportunity as a profitable play due to the high productivity of the reservoir and low risk. Nevertheless, there is still room for improvement in the wells' cost according to the results of this study. This paper presents the evolution of the economics and the risk performance of Vaca Muerta.

In May 2019, the merger between Wintershall GmbH and DEA Deutsche Erdoel AG was closed, which was the start of Wintershall Dea.This paper provides detailed insight into managing reserves and resource information during this merger. After merger, three resource management activities required attention: (i) combining existing resources reporting, (ii) defining a lean but effective resources management and control system, and (iii) ensuring readiness for Initial Public Offering (IPO) by establishing an external independent evaluation of resources ("external resource audit"). This paper describes objectives, challenges and solutions on reserves and resources reporting of the new company. The merged reserves and resources database of the previous year's reports had to consider audits from two different reporting systems in parallel to four different external auditors.With priorities defined by status of external auditing, operatorship and asset share a common database was derived and could immediately be used for financial transactions such as the issuance of an inaugural bond. The new system for internal reporting of petroleum resources provides a fit-for-purpose approach, such as a consistent interpretation of commerciality criteria or definition of resources sub-classes.Particular attention was paid to synergies with respect to business planning, strategic portfolio analysis, and a link to technology & innovation. By defining specific attributes and sub-processes, the portfolio can be analyzed systematically. This provides additional insights and ensures synergies with business planning, strategic planning as well as internal technology initiatives. A systematic resource control system is defined focusing on internal review, external and internal audits as well as synergetic use of project reviews. Moreover, a feedback loop for continuous improvement of reservoir management allows attending to important audit observations. The external resource audit to ensure IPO readiness was structured to assign tasks for head office, business units and auditing company.The sequence of events from introduction to assets to reconciliation of differences between auditor and company was set-up, executed and monitored.Focus was on the definition of a structured but agile approach for external independent evaluation of all reserves and contingent resources.

In 2011, the Society of Petroleum Evaluation Engineers (SPEE) published Monograph 3 as an industry guideline for reserves evaluation of unconventionals, especially for probabilistic approaches. This paper illustrates the workflow recommended by Monograph 3. The authors also point out some dilemmas one may encounter when applying the guidelines. Finally, the authors suggest remedies to mitigate limitations and improve the utility of the approach. This case study includes about 300 producing shale wells in the Permian Basin. Referring to Monograph 3, analogous wells were identified based on location, geology, drilling-and-completion (D&C) technology; Technically Recoverable Resources (TRRs) of these analogous wells were then evaluated by Decline Curve Analysis (DCA). Next, five type-wells were developed with different statistical characteristics. Lastly, a number of drilling opportunities were identified and, consequently, a Monte Carlo simulation was conducted to develop a statistical distribution for undeveloped locations in each type-well area. The authors demonstrated the use of probit plots and demonstrated the binning strategy, which could best represent the study area. The authors tuned the binning strategy based on multiple yardsticks, including median values of normalized TRRs per lateral length, slopes of the distribution lines in lognormal plots, ratios of P10 over P90, and well counts in each type-well category in addition to other variables. The binning trials were based on different geographic areas, producing reservoirs, and operators, and included the relatively new concept of a "learning curve" introduced by the Society of Petroleum Engineers (SPE) 2018 Petroleum Resources Management System (PRMS). To the best of the authors’ knowledge, this paper represents the first published case study to factor in the "learning curves" method. This paper automated the illustrated workflow through coded database queries or manipulation, which resulted in high efficiencies for multiple trials on binning strategy. The demonstrated case study illustrates valid decision-making processes based on data analytics. The case study further identifies methods to eliminate bias, and present independent objective reserves evaluations. Most of the challenges and situations herein are not fully addressed in Monograph 3 and are not documented in the regulations of the U.S. Security and Exchange Commission (SEC) or in the PRMS guidelines. While there may be differing approaches, and some analysts may prefer alternate methods, the authors believe that the items presented herein will benefit many who are starting to incorporate Monograph 3 in their work process. The authors hope that this paper will encourage additional discussion in our industry.

As most oilfields in Ecuador are approaching to the end of the service contracts under an advanced degree of maturity, it was imperative to implement a fast-track integrated methodology that supports the decisionmaking process during assets' evaluation. This practice aimed to identify new business opportunities and assure the rehabilitation of brownfields. These fields became a target for investors willing to intervene in new joint ventures with moderate risk to boost production and returns. The methodology is prepared to overcome specific challenges such as severe reservoir pressure depletion, harsh water management issues, facilities constraints and integrity. All this while keeping economics and safe operational standards. This process is divided into five stages: First, the diagnosis of field challenges and associated risks, so that review the current status of subsurface and surface aspects. Then, the following three parallel phases are focused on the study of reservoir architecture, dynamics and performance. Finally, the remaining potential of the asset is assessed by integrating action plans to take advantage of current facilities capacities. This workflow was implemented for the evaluation of three assets: Asset 1: Mature field with a secondary gas cap where its current reservoir pressure is 800 psia (initial pressure 4,200 psia). The asset was evaluated in fifteen (15) days resulting in an integrated solution with 14 activities: conversions to injectors, water source, upsizing, reactivations, change zone, and new wells. The results presented an incremental recovery factor of 6% (by 2028) with an expected production peak of 3,500 BOPD (by 2021). Asset 2: A field producing from two main reservoirs with harsh water management issues under a non-monitored waterflooding scheme with challenging sweet spots identification was evaluated in 10 days, resulting in a redevelopment plan considering: production losses optimization, sixteen (16) activities: workovers, dual completions, new wells, reentry, shut-in, and conversion to water injectors. This evaluation delivered an incremental recovery factor of 10% (by 2029). Asset 3: Producing for around one-hundred (100) years with 3,000 wells drilled. There was a lack of pressure support and facilities and well completions integrity. The fast-track assessment focused on production optimization lasted fifteen (15) days, resulting in one-hundred eighteen (118) wells for reactivation representing an additional recovery factor of 3% (by 2029). This work supported the process for contract's renegotiation and assets' acquisition. This integrated methodology aimed to maximize the assets' value while considering the involved shareholders' needs. Each asset was analysed in an integrated and collaborative manner through the propper resources identification and the usage of the latest technology and workflows. High-resolution reservoir simulation, complex python scripts, and a chemical processes simulator were used to perform an in-depth evaluation and meet the expectations.

Reserves Replacement Ratio (RRR) has been used as a Key Performance Indicator (KPI) for monitoring the ability of IOCs to replace their reserves through time. It provides valuable information about the expected long-term viability of the company and its ability to move forward with new projects. If this remains an important metric for investors and operators alike, it is worth understanding the key parameters behind this KPI. How should we realistically expect the RRR to behave in the future? What does it tell about the reserves and resources of the operator? The objective of this document is to investigate how a widely underused KPI can be analyzed and forecasted based on previous reserves and business plan submissions. We suggest a method to provide teams with relevant and realistic RRR, that can be expected based on the company portfolio. This method was developed by reviewing the Reserves & Resources in hundreds of reservoirs for all projects, including No Further Activity (NFA) cases and all future development projects. Based on the future profiles, the evaluation of the RRR was forecasted for 10 years. The forecasts are being reviewed year on year to ensure that teams are provided with realistic KPIs based on hard data rather than biased expectations. The RRR is computed using (i) the amount of reserves added during the year, divided by (ii) the production of the company during the same year. This paper will focus on the reserves changes from one year to another, considering that reserves addition (or reduction), comes from two main sources: Project maturation: as projects are maturing on the SPE PRMS vertical axis, they will eventually become commercial according to the PRMS definition and move from contingent resources to reserves. Project maturation is driven by the asset team and by the company's willingness to move forward with the development. Technical review: as more data becomes available, the reserves of the asset will change, up or down, within (hopefully) the range of uncertainty defined in PRMS. This is driven by the inherent uncertainty in the reservoir. The control that the company has on this parameter is very limited. This document emphasizes on the impact of the uncertainty captured by the asset team during the reserves evaluation exercise, and how it will (should) affect the KPI calculations. Finally, we will discuss what should be a reasonable KPI depending on the PRMS submissions, 1P, 2P or 3P. Eventually, this method should provide a healthy middle ground between asset teams being too pessimistic in setting their KPIs, and managers who are keen to set challenging, and sometime unrealistic, targets

This paper outlines the systematic approach adopted in carrying out post-production fluid content and contact analysis in a mature Niger Delta field with more than 50 years production of oil and gas in multi-stacked reservoirs with varying fluid properties and moderate to high rock properties. The surveillance data acquired revealed varying dynamic behavioral patterns in the reservoirs. The result was a vital guide in rigless production adding intervention activities, in addition to well placement decisions. A sand penetration chart was developed from the initial analysis and stacking of the fluid distribution in all the reservoirs encountered by the wells defining the valuable wells to be logged. The volumetric and production information from the reservoirs were reviewed for reservoir ranking purposes. The structural location on the wells was also a screening consideration for the well selection. Having established the reservoir viability, the wellbore accessibility was investigated and wells for logging were ranked based on all the parameters earlier established. The logging activities were sequenced to minimize production deferment. Processing and interpretation of data acquired was an iterative process between the logging contractor and the asset operator. Post-production Carbon/Oxygen saturation logging was carried out in 2018 with 8 out of 40 wells logged. The results of the data acquisition revealed different dynamic behavior patterns in the reservoirs logged. Some reservoirs exhibited non uniform flushing, as crestal wells were observed to be more flushed than flank wells in the same reservoir. Reservoirs earlier interpreted to be oil-bearing were logged and interpreted to be gas-bearing, indicating that a secondary gas-cap had developed Some gas-bearing reservoir columns were observed to be re-saturated with oil, resulting in additional scope for oil completion in some wells Reservoirs with high viscous crude showed patchy drainage patterns with a lot of by-passed oil in wells close to the vicinity of high BSW producing wells. Few reservoirs also exhibited piston-like production pattern The variability in the result indicates that the reservoir system is not as simple as previously interpreted. Though the reservoirs are continuous, and the rock properties are generally good, there is likelihood of the existence of baffles, which also have controlling influence on the dynamic behavior of the reservoir. However, the results were a key input in identifying intervention activities and optimal in-field well placement. Subsurface engineers most times have the understanding that the dynamic behavior in the Niger Delta reservoirs is controlled mainly by the structural configuration due to the continuity and good rock properties of the reservoirs, however this is not necessarily the case in all situations as stratigraphic components can also have a controlling influence. Acquiring time-lapse saturation data for calibration and better understanding of reservoir dynamics is vital for optimal reservoir development.

Two recent papers (URTeC 3003052 and SPE 200626) have presented detailed investigations of reasons why proved reserves estimates based on PRMS principles and on SEC regulations differ by substantial amounts in several instances. This paper synthesizes and extends this previous work and addresses implications of differences and reasons for differences. We examined the relatively limited number of cases in which reserves filers have reported reserves as of a given date based on both PRMS definitions and SEC regulations. To understand the differences, we also reviewed numerous annual reports and comment letter exchanges between reserves filers and the SEC staff during the period 2009 to 2019. We devoted attention to identify differences that might arise from different interpretations of reserves definitions adopted by the two different systems. Since COGEH is quite like PRMS regarding the use of forecast prices and costs, we extended our analysis to include comparisons with COGEH proved reserves when addressing prices and costs as root causes for the differences. While we examined many potential reasons why proved reserves using SEC and PRMS definitions and principles are different, we found three that appeared to be dominant. The first is that the SEC requires use of current prices in reserves estimates whereas PRMS (and COGEH) allow use of forecasted prices (often escalated) for sales volumes. The second is that PRMS requires firm evidence that a recovery project for resources has been fully approved and funded to reach classification as reserves with the status of "Approved for Development," but also allows recovery projects which are reasonably expected to receive approval to be classified as reserves with the status of "Justified for Development." Examination of comment letter exchanges indicates that the SEC also requires "reasonable certainty" that a "Final Investment Decision" will be reached to classify resources as reserves, essentially the same as the PRMS requirement for "Justified for Development" status. Comment letter exchanges indicate that this is the SEC staff's customary practice even though a literal reading of SEC guidance might suggest a stricter standard. The implication is that some reserves filers may unknowingly limit their reserves bookings, which might not be in the filers' or other stakeholders' best interests. The third reason is the five-year SEC development limitation (unless specific circumstances justify a longer time). Interpretation of reserves disclosure requirements in principles-based regulations based on how they are enforced in practice may serve investors and owners of resources better than assumptions based on literal readings of these regulations.

In the current move to a low-carbon and low-crude-oil-price world, numerous existing steam flood projects need to be economically and environmentally re-evaluated. Because of the high cost of steam generation and high associated carbon emissions, these projects will require increased attention and assessment. Traditionally, reservoir simulation is a crucial part of field evaluation and project economic forecasting. However, large-scale steam flood projects can cover thousands of acres, making simulation challenging. Forecasting of steam flood performance based on reservoir simulation is effective when addressing the complicated geological structure and heterogeneous reservoir properties in real reservoirs. However, large scale simulation of steam floods is still not computationally efficient. A fine grid is required to capture the steam/condensing waterfront, thus the simulation efficiency is difficult to improve by enlarging the size of the grid. At field-scale, running a simulation in the millions or even billions of grids is not a practical approach. Thus, this paper uses a semi-conceptual method named "Pattern-Based Modeling (PBM)" to generate the fast and accurate forecast for large steam projects. Instead of using grids, PBM uses patterns as the foundation of modeling. A pattern is a flow regime with the special arrangement of producers and injectors. For steam flood projects, the size of a pattern is measured in acres which is thousands of times greater than the size of grid. The upscaling from grid to pattern is valid because (a) the heavy crude field usually has high permeability and porosity sandstone; (b) heavy crude oil is almost immobile unless heated by the steam while steam has a huge mobility ratio. Those physics phenomena substantially reduces the effect of heterogeneity. In the field and lab observations, steam always goes to the impermeable overburden immediately due to the vast density differences. The steam chest is formed after steam reaches the bottom of producers. The steam chamber, which contains saturated steam, pushes the oil below using the latent heat. In a single pattern, the oil rate type curve is constructed by two mechanisms: (a) the frontal displacement (Marx-Langenheim) before steam breakthrough; (b) the gravity drainage (Vogel) after steam breakthrough. Then the oil rate type curve is created based on these two mechanisms. The paper also provided a multi-temperature fractional flow method to forecast the water cut. The oil and water rate forecast in the single pattern is validated by reservoir simulation. The paper runs numerous pattern simulations in multiple scenarios. The results indicate that the oil and water rate is independent of well placement after assuming the homogeneity of reservoir properties in the single pattern. The number of injector and producer will also not affect the oil and water rate, which means that the well arrangement (invert five-spot, nine-spot, etc.) would likely to generate similar results. Although the reservoir properties (porosity, permeability, initial saturation, etc.) in a single pattern are homogeneous, they can vary between patterns. The PBM has the flexibility that for single pattern, the type curve can be generated based on the individual reservoir properties of that pattern. Each pattern is assigned a starting date. Then the field rate is the accumulation of each pattern. The single pattern reservoir properties can be adjusted during the rate history match. For the steam performance forecast, the paper introduced a statistical correlation, which indicated that besides the reservoir properties, the surface steam operation would also affect the ultimate recovery (EUR). High steam injection rate results in high steam chest pressure (or temperature), and thus reduces the thermal latent heat, and thus decrease EUR. The paper has conducted a case studies for the demonstration: the Kern River Field (USA) with a long history of steam flood. The paper showed that PBM generated a more than 90% correlation coefficient during the history matching. Based on the correlation, the Kern River thermal EUR within current operation is about 87 % of in place resources.

The objective of the present work is to streamline the analysis of Lease Operating Statements (LOS) with advanced learning paradigms from artificial intelligence (AI). The proposed approach aims at the: (a) consolidation of disparate expenses data; (b) timely expense assessment at field, pad or well level; (c) prevention and quick identification of negative cash flow trends (d) robust LOS predictions; and (e) optimal budget planning under uncertainty. To achieve this objective, a LOS acceleration platform was developed for the automatic integration of volume estimation with Lease Operating Expenses (LOE). Historical data from production volumes, revenues, price differentials, LOEs, marketing and transportation costs, taxes, CAPEX, P&A costs and others considered to strengthen the accuracy of individual expense categories and the overall LOS predictive model. The predictive model consists of a combined blend of analytical and machine learning models that allows to reliably forecast trends and proactively detect anomalies that may be negatively affecting the operational cash flow. Intuitive and portable visualizations allow a quick interpretation and communication of results among engineers and managers. The implemented platform fills a gap between traditional LOS analysis and preemptive expense planning involving many wells and expense categories that are hard to track daily. It is shown that the proposed approach can lead to savings of the order of 30% in incurred expenses.

Recovery efficiency (RE) is a poorly constrained parameter with respect to unconventional resource development. However, the increasing volume and quality of data related to Marcellus development in West Virginia provide an opportunity to assess this parameter. In this report, we develop a map of estimated ultimate recovery (EUR) per square mile (mi 2 ) for the Marcellus Play in West Virginia and compare that map to published estimates of both technically recoverable resources (TRR) and original gas-in-place resources (GIP). These comparisons reveal that Marcellus wells appear capable of producing significantly more gas than previously estimated and, throughout large areas of the State, more gas than has been assessed to be in place. While explanations for this observation likely include both potential overestimation of EUR and underestimation of GIP, our review indicates that a primary source of the discrepancy is overly-conservative GIP calculations. New GIP calculations have been conducted for a range of wells that appear to account for the bulk of the "excess gas" by including GIP volumes from bounding formations determined likely to be part of the larger Marcellus Reservoir Unit (MRU). In northeastern West Virginia, the MRU includes both the Marcellus Formation and the lower 300 ft of the Mahantango Formation. In northwestern West Virginia, units up to the Cashaqua Shale Member of the Sonyea Formation are included in the MRU. These new GIP/mi 2 values, when compared with our estimates of TRR/mi 2 , indicate that ultimate RE for ongoing development generally ranges between 20 to 60%. Our approach to estimate TRR/mi 2 is based on identification of 166 Marcellus "development sites" in northern West Virginia. Each development site was selected to consist of multiple horizontal wells that were drilled as a coordinated activity at a common spacing over a short period of time by a single operator. Sites and wells also were selected to maximize both the relevance (post 2012 vintage wells where possible) and reliability (more than 18 months production history) of the EUR forecasts. Site area was determined as the product of cumulative lateral length and well spacing. The cumulative EURs for all wells in each site were estimated (using two independent sources for EUR estimation) and those sums were divided by the area to obtain a single point estimate of TRR/mi 2 . Our review indicates that Marcellus Play resource assessments tend to be overly conservative. This analysis shows the value of focusing on the most relevant production data and using those data as a ground truth check on resource assessments.