Uncertainty range in production forecasting gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts. Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than "proved." This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively. It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts.
The basic objective of this course is to introduce the overview and concept of production optimisation, using nodal analysis as a tool in production optimisation and enhancement. The participants are exposed to the analysis of various elements that help in production system starting from reservoir to surface processing facilities and their effect on the performance of the total production system. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Agrawal, Gaurav (Schlumberger) | Kumar, Ajit (Schlumberger) | Mishra, Siddharth (Schlumberger) | Dutta, Shaktim (Schlumberger) | Khambra, Isha (Schlumberger) | Chaudhary, Sunil (ONGC) | Sarma, K. V. (ONGC) | Murthy, M. S. (ONGC)
Objectives/Scope: XYZ is one of the marginal fields of Mumbai Offshore Basin located in western continental shelf of India. Wells in this field were put on ESP for increasing the production. Regular production profiling with traditional production logging was done in these wells to ascertain the water producing zones if any and do the subsequent well intervention if required.
Methods, Procedures, Process: In few deviated wells with low reservoir pressure, low flow rates and large casing size, massive recirculation was observed due to which spinner readings were highly affected. In such scenarios, quantitative interpretation with conventional production logging is highly difficult. Only qualitative interpretation based on temperature and holdup measurements can be made which might not completely fulfill the objective. In one of the deviated wells, massive recirculation was observed due to large casing size. Recirculation on ESP wells is generally not expected due to high energy pressure drawdown exerted on the well. Traditional production logging imposed difficulty in interpretation due to recirculation. Only qualitative interpretation was made from temperature and holdup measurements. Hence advanced production logging tool called Flow Scan Imager (FSI*) with 5 minispinners, 6 sets of electrical and optical probes, designed for highly deviated and horizontal wells to delineate flow affected due to well trajectory, was suggested for quantitative interpretation in such wells suffering with recirculation.
Results, Observations, Conclusions: In the next well, production profiling was to be done before ESP installation in similar completion as the last well. Therefore, huge recirculation phenomenon was expected in the well. FSI was proposed in this deviated well with recirculation for production profiling and also for finding out the complex flow regime inside the wellbore. FSI helped in proper visualization of the downhole flow regime with the help of multispinners and probes. Quantitative interpretation was made with the help of FSI data. Also, quantification was confirmed inside the tubing (lesser cross section area) where no recirculation is expected as the mini spinner does not collapse inside the wellbore. In traditional production logging, it is generally not possible due to the collapsing of full bore spinners inside tubing. Better understanding of the flow regime can be obtained with FSI than conventional production logging due to the presence of multiple sensors. Later interventions using FSI results have shown significant oil gains.
Novel/Additive Information: FSI was used in deviated ESP wells with recirculation for production profiling, accurate quantification, better understanding of flow regimes and to take improved well intervention decisions.
A well-designed pilot is instrumental in reducing uncertainty for the full-field implementation of improved oil recovery (IOR) operations. Traditional model-based approaches for brown-field pilot analysis can be computationally expensive as it involves probabilistic history matching first to historical field data and then to probabilistic pilot data. This paper proposes a practical approach that combines reservoir simulations and data analytics to quantify the effectiveness of brown-field pilot projects.
In our approach, an ensemble of simulations are first performed on models based on prior distributions of subsurface uncertainties and then results for simulated historical data, simulated pilot data and ob jective functions are assembled into a database. The distribution of simulated pilot data and ob jective functions are then conditioned to actual field data using the Data-Space Inversion (DSI) technique, which circumvents the difficulties of traditional history matching. The samples from DSI, conditioned to the observed historical data, are next processed using the Ensemble Variance Analysis (EVA) method to quantify the expected uncertainty reduction of ob jective functions given the pilot data, which provides a metric to ob jectively measure the effectiveness of the pilot and compare the effectiveness of different pilot measurements and designs. Finally, the conditioned samples from DSI can also be used with the classification and regression tree (CART) method to construct signpost trees, which provides an intuitive interpretation of pilot data in terms of implications for ob jective functions.
We demonstrate the practical usefulness of the proposed approach through an application to a brown-field naturally fractured reservoir (NFR) to quantify the expected uncertainty reduction and Value of Information (VOI) of a waterflood pilot following more than 10 years of primary depletion. NFRs are notoriously hard to history match due to their extreme heterogeneity and difficult parameterization; the additional need for pilot analysis in this case further compounds the problem. Using the proposed approach, the effectiveness of a pilot can be evaluated, and signposts can be constructed without explicitly history matching the simulation model. This allows ob jective and efficient comparison of different pilot design alternatives and intuitive interpretation of pilot outcomes. We stress that the only input to the workflow is a reasonably sized ensemble of prior simulations runs (about 200 in this case), i.e., the difficult and tedious task of creating history-matched models is avoided. Once the simulation database is assembled, the data analytics workflow, which entails DSI, EVA, and CART, can be completed within minutes.
To the best of our knowledge, this is the first time the DSI-EVA-CART workflow is proposed and applied to a field case. It is one of the few pilot-evaluation methods that is computationally efficient for practical cases. We expect it to be useful for engineers designing IOR pilot for brown fields with complex reservoir models.
In today's fast paced and challenging oil industry, the need of faster evaluation studies for quick generation of field development plan (FDP) is becoming more crucial to remain competitive. Field's geological and structural complexity, uncertainty of production data adds to the challenges. Traditional approach of building dynamic mesh models carrying out numerical simulation to history match, then predict has always remained time consuming in large mature fields.
The ‘B’ field in Peninsular Malaysia is a mature clastic with stacked reservoirs having a huge gas cap with moderate aquifer. Significant production over last 30+ years led to uneven movement of the gas cap and also of the edge aquifer leading to possibility of bypassed oil. The updated dynamic model could not match the preferential gas cap movement, thus failed to match the high GOR of downdip wells and also unable to match high watercut of certain updip wells. To identify the areas of bypassed oil thus is a significant challenge with the current dynamic model. New engineering tools of polygon balancing, material balance, normalized EUR bubbles were used with the 3D static model volume and the facies understanding. The uncertainties and risks were also identified and clear measurable methods were proposed to address the uncertainties and reduce the risks. Very detailed decision tree with clear data gathering plan to drill successive optimum wells have been planned during the campaign.
This paper details the new engineering tools used to delineate and quantify the bypassed oil in these huge clastic reservoir with preferential gas and water movement, unable to be history matched by the dynamic model. It explains the engineering methods applied to identify and quantify the 10 infill wells proposed for the development campaign. To reduce risks, this paper would also explain the blind testing that was carried out on for this new reservoir engineering analysis tool by deriving the infill potentials of the previous campaign (4 years back) by the same method.
The paper details how robust technical development plans were generated having infill well locations and reserve determination. This paper will also demonstrate the classic "Do-Learn-Adapt" strategy through its infill wells prioritization & ranking, subsurface de-risking analysis, data acquisition and mitigations plans.
Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Putra, Dike (Rafflesia Energy) | Asena, Ahmet (Turkish Petroleum Corp.) | Ranjith, Rahul (Far Technologies) | Jongkittinarukorn, Kittiphong (Chulalongkorn University)
Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way.
History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field.
Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases.
Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.
Founded in 2005, the International Petroleum Technology Conference (IPTC) continues to be the flagship multidisciplinary technical event in the Eastern Hemisphere. The conference content and associated programmes will address the technology and related industry issues that challenge management, established petroleum professionals and young people seeking to join our industry from around the world. IPTC is focused on the dissemination of new and current technology, best practices and multi-disciplinary activities, emphasising the importance of the value chain, and multidisciplinary cooperation in order to maximise asset value. IPTC is sponsored by four professional associations, the American Association of Petroleum Geologists (AAPG); the European Association of Geoscientists and Engineers (EAGE); the Society of Exploration Geophysicists (SEG); and the Society of Petroleum Engineers (SPE). The synergy of these four leading, member driven organisations, through the formation of knowledgeable and experienced committees comprising senior leaders and experts, supportive of the IPTC mission, has ensured another outstanding technical programme consistent with previous editions of IPTC.
By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.