Ghazali, Ahmad Riza (PETRONAS) | Abdul Rahim, M. Faizal (PETRONAS) | Mad Zahir, M. Hafizal (PETRONAS) | Muhammad, M. Daniel Davis (PETRONAS) | Mohammad, M. Afzan (PETRONAS) | A. Aziz, Khairul Mustaqim (PETRONAS)
The key objectives were to achieve better seismic resolution and spatial delineation in very heterogeneous reservoirs. We decided to supplement simultaneously the surface 3D multi component seismic acquisition by placing additional fiber optic live receivers in the subsurface via a "True-3D" experiment without shutting down the oil production. The most cost-effective method to snapshot this wavefield propagation downhole is by utilizing fiber optic Distributed Acoustic Sensing (DAS). The borehole 3D VSP data were acquired by sharing the surface OBN nodal survey airgun sources. This is an important experiment for the field in the future so that the need to halt insitu field production for 4D time lapse monitoring will not be required if the S/N is acceptable by using this method. This permanent installation of fiber optic cables has become our ears on wells, not only for 3D DAS VSP but for proactive monitoring of the field, ensuring optimum production performance throughout the life of the field.
This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. 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.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.
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 results of an investigative research study on the impact of the in-situ stress, shale matrix composition, maturity, amount of organic matter and clay composition affecting the anisotropy level of the geomechanical properties have been discussed in this paper. These parameters are among the key factors known to control the geomechanical properties in organic-rich shale formations. Organic-rich shale formations with different mineralogical compositions and organic matter maturity have been measured under uniaxial and triaxial stress state along with the field data from limited number of the wells in these shale basins where the core samples are obtained to investigate the role of each factor on the level of geomechanical anisotropy.
The field data has been analyzed to compare the trends obtained from the laboratory data collected under customized controlled field conditions to the field data trends. Artificial Neural Network (ANN) analysis was used in wells without full log suits to obtain the anisotropic geomechanical parameters. The results highlight the maturation, organic richness and clay composition effect on the recorded field data as well as the geomechanical properties obtained from the laboratory measurements.
The stress and fluid sensitivity of shale formations have been well recognized since the early days of conventional reservoir drilling, completion and production operations as they typically require special attention for minimizing wellbore instability during drilling and maintaining high integrity wells throughout the life cycle of these wells. Shales are highly heterogeneous and anisotropic formations and their source rock characteristics also have introduced further complexities with the organic matter and compositional variations throughout the areal extent of the reservoirs. These variations and their alterations as a function of the level of maturity of the organic matter require further study for better understanding of the differences and similarities between the seal shales and reservoir shales and the role of the organic matter and its maturity level in these differences. One of the critical aspects of the organic matter presence is in quantification of shale mechanical properties and strength and their direction dependence for successful field development. The level of maturity of the organic matter also influences the mechanical, acoustic, petrophysical and failure properties of organic rich shale formations. The mineralogical composition typically deviates from carbonate rich to quartz rich in the rock matrix with clay and organic matter amount and distribution heterogeneity in the reservoir. The layered structure introduced by the depositional history of the formation along with the heterogeneity in the distribution of organic matter result in various degree of anisotropy in reservoir properties (Sondergeld and Rai, 2011; Vernik and Milovac, 2011). A better understanding on the anisotropic characteristics of the shale formations and key parameters impacting the anisotropy is essential for field operational success from exploration studies for seismic attributes to reservoir characterization, drilling and hydraulic fracture design and production optimization.
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.