The Spraberry trend area is part of a larger oil-producing region within the Midland basin in United States. The main targets, Spraberry, Dean and Wolfcamp, are reservoirs of shales interbedded with clastic formations. Thus, the reservoirs exhibit TIV (transverse isotropy vertical) anisotropy due to thin laminations. A pilot well was drilled vertically in the complex lithology and logged with the advanced acoustics measurements. Shallow penetration of Stoneley energy into the formation raised concerns about the depth resolution of the inverted shear slowness derived from it. It is very difficult to get a reliable horizontal shear slowness from Stoneley when the borehole condition is rugose, there is a complex mud rheology and gas influx inside the borehole.
A machine learning based approach integrating the advanced acoustics measurements and petrophysical interpretation is adopted to provide the solution to get the lithology-based horizontal shear slowness. To eliminate the variability of getting the horizontal shear slowness from Stoneley wave, to process for an advanced geomechanics product like for TIV anisotropy analysis, two machine learning algorithms are used. First one is a very commonly used linear supervised learning algorithm multi-linear regression (MLR) and second is random forest (RF) a nonlinear supervised learning algorithm. These algorithms take inputs from formation evaluation and advanced acoustics to predict the horizontal shear slowness. The random forest algorithm being an ensemble learning method have greater predictive capabilities compared with any linear supervised learning models and many of the non-linear supervised learning algorithms. The inputs for RF and MLR regressions are values of dry weight fractions of calcite, dolomite, quartz, illite, total porosity, permeability, gamma ray, compressional slowness and fast shear slowness. These values are obtained for the entire depth of interest from advance logging tools and interpretation techniques. To check the performance of the model, standard machine learning techniques such as the error evaluation metrics of the mean squared error and the coefficient of determination (
Hyperparameter tuning of the RF model has been done to improve upon the prediction accuracy. After the parameters are tuned, the mean squared error and
Kianinejad, Amir (Quantum Reservoir Impact, LLC) | Kansao, Rami (Quantum Reservoir Impact, LLC) | Maqui, Agustin (Quantum Reservoir Impact, LLC) | Kadlag, Rahul (Quantum Reservoir Impact, LLC) | Hetz, Gill (Quantum Reservoir Impact, LLC) | Ibrahima, Fayadhoi (Quantum Reservoir Impact, LLC) | Suicmez, Vural (Quantum Reservoir Impact, LLC) | Darabi, Hamed (Quantum Reservoir Impact, LLC) | Castineira, David (Quantum Reservoir Impact, LLC)
Decline curve analysis (DCA) is one of the most widely used forms of data analysis that evaluates well behavior and forecasts future well and field production and reserves. Usually, this practice is done manually, making analysis of assets with a large number of wells cumbersome and time-consuming. Moreover, results are subject to alternate interpretations, mostly as a function of experience and objectives of the evaluator.
In this work, despite the common practice of the industry, i.e. manual DCA, we developed and deployed cutting-edge technologies that intelligently apply DCA methods to any number of wells in an unbiased, systematic, intelligent, and automated fashion. The tool reads production data, and multidisciplinary well information (e.g., drilling and completion data, geological data, artificial lift information, etc.). Then it performs cluster analysis using unsupervised machine learning and pattern recognition to partition the dataset into internally homogeneous and externally distinct groups. This cluster analysis is later used for type-curve generation for wells with short production history. For wells with long enough history, the tool first detects production events through a fully automated event detection algorithm without any human interference. Since production events are highly correlated with real-time events, it also cross-validates with the operating conditions. Next, the last event is selected, and a decline curve is fitted using advanced nonlinear optimization and minimization algorithms. This leads to a reliable and unbiased prediction. For each cluster, a type curve is computed that truly captures the underlying production behavior of the wells that belong to the same group or cluster, and then is applied to the wells with short production history within that cluster. To capture the probabilistic nature of such analysis and quantify the inherent uncertainty, we extended the method to a probabilistic DCA using quantile regression.
We successfully deployed this technology/tool to a giant Middle Eastern reservoir, with more than 2,000 wells and 70 years of production. Our predicted aggregated field decline rate is in good agreement with the client's reservoir simulation results run under the "do-nothing" scenario. While performing traditional DCA for such a field would require several weeks and significant resources, our automated solution integrates all real-life events/information and provides a comprehensive analysis in field, cluster and well level. In addition, our results are "unbiased," as it is not subject to human errors or evaluator's interpretations.
Our robust and intelligent DCA allows for exhaustive evaluation of production trends and opportunities in fields across time, production zones, well types, and any combinations of the above. The results demonstrate the effectiveness of the automated DCA to rapidly execute decline curve analysis for a large number of wells. The accuracy is improved significantly through automatic event detection, cross-validation of events, curve fitting optimization, quantile regression, and cluster-based type-curving.
A major outstanding challenge in developing unconventional wells is determining the optimal cluster spacing. The spacing between perforation clusters influences hydraulic fracture geometry, drainage volume, production rates, and the estimated ultimate recovery (EUR) of a well. This paper systematically examines the impact of cluster spacing in the Eagle Ford shale wells by calibrating fracture geometry and fracture/reservoir properties using field injection and production data and evaluating the optimal cluster spacing under different reservoir conditions.
We explore a sequential technique to evaluate and optimize cluster spacing using a controlled field test at the Eagle Ford field. This study first identifies the fracture geometry by history matching the field injection treatment pressure. Using the rapid Fast Marching Method based flow simulation and Pareto-based multi-objective history matching, we match the well drainage volume and the cumulative production to calibrate the fracture and SRV properties. The impact of cluster spacing on the EUR are examined using the calibrated models. We run injection and production forecasts for various cluster spacing to investigate optimal completion under different reservoir conditions.
The unique set of injection and production data used for this study includes two horizontal wells completed side by side. The well with tighter cluster spacing has larger drainage volume and better production performance. This is because of the increased fracture complexity in spite of the impact of stress shadow effects leading to shorter fractures. The calibrated models suggest that most of the fractures are planar in the Eagle Ford shale. The well with wider cluster spacing tends to develop longer fractures but the well with tighter cluster spacing has better stimulated reservoir volume with enhanced permeability, thus resulting in better drainage volume and production performance. From the optimization runs under different reservoir conditions, our results seem to indicate that when natural fractures are present or when stress anisotropy is high with no natural fractures, the wells with tighter cluster spacing tend to outperform the wells with wider cluster spacing. However, severe stress shadow effect is observed when stress anisotropy is low with no natural fractures, likely making tighter cluster spacing wells less favorable.
The calibrated fracture geometries and properties with a unique set of Eagle Ford field data explain the performance variation for completions using different cluster spacing within the reservoir and provides insight into optimal cluster spacing under different reservoir conditions (low vs high stress anisotropy and with/without natural fractures).
With the industry shifting gears toward pad development there has been a significant increase in operator press releases to stockholders expressing concern about fracture driven interactions (formerly called "frac hits") within a drilling spacing unit (DSU) (
Depletion Mitigation Opportunities Depletion Mitigation Results Infill Well Asymmetric Frac in Toe Stage with Depleted Primary Well Overlap
Depletion Mitigation Opportunities
Depletion Mitigation Results
Infill Well Asymmetric Frac in Toe Stage with Depleted Primary Well Overlap
Historically, refrac operations in horizontal organic shale wells have had unpredictable production results, with the industry moving toward mechanical isolation following an often painful history that included single stage "pump and really pray" treatments with no diversion to "pump and pray" with chemical or ball sealer diversion. While results from mechanical isolation have been more consistent than these first two methods (
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.
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.
A passive tracer that labels gas or water in a well-to-well tracer test must fulfill the following criteria. It must have a very low detection limit, must be stable under reservoir conditions, must follow the phase that is being tagged and have a minimal partitioning into other phases, must have no adsorption to rock material, and must have minimal environmental consequences. The tracers discussed in the following sections have properties that make them suitable for application in well-to-well test in which dilution volumes are large. For small fields in which the requirement with respect to dilution is less important, other tracers can be applied. Figure 1.1 – Production curve of S14CN compared with the production curve of HTO in a dynamic flooding laboratory test (carbonate rock) (after Bjørnstad and Maggio). There are no possibilities for thermal degradation, and it follows the water closely. The 36Cl- is a long-lived nuclide (3 105 years), and the detection method is atomic mass spectroscopy rather than radiation measurements. The disadvantage is that the analysis demands very sophisticated equipment and is relatively time consuming. For mono-valent anions, the retention factors (see Eq. 6.2) are in the range of 0 to -0.03, which means that such tracers pass faster through the reservoir rock than the water itself (represented by HTO). A compound such as 35SO42- may be applied in some very specific cases but should be avoided normally because of absorption. Some anionic tracers may show complex behavior. Radioactive iodine (125I- and 131I-) breaks through before water but has a substantially longer tail than HTO. Both a reversible sorption and ion exclusion seem to play a role here. Cationic tracers are, in general, not applicable; however, experiments have qualified 22Na as an applicable water tracer in highly saline (total dissolved solids concentration seawater salinity) waters. In such waters, the nonradioactive sodium will operate as a molecular carrier for the tracer molecule. Retention factor has been measured in the range of 0.07 (see Eq. 6.2) at reservoir conditions in carbonate rock (chalk). Wood reported the use of 134Cs, 137Cs, 57Co, and 60Co cations as tracers.
Interwell tracer tests are widely used. This article reviews some of the studies reported in open literature. The selection introduces different problems that have been addressed, but the original papers should be studied to obtain a more detailed description of the programs. The Snorre field is a giant oil reservoir (sandstone) in the Norwegian sector of the North Sea. Injection water and gas were monitored with tracers, 18 and the resulting tracer measurements are discussed in this page.
Shoemaker, Michael (Callon Petroleum Company) | Hawkins, James (Callon Petroleum Company) | Becher, John (Callon Petroleum Company) | Gonzales, Veronica (Callon Petroleum Company) | Mukherjee, Sandeep (Callon Petroleum Company) | Garmeh, Reza (Callon Petroleum Company) | Kuntz, David (Callon Petroleum Company)
E&P companies in the Permian Basin typically implement basin-wide development strategies that involve cookie-cutter type methods that use multi-well pads with identical geometric stage and cluster spacing. Such development strategies however fail to recognize and account for subsurface stress heterogeneity, and thus assume similar geomechanical properties that are homogeneous and isotropic which may cause well-to-well interference or “frac hits”, particularly near “parent” wells as fields continue to mature.
Minimum horizontal stress (Sh) is the leading parameter that controls hydraulic fracture stimulation, but is next to impossible to measure quantitatively, especially far field and in 3D space. In-situ stress differences from fluid depletion, combined with stratigraphy and subsequent mineralogy contrasts, control fracture containment vertically and laterally which define fracture propagation and complexity. Far field preference of virgin rock towards brittle vs ductile deformation is governed by mineralogy which defines the elastic moduli or geomechanical behavior of the rock. When integrated with pore pressure and overburden stress, the elastic rock properties are characterized by the Mechanical Earth Model (or MEM) which defines key inputs for calculating Sh using the uniaxial Ben Eaton stress equation. However, implementing this model historically produces incorrect calculated stress, when compared to field measured stress, due to an assumed homogeneous and isotropic subsurface.
Parameterization of fracture geometry models for well spacing, frac hit mitigation, and engineered treatment design in shale (or mudrock) requires an anisotropic in-situ stress measurement that accurately captures subsurface stress states. A method herein is proposed that achieves this using a modified version of the anisotropic Ben Eaton stress equation. The method calculates minimum horizontal stress by substitution of AVO seismic inversion volumes directly into the stress equation, replacing the bound Poisson's ratio term with an equivalent anisotropic corrected Closure Stress Scalar (CSS) defined in terms Lamé elastic parameters, specifically lambda (λ) or incompressibility and mu (μ) for shear rigidity. The CSS volume is corrected for anisotropy using static triaxial core, and is calibrated to multi domain data types including petrophysics, rock physics, completion engineering, and reservoir engineering (DFIT) measurements.
Successful application of said method in the Delaware and Midland sub-basins (of the greater Permian Basin) is shown. Anisotropic minimum horizontal stress (Sh) volumes from 3D seismic defined at 1 ft. vertical log resolution were interpreted quantitatively regionally, particularly as a prevention tool near parent wells prone to frac-hits. Moreover, the method provides an anisotropic measurement of in-situ stress variability (or stress differential) to qualitatively model 3D fracture geometries for engineered treatment optimization. Current stress modeling methods rely on the propagation of geomechanical properties from well control, which do not necessarily represent rock properties and stress states at the area of interest.
Park, Jaeyoung (Texas A&M University) | Iino, Atsushi (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University) | Bi, Jackson (Anadarko Petroleum Corporation) | Sankaran, Sathish (Anadarko Petroleum Corporation)
The objective of this study is to develop a workflow to rapidly simulate injection and production phases of hydraulically fractured shale wells by (a) incorporating fracture propagation in flow simulators using a simplified physical model for pressure-dependent fracture conductivity and fracture pore volume (b) developing a hybrid Fast Marching Method (FMM) and 3D Finite Difference(FD) model for efficient coupled simulation and (c) automating the entire workflow for rapid analysis in a single simulator domain.
Pressure-dependent fracture transmissibility and pore volume multiplier models are assigned to predefined potential hydraulic fracture paths to mimic geomechanical behavior of fractures (i.e. opening and closure). The multipliers are based on empirical equations (e.g., Barton-Bandis model) and theoretical models (e.g., linear elastic fracture mechanics and cubic law). The FMM-based simulation transforms an original 3D reservoir model into an equivalent 1D simulation grid leading to orders of magnitude faster computation and is utilized to efficiently history-match field production and pressure data. A population-based history matching algorithm was used to minimize data misfit and quantify uncertainties in tuning parameters.
We demonstrate the effectiveness and efficiency of the proposed method using synthetic and field examples. First, we validated our proposed simplified fracture propagation model with a comprehensive coupled fluid flow and geomechanical simulator, ABAQUS. The results showed close agreement in both injection pressure response and fracture geometry. Next, the method was applied to a field case to history-match injection pressure and production data. Fracture geometry and properties were inferred from the injection phase and are input to the production phase modeling. After history matching, the misfit and uncertainty ranges in reservoir and fracture properties were substantially reduced.
The proposed workflow enables rapid analyses of hydraulically fractured wells and does not require computationally demanding geomechanical simulations to generate fracture geometry and properties. The FMM-based simulation further improves computational efficiency and allows us to automate the workflow using population-based history matching algorithms to quantify and assess parameter uncertainty.